Status monitoring device, status monitoring method, and status monitoring program

Abstract

Time series data of the status signal is acquired from the robot's operation, and an evaluation value is calculated. The lifespan estimation unit determines the timing at which a trend line created from multiple evaluation values ​​reaches a lifespan threshold as the predicted lifespan timing. When the robot's operation is changed, a past pre-increase estimated evaluation value for the changed operation is determined. A coefficient indicating how many times the lifespan threshold applied to the operation before the change is compared to the initial evaluation value based on the operation before the change is called a lifespan threshold coefficient. The lifespan threshold applied to the changed operation is determined by multiplying the past pre-increase estimated evaluation value by the lifespan threshold coefficient before the change. The timing at which a trend line created from multiple evaluation values ​​based on the operation after the change reaches the lifespan threshold applied to the operation after the change is determined as the predicted lifespan timing.

Description

[Technical field]

[0001] The present disclosure relates primarily to robot life prediction. [Background technology]

[0002] When industrial robots are operated repeatedly in factories, it is inevitable that the various parts of the robot (e.g., mechanical parts) will deteriorate. As this situation progresses, the robot will eventually break down. If a robot breaks down and the line is stopped for an extended period of time, this will result in a huge loss, so there is a strong demand to carry out maintenance (preservation) before the robot breaks down. On the other hand, carrying out frequent maintenance is difficult from the perspective of maintenance costs, etc.

[0003] In order to perform maintenance at an appropriate time, a device has been proposed for predicting the remaining life of a robot's reducer, etc. Patent Document 1 discloses this type of robot maintenance support device.

[0004] The robot maintenance support device of Patent Document 1 is configured to diagnose the future change trend of the current command value based on data on the current command value of the servo motor that constitutes the robot drive system, and determine the period until the current command value reaches a preset value based on the diagnosed change trend. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP2016-117148A Summary of the Invention [Problem to be solved by the invention]

[0006] In order to predict the remaining life using a device such as that described in Patent Document 1, it is necessary to actually operate the robot and collect data for evaluation (for example, the above-mentioned current command value).

[0007] It is possible to predefine a special program to operate the robot in order to collect data. The robot reproduces a certain movement pattern for evaluation by executing the dedicated program periodically or irregularly. However, in this case, it is necessary to make the robot perform a special movement different from the actual work, and a decrease in operating efficiency is unavoidable.

[0008] Data for predicting remaining life can be collected while the robot is operating by executing a program for actual work. However, it is possible that the operating program used for actual work may change while the robot is in operation. If the operating program (in other words, the actions performed by the robot) is changed, the continuity of trend management will be lost.

[0009] The present disclosure has been made in consideration of the above circumstances, and its main purpose is to accurately capture signs of robot failure and remaining lifespan even when the operation of the robot used for evaluation is changed. [Means for solving the problem]

[0010] The problem to be solved by the present disclosure is as described above. Next, the means for solving this problem and the effects thereof will be described.

[0011] According to a first aspect of the present disclosure, a state monitoring device having the following configuration is provided. That is, this state monitoring device monitors the state of an industrial robot capable of reproducing a predetermined operation. The state monitoring device includes a time series data acquisition unit, a storage unit, and a life estimation unit. The time series data acquisition unit acquires time series data of a state signal reflecting the state of the robot. The storage unit stores the time series data acquired by the time series data acquisition unit. The life estimation unit acquires an evaluation value for evaluating the state of the robot based on the time series data, and creates a trend line that represents the tendency of the evaluation value to change over time. The life estimation unit determines the timing at which the trend line reaches a predetermined life threshold as a predicted life timing. When the operation of the robot that is the basis for acquiring the time series data of the state signal is changed, the life estimation unit determines a past pre-increase estimated evaluation value for the changed operation. Here, a coefficient indicating how many times the life threshold applied to the operation before the change is a multiple of the evaluation value at the beginning based on the operation before the change is called a life threshold coefficient for the operation before the change. The life estimation unit obtains a life threshold to be applied to the changed operation by multiplying the past pre-increase estimated evaluation value for the changed operation by the life threshold coefficient for the changed operation. The life estimation unit creates a trend line based on the multiple evaluation values ​​based on the changed operation, and sets the timing at which the trend line reaches the life threshold to be applied to the changed operation as the predicted life timing.

[0012] According to a second aspect of the present disclosure, there is provided a state monitoring method for monitoring the state of an industrial robot capable of reproducing a predetermined operation, as follows. That is, in this state monitoring method, time series data of a state signal reflecting the state of the robot is acquired. The acquired time series data is stored. An evaluation value for evaluating the state of the robot is acquired based on the time series data, and a trend line is created that shows the tendency of the evaluation value to change over time. The timing at which the trend line reaches a predetermined life threshold is obtained as a predicted life timing. In the state monitoring method, when the operation of the robot that is the basis for acquiring the time series data of the state signal is changed, the predicted life timing is obtained as follows. That is, a past pre-increase estimated evaluation value for the changed operation is obtained. Here, a coefficient indicating how many times the life threshold applied to the operation before the change is the evaluation value at the beginning based on the operation before the change is called a life threshold coefficient for the operation before the change. A life threshold applied to the changed operation is obtained by multiplying the past pre-increase estimated evaluation value for the changed operation by the life threshold coefficient for the operation before the change. A trend line is created based on the evaluation values ​​based on the changed operation, and the timing at which the trend line reaches the life threshold value applied to the changed operation is defined as the predicted life timing.

[0013] According to a third aspect of the present disclosure, there is provided a state monitoring program having the following configuration for monitoring the state of an industrial robot capable of reproducing a predetermined operation. That is, this state monitoring program causes a computer to execute a time series data acquisition step, a storage step, and a lifespan estimation step. In the time series data acquisition step, time series data of a state signal reflecting the state of the robot is acquired. In the storage step, the time series data acquired in the time series data acquisition step is stored. In the lifespan estimation step, an evaluation value for evaluating the state of the robot is acquired based on the time series data, a trend line is created that shows the tendency of the evaluation value to change over time, and the timing at which the trend line reaches a predetermined lifespan threshold is obtained as a predicted lifespan timing. When the operation of the robot that is the basis for acquiring the time series data of the state signal is changed, in the lifespan estimation step, the predicted lifespan timing is obtained as follows. That is, a past pre-increase estimated evaluation value for the changed operation is obtained. Here, a coefficient indicating how many times the lifespan threshold applied to the operation before the change is a multiple of the evaluation value at the beginning based on the operation before the change is called a lifespan threshold coefficient for the operation before the change. A life threshold applied to the changed operation is obtained by multiplying the past pre-increase estimated evaluation value for the changed operation by the life threshold coefficient for the changed operation. A trend line is created based on the multiple evaluation values ​​based on the changed operation, and the timing at which the trend line reaches the life threshold applied to the changed operation is set as the predicted life timing.

[0014] As a result, even if the robot's behavior, which is the basis for the evaluation value, is changed, the evaluation values ​​before and after the change can be handled consistently, and lifespan prediction can be performed without inconsistency. Therefore, it is possible to obtain state time-series data and evaluation values ​​based on the robot's actual behavior, thereby improving the robot's operating efficiency. By using the past pre-increase estimated evaluation value, the lifespan threshold value based on the changed behavior can be obtained by simple calculation. Effect of the Invention

[0015] According to the present disclosure, even if the operation of the robot used in the evaluation is changed, it is possible to accurately detect the signs of robot failure and the remaining lifespan of the robot. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a perspective view showing a configuration of a robot according to an embodiment of the present disclosure. [Diagram 2] FIG. 2 is a block diagram illustrating an electrical configuration of the robot and the state monitoring device. [Diagram 3] 11 is a graph showing an example of time-series data of current values. [Figure 4] 11 is a graph illustrating the transition and trend line of evaluation values ​​obtained from time-series data of current values. [Diagram 5] 11 is a flowchart for explaining a process of acquiring a reference pattern from position time-series data. [Figure 6] 6 is a graph illustrating a process of extracting a time interval from position time series data to obtain a reference pattern. [Figure 7] 6 is a flowchart mainly explaining a pattern matching process for searching for a reference pattern from position time-series data. [Figure 8] FIG. 4 is a schematic diagram illustrating a pattern matching process. [Figure 9] 5A and 5B are schematic diagrams illustrating the average error and maximum error of position time series data in a time section in which a reference pattern is matched. [Figure 10] 11 is a graph showing an example of time-series data of a current value that changes as the motion pattern of the robot is changed. [Figure 11] 6 is a graph illustrating a first method for consistently processing evaluation values ​​before and after a change in a robot's motion pattern. [Figure 12] 6 is a graph illustrating a process when the motion pattern of the robot is changed twice in the first method. [Figure 13] 13 is a graph illustrating a second method. [Figure 14] 13 is a graph illustrating a third method. [Figure 15]13 is a graph illustrating a fourth method. [Figure 16] Graph illustrating the fifth method. [Figure 17] Graph illustrating the sixth method. [Figure 18] 13 is a graph illustrating the seventh method. [Figure 19] 13 is a graph illustrating the eighth method. [Figure 20] Graph illustrating the ninth method. [Figure 21] Graph illustrating the tenth method. [Figure 22] 13 is a graph illustrating an eleventh method. [Diagram 23] Graph illustrating the twelfth method. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Next, an embodiment of the present disclosure will be described with reference to the drawings. Fig. 1 is a perspective view showing a configuration of a robot 1 according to an embodiment of the present disclosure. Fig. 2 is a block diagram showing an electrical configuration of the robot 1 and a state monitoring device 5. Fig. 3 is a graph showing an example of time series data of current values. Fig. 4 is a graph explaining the transition and trend line of an evaluation value obtained from the time series data of current values.

[0018] A state monitoring device 5 according to the present disclosure is applied to, for example, a robot (industrial robot) 1 as shown in Fig. 1. The robot 1 performs work such as painting, cleaning, welding, and transporting on a workpiece as a work target. The robot 1 is realized by, for example, a vertical articulated robot.

[0019] The configuration of the robot 1 will be briefly described below with reference to FIGS. 1 and 2.

[0020] The robot 1 includes a swivel base 10, an articulated arm 11, and a wrist 12. The swivel base 10 is fixed to the ground (e.g., a factory floor). The articulated arm 11 has a plurality of joints. The wrist 12 is attached to the tip of the articulated arm 11. An end effector 13 is attached to the wrist 12 to perform work on a workpiece.

[0021] As shown in FIG. 2, the robot 1 includes an arm driving device 21.

[0022] These driving devices are composed of actuators configured as servo motors, reducers, etc. However, the configuration of the driving devices is not limited to the above. Each actuator is electrically connected to a controller 90. The actuators operate to reflect command values ​​input from the controller 90.

[0023] Driving forces from the servo motors constituting the arm driving device 21 are transmitted via reducers to each joint of the articulated arm 11, the rotating base 10, and the wrist 12. Each servo motor is fitted with an encoder (not shown) that detects its rotational position.

[0024] The robot 1 performs a task by playing back the motions recorded by teaching. The controller 90 controls the actuators so that the robot 1 reproduces a series of motions previously taught by an instructor. Hereinafter, this series of motions may be referred to as a motion pattern. The robot 1 can be taught by the instructor operating a teaching pendant (not shown).

[0025] A program for moving the robot 1 is generated by teaching the robot 1. A program is generated each time teaching is performed on the robot 1. If the movement pattern to be taught to the robot 1 is different, the program will also be different. By switching between multiple programs to be executed, the movement pattern performed by the robot 1 can be changed.

[0026] The controller 90 is configured as a known computer including, for example, a CPU, a ROM, a RAM, an auxiliary storage device, etc. The auxiliary storage device is configured as, for example, a HDD, an SSD, etc. The auxiliary storage device stores a program for operating the robot 1, etc.

[0027] 1, the state monitor 5 is connected to a controller 90. The state monitor 5 acquires, via the controller 90, the transition of the current value of the current flowing through the actuator (servo motor), etc.

[0028] If an abnormality occurs in the servo motor or the reducer connected thereto, the servo motor's current value is thought to fluctuate as a result. This current value therefore corresponds to a status signal that reflects the state of the robot 1. The transition of the current value can be expressed by repeatedly acquiring the current value at short time intervals and arranging many current values ​​in a time series. Hereinafter, data in which the values ​​of the status signal are arranged in a time series may be referred to as status time series data.

[0029] The state monitoring device 5 can determine the presence or absence of an abnormality in the robot 1 by monitoring the acquired state time-series data. In this embodiment, the state monitoring device 5 determines the presence or absence of an abnormality mainly for the servo motors and reducers of each joint. Here, "abnormality" includes cases where some condition that does not lead to malfunction / inability to operate but is a precursor to such an abnormality occurs in the servo motors, reducers, or bearings.

[0030] As shown in FIG. 2, the condition monitoring device 5 includes a time-series data acquisition unit 51, a storage unit 52, a time-series data evaluation unit 53, a life estimation unit 54, and a display unit 55.

[0031] The condition monitoring device 5 is configured as a known computer including a CPU, a ROM, a RAM, an auxiliary storage device, etc. The auxiliary storage device is configured as, for example, an HDD, an SSD, etc. Various programs, such as a state monitoring program for the robot 1, are stored in the auxiliary storage device. This state monitoring program causes the computer to execute a time series data acquisition step, a storage step, a lifespan estimation step, etc., thereby realizing the state monitoring method of the present disclosure. Through cooperation of these hardware and software, the computer can be operated as a time series data acquisition unit 51, a storage unit 52, a time series data evaluation unit 53, a lifespan estimation unit 54, etc.

[0032] The time series data acquiring unit 51 acquires the above-mentioned state time series data. The time series data acquiring unit 51 acquires state time series data for all servo motors provided in the arm driving device 21 of the robot 1. The state time series data is acquired for each of the multiple servo motors (in other words, multiple reducers) arranged in each part of the robot 1.

[0033] In this embodiment, the status signal is a current value. Here, the current value means a measured value obtained by measuring the magnitude of the current flowing through the servo motor by a sensor. The sensor is provided in a servo driver (not shown) that controls the servo motor. However, a monitoring sensor may be provided separately from the servo driver. Alternatively, a current command value given to the servo motor by the servo driver may be adopted as the status signal. The servo driver feedback controls the servo motor so as to bring the current current value closer to the current command value. Therefore, from the viewpoint of detecting an abnormality in the servo motor or the reducer, there is almost no difference between the current value and the current command value.

[0034] The magnitude of the torque of the servo motor is proportional to the magnitude of the current, so the torque value or the torque command value may be used as the status signal.

[0035] The deviation (rotational position deviation) between the target value for the rotational position of the servo motor and the actual rotational position obtained by the encoder may be used as the status signal. Usually, the servo driver multiplies this deviation by a gain and gives it to the servo motor as a current command value. Therefore, the transition of the deviation of the rotational position shows a similar trend to the transition of the current command value.

[0036] The time-series data acquiring unit 51 acquires state time-series data for each servo motor every time the robot 1 reproduces a taught motion pattern. However, instead of acquiring state time-series data for all reproduced motions, it may be acquired for only one or several reproduced motions per day, for example.

[0037] In this embodiment, the motion pattern for which the time-series data acquiring unit 51 acquires state time-series data is a motion pattern taught to the robot 1 to perform an actual task. In other words, the robot 1 does not operate according to a special motion pattern defined for state monitoring.

[0038] The time-series data acquisition unit 51 acquires time-series data of the current values ​​flowing through each servo motor between the timing of receiving the acquisition start signal and the timing of receiving the acquisition end signal. The acquisition start signal and the acquisition end signal are output by the controller 90, for example.

[0039] The graph in Fig. 3 shows an example of the current value flowing through the servo motor of a certain joint when the robot 1 reproduces a taught movement pattern. In the graph in Fig. 3, the vertical axis represents the current value and the horizontal axis represents time. As shown in Fig. 3, before the program for the reproduced movement is executed, the current value of the servo motor is zero. At this time, electromagnetic brakes (not shown) are operating in each joint, so the posture of the multi-joint arm 11, etc. is maintained.

[0040] Next, the playback operation program for the robot 1 is started. The brake is released and, at almost the same time, current begins to flow to the servo motor. At this point, the output shaft of the servo motor is controlled to stop. After a certain amount of time has passed, which is necessary for the angle of the output shaft of the servo motor to stabilize, the servo motor starts to rotate. This essentially starts the operation of the robot 1.

[0041] After the brake is released, the controller 90 outputs an acquisition start signal to the state monitor 5 (and thus to the time-series data acquisition unit 51) shortly before the servo motor starts to rotate.

[0042] When the series of operations included in the operation pattern is all completed, the servo motor is controlled to stop rotating. After the servo motor stops rotating, the controller 90 outputs an acquisition end signal to the time-series data acquisition unit 51 before the program ends.

[0043] The storage unit 52 is configured, for example, by the auxiliary storage device described above. The storage unit 52 stores the time-series data acquired by the time-series data acquisition unit 51 and the like.

[0044] In this embodiment, the time series data is a number of current values ​​obtained by repeatedly detecting at short, fixed time intervals and arranged in chronological order. Therefore, the current values ​​in the time series data are sampled values. The time interval (sampling interval) for detecting the current values ​​is, for example, several milliseconds. The time series data corresponds to the transition of the current value from the timing of the acquisition start signal to the timing of the acquisition end signal in the graph of FIG. 3.

[0045] Time information indicating the time when the data was acquired is stored in association with the time series data in the storage unit 52. The time information may be, for example, a time stamp indicating the date and time when the acquisition start signal was received.

[0046] Similarly, in the storage unit 52, playback identification information indicating the movement pattern performed by the robot 1 when the time-series data of the current value was acquired is stored in association with the time-series data. The movement pattern of the robot 1 is defined by a playback movement program. Therefore, the playback identification information can be, for example, a program number or a program name given to uniquely identify the playback movement program.

[0047] The storage of time series data will be specifically described. Using the file system of the OS included in the state monitoring device 5, which is a computer, a folder named after the program number or program name is created in the above-mentioned auxiliary storage device. In this folder, a file of state time series data obtained when the program with that number is executed is automatically saved. In this file, time series data regarding the current values ​​of the servo motors of the six joints is written, for example, in a comma separated value format (CSV). The file name includes a timestamp character string. In this way, the association of the timing information and the reproduction identification information for the state time series data is realized. However, the above is an example, and the association may be realized in other ways.

[0048] In this embodiment, the time series data acquisition unit 51 acquires time series data on the rotational positions of the servo motors of the six joints when acquiring time series data on the current values ​​of the servo motors of the six joints. This rotational position may be the target value described above, or may be an actual rotational position obtained by an encoder. The time series data on the rotational position is used for generating a reference pattern, which will be described later. Hereinafter, the time series data on the rotational positions of the servo motors acquired in correspondence with the state time series data may be referred to as position time series data. The position time series data can be written in, for example, a CSV format, similar to the state time series data. The state time series data and the position time series data may be saved in one file or in separate files.

[0049] The time series data evaluation unit 53 evaluates the state time series data acquired by the time series data acquisition unit 51. The state time series data to be evaluated is, for example, time series data of current values ​​from an acquisition start signal to an acquisition end signal, as shown in FIG. 3. The time series data evaluation unit 53 outputs an evaluation value for evaluating the state of the robot 1 based on the evaluation result. In this embodiment, I2 and a frequency analysis integrated value are used as the evaluation value. Details of the evaluation will be described later.

[0050] I2 is the root mean square value of the current. This I2 corresponds to "I2" in Patent Document 1, so a detailed description will be omitted.

[0051] The frequency analysis integrated value is a value obtained by performing frequency analysis on time series data of the current value, and integrating components from 0 Hz to several tens of Hz for the obtained amplitude spectrum. Since 0 Hz means a direct current component, the frequency analysis may be an integrated value of components from several Hz to several tens of Hz excluding this. The frequency analysis can be realized by a known method, typically by fast Fourier transform. In some cases, it is preferable to use the frequency analysis integrated value to determine the presence or absence of a fault sign, such as a tendency for the robot 1 to vibrate. There are various possible causes for the tendency for vibration, and an example of this is an increase in lost motion due to wear of the reducer. Instead of the amplitude spectrum, the power spectrum or the power spectrum density may be the target of integration. The amplitude spectrum, the power spectrum, and the power spectrum density are all types of frequency spectrum.

[0052] Assuming that the natural frequency of the system of the robot 1 is 8 Hz, the 8 Hz component becomes large due to resonance in the amplitude spectrum. In this regard, in the frequency analysis integrated value of this embodiment, a value obtained by integrating components from 0 Hz to several tens of Hz, for example, is used for evaluation. As time passes, in addition to the original main mode vibration, vibrations due to the deterioration of various parts are often added. By observing a wide range of frequencies, the detection range of failure signs can be expanded.

[0053] The evaluation value is not limited to I2 and the frequency analysis integrated value. For example, a peak, PTP (Peak to Peak), etc. can be used as the evaluation value. A peak is the peak value of a current waveform. A PTP is a value obtained by subtracting a low peak current value from a high peak current value of a current waveform. Both a peak and a PTP are types of "peak current" as described in Patent Document 1. As an evaluation value, a dissimilarity obtained by comparing the current waveform obtained this time with the current waveform at the start of the status monitoring can also be used. The similarity can be calculated by any known method, and for example, the sum or average value of Euclidean distances can be used. As the similarity, a DTW distance or the like can also be used.

[0054] The life estimation unit 54 can predict the timing at which each joint of the robot 1 will malfunction / become unable to operate in the future, based on the transition of I2 and the frequency analysis integrated value over time up to the present. Hereinafter, this timing may be referred to as life timing.

[0055] The graph in Figure 4 shows an example of approximately 70 days after the start of monitoring the condition of the robot 1. This graph focuses on one of the six joints of the robot 1 and shows the progress of I2 as an evaluation value. The I2 value is obtained at appropriate times from the start of monitoring to the present and plotted on the graph.

[0056] The life estimation unit 54 creates a trend line TL1 that indicates the tendency of I2 to increase over time. This trend line TL1 can be obtained, for example, by a known least squares method. A life threshold value TH1 is set in advance for I2. The point in time when the obtained trend line TL1 reaches the life threshold value TH1 indicates the timing when the joint is predicted to malfunction / become inoperable. Hereinafter, the predicted life timing may be referred to as predicted life timing PL1. Similarly, for the frequency analysis integrated value, a trend line is created to obtain the timing when the frequency analysis integrated value reaches the life threshold, thereby predicting the life timing. After obtaining the predicted life timing for each of I2 and the frequency analysis integrated value, the life estimation unit 54 determines whichever timing arrives first as the overall predicted life timing.

[0057] The display unit 55 can display the transition of I2 and the frequency analysis integrated value over time, for example, in the form of a graph. The display unit 55 is composed of a display device such as a liquid crystal display. The display unit 55 can display the trend line TL1, the life threshold value TH1, and the predicted life timing PL1 together with the graph.

[0058] The operator monitors whether there are any unusual points that deviate from the usual trends for I2 and frequency analysis integrated values. The operator can use this information to appropriately plan future maintenance.

[0059] Next, the detailed configuration of the time-series data evaluation unit 53 will be described.

[0060] The time series data evaluation unit 53 determines an evaluation value representing the state of the robot 1 by focusing on only a partial time interval, rather than on all of the time series data of the current value. Hereinafter, the time interval to be evaluated may be referred to as an evaluation interval. As a premise for determining the evaluation interval, the time series data evaluation unit 53 determines and stores one or more reference patterns (basic data) corresponding to a portion extracted from the position time series data. The evaluation interval of the state time series data is determined by a pattern matching process that searches for the reference data from the position time series data.

[0061] As shown in FIG. 2, the time-series data evaluation unit 53 includes a reference pattern creation unit 61, a search processing unit 62, an evaluation value calculation unit 63, and an operation change monitoring unit 64.

[0062] The reference pattern creation unit 61 creates a predetermined reference pattern. The reference pattern can be obtained by extracting an appropriate time period from the position time-series data acquired when the status monitoring device 5 first starts monitoring.

[0063] In a teaching program with certain specifications, for example, a home position is determined so that the last teaching point of the previously played program and the first teaching point of the next played program always coincide with each other. In a teaching program with other specifications, it is permitted that the last teaching point of the previously played program and the first teaching point of the next played program are different, and in that case, when the next program is played, an operation of moving between these two points is performed. Since it may not be appropriate to include this positioning operation in the reference pattern, the reference pattern may be created only from the portion of the position time series data after a predetermined time has elapsed since the start of program execution. The process of extracting the reference pattern will be described in detail later.

[0064] In order to perform pattern matching appropriately, a lower limit length and an upper limit length are predefined for the reference pattern. For example, the lower limit length can be 10 seconds and the upper limit length can be 60 seconds, but are not limited thereto.

[0065] When the reference pattern creating unit 61 creates a reference pattern, it assigns identification information that uniquely identifies the reference pattern, and stores the reference pattern in the storage unit 52 .

[0066] The search processing unit 62 performs a pattern matching process to search for a reference pattern from the position time series data. A time interval in which the reference pattern matches the position time series data is identified as an evaluation interval.

[0067] The evaluation value calculation unit 63 calculates an evaluation value for the evaluation section specified for the position time series data. The obtained evaluation value is output from the time series data evaluation unit 53 to the life estimation unit 54 together with the identification information of the reference pattern.

[0068] The evaluation value of an evaluation target such as a reference pattern can be identified, for example, by a program number, a program name, a reference pattern number, a reference pattern name, etc. Identification is performed based on one or a combination of a plurality of these.

[0069] The movement change monitoring unit 64 monitors whether or not a program including a registered movement pattern of the robot 1 has been changed and is no longer in use, based on the result of the search processing unit 62 searching for a reference pattern from the position time-series data.

[0070] Next, extraction of a reference pattern performed by the reference pattern creating unit 61 will be described with reference to the flowchart of Fig. 5. Fig. 5 is a flowchart illustrating a process of acquiring a reference pattern from position time-series data.

[0071] The reference pattern creation unit 61 first judges whether the robot 1 is in a moving state or a non-moving state at each time point of the given position time series data (step S101). An example of the position time series data is shown in Fig. 6. In each of the six graphs in Fig. 6, the vertical axis represents the rotational position and the horizontal axis represents the time.

[0072] The operating state of a robot refers to a state in which one or more of the six joint axes (in other words, servo motors) are moving, and the non-operating state refers to a state in which all six axes are not moving. The determination of whether the robot is in an operating state or a non-operating state can be made based on the change in the rotational position of each axis. The bottom of the graph in Figure 6 shows the results of the operating / non-operating state determination at each point in time.

[0073] Usually, the robot 1 is in a non-operating state from when the program starts to execute until the aforementioned electromagnetic brake is released and the servo motor starts to rotate. The robot 1 is also in a non-operating state when interlock control is performed between the robot 1 and other devices. Such non-operating state portions of the time-series data are not suitable for evaluating the state of the robot 1.

[0074] The operating state of the robot 1 may be defined as a state in which all of the six joint axes (in other words, the servo motors) are moving. In this case, the non-operating state means a state in which at least one of the six axes is not moving.

[0075] Although it depends on the complexity of the motion to be taught, in the position time-series data corresponding to one motion pattern, the motion state and the non-motion state usually appear alternately. In step S101, by identifying the parts corresponding to the motion state and the non-motion state of the robot 1 from the time-series data of the status signal, it is possible to determine the reference pattern so as not to include the non-motion state parts.

[0076] When the robot 1 performs spot welding, for example, all six axes of the robot 1 are stopped for, for example, one second during the welding operation, and during this time, electricity is passed through the electrodes of the gun disposed at the tip of the robot 1. In order to prevent such a situation from being determined as a non-operating state, in step S101, it is preferable to determine that the robot 1 is not in an operating state only if a situation in which all six axes of the robot 1 are not moving continues for a predetermined period of time.

[0077] Until a predetermined time has elapsed since the program started, the reference pattern creation unit 61 may consider the robot 1 to be in a non-moving state, regardless of whether the axes of the six joints are moving or not. This makes it possible to prevent the above-mentioned alignment operation from being included in the reference pattern.

[0078] Next, the reference pattern creation unit 61 identifies the section with the longest continuous action section in the position time series data as a candidate section for extracting a reference pattern (step S102). A continuous action section is a section in which the above-mentioned motion state continues over time. The identified candidate section is shown in FIG. 6.

[0079] Next, the reference pattern creation unit 61 judges whether the time length of the candidate section is equal to or greater than the lower limit length (step S103). If the time length of the candidate section is less than the lower limit length, the process is terminated because a reference pattern of an appropriate length cannot be extracted from the candidate section.

[0080] If it is determined in step S103 that the time length of the candidate section is equal to or greater than the lower limit length, the reference pattern creation unit 61 divides the time length of the candidate section by the upper limit length to obtain a ratio (step S104). Next, the reference pattern creation unit 61 rounds up the ratio value to obtain an integer value, and determines the obtained integer value as the number of reference patterns to be extracted from the candidate section (step S105). Hereinafter, this number may be referred to as the extraction number. In the example of FIG. 6, the ratio value is approximately 1.8, so the extraction number is 2.

[0081] Next, the reference pattern creating unit 61 extracts a reference pattern from the position time-series data according to the extraction number (step S106).

[0082] When the number of extractions is 1, the reference pattern creation unit 61 extracts all of the candidate sections identified in step S102 from the position time-series data to obtain one reference pattern. The length of the reference pattern is equal to or greater than the lower limit and equal to or less than the upper limit.

[0083] When the number of extractions is two or more, the reference pattern creation unit 61 obtains a reference pattern by extracting a continuous part of the time interval from the candidate interval. By making the start and end of the time intervals extracted from the candidate interval different from each other, it is possible to obtain multiple reference patterns from one candidate interval. In FIG. 6, two reference patterns obtained from the candidate interval are shown enclosed in a rectangle. For each of the reference patterns, the length of the extracted time interval coincides with the above-mentioned upper limit length. The multiple reference patterns are respectively extracted from the candidate interval so that any part of the candidate interval is included in one of the reference patterns. When the number of extractions is three or more, it is preferable that the multiple extracted time intervals are evenly arranged in the candidate interval. When the length of the candidate interval is smaller than the length obtained by multiplying the upper limit length by the number of extractions, the multiple reference patterns are extracted so as to partially overlap each other. In the example of FIG. 6, the two acquired reference patterns partially overlap each other.

[0084] In the example of Fig. 6, for the sake of simplicity, the upper limit length of the reference pattern is set to a certain degree. By setting the lower limit length and the upper limit length of the reference pattern to be sufficiently small, many reference patterns can be obtained from one piece of position time series data.

[0085] The reference pattern can be determined by the above series of processes. After acquiring a certain number of reference patterns as candidates, a selection process for eliminating inappropriate patterns may be performed to adopt the reference pattern. For example, it is possible to eliminate a pattern in which the change in one or more axes among the multiple axes is always zero or a predetermined amount or less over the entire time period of the pattern. A reference pattern in which the load applied to each joint of the robot 1 is light, or the change in load is a predetermined amount or less may be eliminated. The load can be evaluated using, for example, the above-mentioned I2 and the frequency analysis integrated value.

[0086] In step S105, the ratio may be rounded down to an integer to determine the number of reference patterns to be extracted from the candidate section. In this case, multiple reference patterns may be extracted from one candidate section so that the overlapping does not occur. Rounding may be performed to switch between rounding up and rounding down depending on the value after the decimal point, such as rounding up or rounding down.

[0087] Steps S104 and S105 may be omitted, and the number of extractions may always be 1. In this case, in step S106, one reference pattern is extracted from the candidate section, with the upper limit length of the reference pattern as the upper limit. If the length of the candidate section is longer than the upper limit length of the reference pattern, the position from which the reference pattern is extracted is arbitrary. For example, one reference pattern may be extracted from the beginning or end of the candidate section.

[0088] In this embodiment, the storage unit 52 can store up to five different reference patterns. Each of the reference patterns created by the reference pattern creation unit 61 is stored in the storage unit 52.

[0089] When a reference pattern is extracted from the initially acquired position time-series data, the time-series data evaluation unit 53 sets the same time interval as that in which the reference pattern was extracted as an evaluation interval in the time-series data of the current value. The time-series data evaluation unit 53 extracts only the evaluation interval from the time-series data of the current value, and obtains the aforementioned evaluation value (i.e., I2 and frequency analysis integrated value). This evaluation value is treated as the initial evaluation value. When there are multiple reference patterns, an evaluation value is obtained corresponding to each of the reference patterns.

[0090] Next, the pattern matching process performed by the search processing unit 62 will be described with reference to Fig. 7. Fig. 7 is a flowchart mainly explaining the pattern matching process for searching for a reference pattern from position time-series data.

[0091] Even after the reference pattern created by the reference pattern creation unit 61 is stored in the memory unit 52, the robot 1 repeatedly performs operations according to the program. At appropriate timing, the time-series data acquisition unit 51 acquires the position time-series data and the time-series data of the value of the status signal, and stores them in the memory unit 52.

[0092] A search processing unit 62 included in the time series data evaluation unit 53 performs a pattern matching process to search for a reference pattern from the obtained position time series data.

[0093] When the flow shown in Fig. 7 starts, the search processing unit 62 selects one reference pattern as a search target, and appropriately determines the number of slides for search, taking into consideration the length of the position time series data and the length of the reference pattern (step S201). As simply shown in Fig. 8, pattern matching is performed by calculating the dissimilarity between the reference pattern to be searched and the position time series data while sliding the position of the reference pattern in the time axis direction relative to the position time series data. If the number of slides increases, the amount of slide in the time axis direction per time (slide amount S1 in Fig. 8) becomes smaller, so that the matching accuracy can be improved, but the calculation load increases. It is preferable that the number of slides is determined taking these circumstances into consideration.

[0094] Next, the search processing unit 62 calculates the dissimilarity between the reference pattern and the position time series data while sliding the reference pattern in the time axis direction with respect to the position time series data for each axis of the robot 1 (step S202). FIG. 8 illustrates an example of pattern matching for the position time series data of one axis. The slide amount S1 of the reference pattern per time is determined by calculation so that the entire position time series data can be covered by sliding the reference pattern the number of times determined in step S201. In this embodiment, the sum of the Euclidean distances (in other words, the absolute values ​​of the differences in rotational positions) at each sampling timing is used as the dissimilarity. The search processing unit 62 determines the position where the above-mentioned dissimilarity is minimum (step S203).

[0095] In steps S202 to S203, the search processing unit 62 slides the reference pattern independently for each axis of the robot 1, and finds the position on the time axis where the dissimilarity is closest to zero. In reality, the position on the time axis where the reference pattern best matches the position time series data should be the same for all axes of the robot 1 (in other words, the servo motors of the six joints). However, due to aging, noise, and other reasons, some axes may experience a lead / lag in the time axis direction of the position time series data. By performing a matching process independently for each axis and adopting the median value of the best match position described later, the reference pattern can be flexibly matched to such position time series data.

[0096] By the process of step S203, the position on the time axis at which the reference pattern best matches is obtained for each of the multiple axes of the robot 1. Hereinafter, this position may be referred to as the best match position. The search processing unit 62 acquires the median value of the best match positions obtained for the multiple axes as the best match position of the reference pattern taking the multiple axes into consideration comprehensively (step S204). Hereinafter, this best match position may be referred to as the representative match position. The method of determining the representative match position is arbitrary, and for example, the average value or the like may be used instead of the median value of the best match positions.

[0097] Next, the search processing unit 62 calculates the average error E between the position time series data and the reference pattern of the representative match position for each axis of the robot 1 (step S205). This average error E is calculated by dividing the sum of the absolute values ​​of the differences in rotational position at each sampling timing for the time interval corresponding to the reference pattern of the representative match position by the number of samplings corresponding to the length of that time interval. The formula for the average error E is shown in the schematic diagram of FIG. 9. Hereinafter, in the position time series data, the time interval corresponding to the reference pattern of the representative match position may be referred to as the reference pattern match interval.

[0098] Next, the search processing unit 62 calculates the maximum error value M between the position time series data and the reference pattern of the representative match position for each axis of the robot 1, for the reference pattern matching section (step S206). This maximum error value is the maximum absolute value of the difference in rotation position at each sampling timing for the reference pattern matching section. The formula for the maximum error value M is shown in FIG. 9.

[0099] Next, the search processing unit 62 calculates a difference A for each axis of the robot 1 by subtracting the minimum value from the maximum value of the rotational position indicated by the position time series data for the reference pattern matching section, as shown in Figure 9 (step S207).

[0100] The search processing unit 62 calculates the values ​​of E / A and M / A for each axis (step S208). These values ​​represent the relative magnitude of the average and maximum error between the position time series data in the reference pattern matching section and the reference pattern with respect to the change in rotational position. For example, due to spike noise or the like, the difference in rotational position may become extremely large compared to the reference pattern in an extremely short time region of the position time series data in the reference pattern matching section. When the reference pattern is long, such noise is unlikely to appear in the E / A value, but can be easily detected by calculating the M / A value.

[0101] The search processing unit 62 compares E / A with a predetermined first threshold value and compares M / A with a predetermined second threshold value for each axis of the robot 1 (steps S209 to S210). The first threshold value and the second threshold value can be set arbitrarily, but it is possible to set the first threshold value to 3% and the second threshold value to 5%, for example.

[0102] When the E / A value is equal to or less than the first threshold and the M / A value is equal to or less than the second threshold for all axes in operation, the search processing unit 62 specifies the reference pattern matching section of the position time series data as an evaluation section (step S211).Then, the evaluation value calculation unit 63 calculates the evaluation value in the evaluation section (step S212).

[0103] Furthermore, the search processing unit 62 stores in the storage unit 52 that the reference pattern has appeared in the position time-series data (step S213). This appearance record is used by the operation change monitoring unit 64 to determine whether or not a change has occurred in the operation pattern of the robot 1.

[0104] If the E / A value exceeds the first threshold or the M / A value exceeds the second threshold for one or more operating axes, an evaluation value should not be calculated from such position time series data, so steps S211 to S213 are skipped.

[0105] The search processing unit 62 and the evaluation value calculation unit 63 repeat the matching process and the evaluation value calculation process of steps S201 to S213 (step S214) for all the reference patterns stored in the storage unit 52. When the process is completed for all the reference patterns, the series of processes shown in FIG. 7 is completed.

[0106] The search processing unit 62 and the evaluation value calculation unit 63 perform the process shown in the flowchart of Fig. 7 on the position time-series data obtained by the operation of the robot 1. As a result, it is possible to obtain, by calculation, the number of times each reference pattern has appeared in the most recent 10 days, for example.

[0107] Next, the change in the movement pattern of the robot 1 and the monitoring by the movement change monitoring unit 64 will be described.

[0108] Consider a case where the motion pattern of the robot 1 described with reference to FIG. 3 is no longer used for some reason, or the frequency of use drops significantly. For example, in the case of robots used in the automobile industry, such a situation occurs when the model of the car being manufactured is changed, or when the main model of the car to be produced in the monthly plan is changed in a mixed production line. In this case, the robot 1 reproduces a motion pattern different from the previous one. The transition of the current value corresponding to the new motion pattern is shown in FIG. 10.

[0109] A change in the motion pattern of the robot 1 is synonymous with a change in the program of the playback motion. When the robot 1 performs a new motion corresponding to Fig. 10, the playback identification information is stored in the storage unit 52 in association with the time-series data, as in the case of Fig. 3. As the program of the playback motion is changed, the playback identification information is also changed. Therefore, the CSV files for the motion in Fig. 10 and the motion in Fig. 3 are saved in folders with different names.

[0110] When the motion pattern of the robot 1 is changed, the reference pattern created before the change no longer matches the position time series data after the change. The motion change monitoring unit 64 judges whether or not the situation in which the reference pattern stored in the storage unit 52 does not appear in the position time series data with sufficient frequency continues. There are various methods for this judgment, but it is possible to compare the consecutive count value of the number of days in which the reference pattern does not appear in the position time series data with a threshold value, or to compare the appearance count value for N days with a threshold value.

[0111] When it is determined that the situation continues in which the stored reference pattern does not appear with sufficient frequency in the position time series data, the operation change monitoring unit 64 deletes the reference pattern from the storage unit 52. Upon detecting this, the reference pattern creation unit 61 automatically performs the process shown in Fig. 5 to create a reference pattern from the position time series data at that time point and store it in the storage unit 52. This ensures the continuity of collection of evaluation values.

[0112] The operation pattern may be changed manually by an operator when a production plan is changed, or may be automatically changed depending on the product. When the operation pattern is changed by an operator, the previously registered reference pattern may be deleted manually or automatically.

[0113] Next, the handling of the evaluation value before and after the change in the robot's motion pattern will be described.

[0114] When the motion pattern of the robot 1 is changed, the load state of the servo motors and the reducers of the six joints changes. Therefore, simply combining the evaluation values ​​obtained before and after the change in the motion pattern cannot be used for trend management for failure prediction and calculation of the remaining life.

[0115] When the motion pattern of the robot 1 is changed, the reference pattern is also automatically changed, except in special cases. FIG. 11 shows the transition of I2 based on one of the reference patterns before the motion change and the transition of I2 based on one of the reference patterns after the motion change. When the reference pattern on which the evaluation value is based is changed, the evaluation value usually changes discontinuously. The life estimation unit 54 calculates the predicted life timing by taking into account the evaluation values ​​both before and after the change of the reference pattern, by using one of the following 12 methods.

[0116] The first method will be described with reference to FIG.

[0117] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold TH1 of the first reference pattern is the initial evaluation value p1 of the first reference pattern multiplied by the life threshold coefficient k1 (TH1=p1×k1). The life threshold coefficient k1 is appropriately determined and can be, for example, 1.2. [b] The final evaluation value p2 based on the pre-change reference pattern is divided by the initial evaluation value p1 based on the pre-change reference pattern to obtain an increase ratio ir1 (ir1=p2 / p1). [c] The initial evaluation value q1 based on the changed reference pattern is divided by the increase ratio ir1 to obtain a past pre-increase estimated evaluation value s1 (s1=q1 / ir1). [d] The lifetime threshold value TH2 based on the changed reference pattern is calculated by multiplying the past pre-increase estimated evaluation value s1 by the lifetime threshold coefficient k1 (TH2 = s1 · k1). [e] A trend line TL2 is created based on a plurality of evaluation values ​​based on the changed reference pattern. The timing at which this trend line TL2 reaches a life threshold value TH2 applied to the changed reference pattern is set as a predicted life timing PL2.

[0118] The life threshold coefficient k1 described in [a] above indicates how many times the life threshold TH1 applied to the behavior of the first reference pattern is larger than the initial evaluation value p1 based on the behavior of the first reference pattern. The life threshold TH1 is given, and the life threshold coefficient k1 can also be calculated by dividing this life threshold TH1 by the initial evaluation value p1. This also applies to the second method and subsequent methods described later.

[0119] In the first method, when the reference pattern is changed multiple times, the processes [b] to [e] may be performed for each change. FIG. 12 shows a case where the robot 1 has changed its motion twice. However, after the second change, the increase ratio ir2 of [b] is calculated by dividing the evaluation value q2 of the last change based on the reference pattern before the change by the past pre-rise estimated evaluation value s1 calculated for the reference pattern before the change (ir2=q2 / s1). In FIG. 12, if the initial evaluation value based on the reference pattern after the second change is r1, the past pre-rise estimated evaluation value s2 based on the reference pattern after the second change is calculated by the formula s2=r1 / ir2. The life threshold TH3 applied to the reference pattern after the second change is calculated by multiplying the past pre-rise estimated evaluation value s2 by the life threshold coefficient k1 (TH3=s2·k1). The life threshold coefficient k1 can be used in common regardless of how many times the motion has been changed.

[0120] A trend line TL3 created based on multiple evaluation values ​​based on the reference pattern after the second change is shown in Figure 12. The timing at which this trend line TL3 reaches the life threshold value TH3 is the predicted life timing. Due to space limitations in the graph, the predicted life timing based on the evaluation values ​​after the second change is omitted in Figure 12.

[0121] In the first method, when the reference pattern is changed multiple times, the latest increase ratio and the latest past pre-increase estimated evaluation value are calculated by cumulatively taking into account the increasing trend of the evaluation value for each past reference pattern.

[0122] Instead of the final evaluation value of [b], the median or average of the evaluation values ​​obtained most recently, including the final evaluation value, may be used. In this case, the influence of noise contained in the evaluation value can be reduced. The final evaluation value, the median, or the average can be considered as a type of evaluation value of the final stage based on one reference pattern. The representative value is not limited to the median or the average.

[0123] Instead of the initial evaluation value in [c], the median or average of multiple evaluation values ​​obtained around the initial evaluation value, including the initial evaluation value, may be used. In this case, the influence of noise contained in the evaluation value can be reduced. The initial evaluation value, the median, or the average can be considered as a type of evaluation value in the early stage based on one reference pattern. The representative value is not limited to the median or the average.

[0124] As described above, the state monitoring device 5 of this embodiment monitors the state of the robot 1 capable of reproducing a predetermined action. The state monitoring device 5 includes a time series data acquisition unit 51, a storage unit 52, and a lifespan estimation unit 54. The time series data acquisition unit 51 acquires state time series data reflecting the state of the robot 1. The storage unit 52 stores the state time series data acquired by the time series data acquisition unit 51. The lifespan estimation unit 54 acquires an evaluation value for evaluating the state of the robot 1 based on the state time series data, creates a trend line TL1 that indicates the tendency of the evaluation value to change over time, and determines the timing at which the trend line TL1 reaches a predetermined lifespan threshold TH1 as a predicted lifespan timing PL1.

[0125] When the robot's motion, which is the basis for acquiring the state time series data, is changed as shown in FIG. 11, the predicted life timing PL2 is obtained as follows. That is, the life estimation unit 54 obtains the past pre-increase estimated evaluation value s1 for the changed motion. Here, a coefficient indicating how many times the life threshold TH1 applied to the changed motion is compared with the evaluation value p1 at the beginning based on the changed motion is called the life threshold coefficient k1 for the changed motion. The life estimation unit 54 obtains the life threshold TH2 to be applied to the changed motion by multiplying the past pre-increase estimated evaluation value s1 for the changed motion by the life threshold coefficient k1 for the changed motion. The life estimation unit 54 creates a trend line TL2 based on a plurality of evaluation values ​​based on the changed motion, and the timing at which the trend line TL2 reaches the life threshold TH2 to be applied to the changed motion is set as the predicted life timing PL2.

[0126] As a result, even if the behavior of the robot 1 on which the evaluation value is based is changed, the evaluation values ​​before and after the change can be handled consistently to perform lifespan prediction without inconsistency. Therefore, it becomes possible to obtain state time-series data and an evaluation value based on the actual behavior of the robot 1, thereby improving the operating efficiency of the robot 1. A calculation using the past pre-increase estimated evaluation value can obtain a lifespan threshold value TH2 based on the changed behavior.

[0127] In the first method, the lifespan estimation unit 54 obtains an increase ratio ir1 for the pre-change motion by dividing an end evaluation value p2 based on the pre-change motion by an early evaluation value p1 based on the pre-change motion. The lifespan estimation unit 54 obtains a past pre-increase estimated evaluation value s1 for the post-change motion by dividing an early evaluation value q1 based on the post-change motion by the increase ratio ir1 for the pre-change motion.

[0128] This makes it possible to obtain the life threshold value TH2 based on the changed operation through simple calculations using the increase ratio and the past pre-increase estimated evaluation value.

[0129] In the first method, when the robot's motion, which is the basis for acquiring the time-series data of the status signal, has been changed two or more times as shown in Fig. 12, the predicted life timing is obtained as follows. That is, the life estimation unit 54 obtains the increase ratio ir2 for the most recent pre-change motion by dividing the final evaluation value q2 based on the most recent pre-change motion by the past pre-rise estimated evaluation value s1 for the most recent pre-change motion. Except for the above, the calculation of the predicted life timing is performed in the same way as for the first change in the motion of the robot 1. Regardless of how many times the change has occurred, the life threshold coefficient k1 for the motion before the first change is commonly used as the life threshold coefficient.

[0130] This makes it possible to accurately estimate the effect on the evaluation value of multiple changes in the behavior of the robot 1.

[0131] In the state monitoring device 5 of this embodiment, the evaluation value of the final stage based on the operation before the change is the evaluation value p2 of the last time based on the operation before the change.

[0132] This simplifies the calculations.

[0133] In the status monitoring device 5 of this embodiment, the final evaluation value based on the operation before the change can also be a representative value such as the median or average of multiple evaluation values ​​obtained most recently, including the final evaluation value p2 based on the operation before the change.

[0134] In this case, the influence of noise contained in the evaluation value can be reduced.

[0135] In the state monitoring device 5 of this embodiment, the evaluation value in the early stage based on the changed operation is the initial evaluation value q1 based on the changed operation.

[0136] This simplifies the calculations.

[0137] In the status monitoring device 5 of this embodiment, the early evaluation value based on the operation before the change can also be a representative value such as the median or average of multiple evaluation values ​​obtained near the initial time, including the initial evaluation value q1 based on the operation after the change.

[0138] In this case, the influence of noise contained in the evaluation value can be reduced.

[0139] The second method will be described with reference to FIG. 13 as follows.

[0140] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold coefficient TH1 of the first reference pattern is the initial evaluation value p1 of the first reference pattern multiplied by the life threshold coefficient k1 (TH1=p1·k1). [b] A trend line TL1 is created based on a plurality of evaluation values ​​based on the pre-change reference pattern, and the timing at which this trend line TL1 reaches the life threshold value TH1 (the predicted life timing PL1 described above) is calculated. [c] The remaining life rate tr1 is calculated by dividing the time from the timing tp2 of the final evaluation value p2 based on the reference pattern before the change to the predicted life time PL1 by the time from the timing tp1 of the initial evaluation value p1 based on the reference pattern before the change to the predicted life time PL1 (tr1=(PL1-tp2) / (PL1-tp1)). [d1] As shown in the following formula, 1 is subtracted from the life threshold coefficient k1 of the reference pattern before the change, the subtracted value is multiplied by the remaining life rate tr1, and then 1 is added to obtain the life threshold coefficient k2 of the reference pattern after the change. k2 = (k1 - 1) tr1 + 1 [e] The initial evaluation value q1 based on the changed reference pattern is multiplied by the life threshold coefficient k2 of the changed reference pattern to obtain the life threshold TH2 to be applied to the changed reference pattern (TH2 = q1 · k2). [f] A trend line TL2 is created based on a plurality of evaluation values ​​based on the changed reference pattern. The timing at which this trend line TL2 reaches a life threshold value TH2 applied to the changed reference pattern is set as a predicted life timing PL2.

[0141] In the second method, if the reference pattern is changed multiple times, processes [b], [c], [d1], [e], and [f] can be performed for each change. However, the above [d1] is extended as shown below in [d1'] so that it can be applied to the second and subsequent changes.

[0142] Consider a case where the [d1'] reference pattern is changed N times and appears in the order of the first reference pattern, the second reference pattern, ..., the (N+1)th reference pattern. The life threshold coefficient k(N+1) corresponding to the (N+1)th reference pattern can be calculated by subtracting 1 from the life threshold coefficient k1 calculated for the first reference pattern, multiplying the value after subtraction by the remaining life rates tr1, tr2, ..., trN calculated from the first reference pattern to the Nth reference pattern, and then adding 1 to the result, as shown in the following formula. k(N+1)=(k1-1)·tr1·tr2·····trN+1

[0143] As described above, in the second method, when the robot's motion, which is the basis for acquiring state time series data, is changed as shown in FIG. 13, the predicted life timing is obtained as follows. That is, the life estimation unit 54 obtains a life threshold coefficient k2 for the changed motion, which indicates how many times the evaluation value q1 at the beginning based on the changed motion is, for the changed motion. The life estimation unit 54 obtains the life threshold TH2 to be applied to the changed motion by multiplying the evaluation value q1 at the beginning based on the changed motion by the life threshold coefficient k2 for the changed motion. The life estimation unit 54 creates a trend line TL2 based on multiple evaluation values ​​based on the changed motion, and sets the timing at which the trend line TL2 reaches the life threshold TH2 to be applied to the changed motion as the predicted life timing PL2.

[0144] This allows the life threshold value TH2 based on the changed operation to be obtained through simple calculation by using the life threshold coefficient.

[0145] As described above, in the second method, when the robot's motion, which is the basis for acquiring the state time series data, is changed as shown in FIG. 13, the predicted life timing is obtained as follows. That is, the life estimation unit 54 creates a trend line TL1 based on a plurality of evaluation values ​​based on the motion before the change, and calculates the predicted life timing PL1 for the motion before the change, which is the timing at which this trend line TL1 reaches the life threshold value TH1 applied to the motion before the change. The life estimation unit 54 obtains the remaining life rate tr1 for the motion before the change by dividing the time from the timing tp2 of the final evaluation value p2 based on the motion before the change to the predicted life timing PL1 by the time from the timing tp1 of the initial evaluation value p1 based on the motion before the change to the predicted life timing PL1. Here, the coefficient indicating how many times the life threshold value TH1 applied to the motion before the change is the initial evaluation value p1 based on the motion before the change is called the life threshold coefficient k1 for the motion before the change. The life estimation unit 54 calculates the life threshold coefficient k2 for the changed operation by subtracting 1 from the life threshold coefficient k1 for the operation before the change, multiplying the result by the remaining life rate tr1 for the operation before the change, and then adding 1 to the result.

[0146] This makes it possible to obtain the life threshold value TH2 based on the changed operation through simple calculation using the life threshold coefficient and the remaining life rate.

[0147] In the second method, when the robot's motion, which is the basis for acquiring the time series data of the state signal, has been changed two or more times (N times), the predicted life timing is obtained as follows. That is, when focusing on one of the first to Nth changes, the life estimation unit 54 calculates the predicted life timing PL for the motion immediately before the focused change. The predicted life timing PL for the motion immediately before the focused change is obtained as the timing at which a trend line TL is created based on a plurality of evaluation values ​​based on the motion immediately before the focused change, and the trend line TL reaches the life threshold TH applied to the motion immediately before the focused change. Next, the life estimation unit 54 obtains the remaining life rate tr for the motion immediately before the focused change. The remaining life rate tr for the motion immediately before the focused change is obtained by dividing the time from the timing of the final evaluation value based on the motion immediately before the focused change to the predicted life timing PL for the motion immediately before the focused change by the time from the timing of the initial evaluation value based on the motion immediately before the focused change to the predicted life timing PL for the motion immediately before the focused change. The life estimation unit 54 focuses on each of the first to Nth changes and obtains the predicted life timing PL1, PL2,...,PLN and the remaining life rate tr1, tr2,...,trN for the operation immediately before the focused change. The life estimation unit 54 subtracts 1 from the life threshold coefficient k1 for the operation before the first change, multiplies the subtracted value by the sum of the remaining life rates tr1, tr2,...,trN from the first to Nth changes, and adds 1 to the multiplied value to obtain the life threshold coefficient k(N+1) for the operation after the Nth change. The life estimation unit 54 multiplies the evaluation value at the beginning based on the operation after the Nth change by the life threshold coefficient k(N+1) for the operation after the Nth change to obtain the life threshold TH(N+1) to be applied to the operation after the change. The life estimation unit 54 creates a trend line TL(N+1) based on multiple evaluation values ​​based on the operation after the Nth change, and sets the timing at which the trend line TL(N+1) reaches the life threshold TH(N+1) applied to the operation after the Nth change as the predicted life timing PL(N+1).

[0148] This makes it possible to accurately estimate the effect on the evaluation value of multiple changes in the behavior of the robot 1.

[0149] As a modification of the second method described above, the following [d2] can be performed instead of [d1].

[0150] [d2] The life threshold coefficient k1 obtained for the operation before the change is used as the base, and the remaining life rate tr1 for the operation before the change is used as the exponent to obtain the life threshold coefficient k2 for the operation after the change. k2=k1 tr1

[0151] In this modified example, if the reference pattern is changed multiple times, processes [b], [c], [d2], [e], and [f] can be performed for each change. However, the above [d2] is expanded as shown below in [d2'] so that it can be applied to the second and subsequent changes.

[0152] [d2'] Consider a case where the reference pattern is changed N times and appears in the order of the first reference pattern, the second reference pattern, ..., and the (N+1)th reference pattern. The life threshold coefficient k(N+1) corresponding to the (N+1)th reference pattern is calculated as a power value with the value Π obtained by multiplying the remaining life rates tr1, tr2, ..., trN calculated from the first reference pattern to the Nth reference pattern as the base, as shown in the following formula. k(N+1)=k1 Π

[0153] Note that under the same conditions, the life threshold coefficients k2, . . . , k(N+1) will be similar whether calculated using [d1] and [d1'] or [d2] and [d2'].

[0154] As described above, in the modified second method, when the robot's motion, which is the basis for acquiring the time-series data of the status signal, is changed once, the predicted life timing is obtained as follows: That is, the life estimation unit 54 obtains a power value, which is the life threshold coefficient k1 obtained for the motion before the change as the base and the remaining life rate tr1 for the motion before the change as the exponent, as the life threshold coefficient k2 for the motion after the change.

[0155] This allows the life threshold value TH2 based on the changed operation to be obtained by simple calculation.

[0156] In a modified version of the second method, when the robot's motion, which is the basis for acquiring the time-series data of the status signal, has been changed two or more times (N times), the predicted life timing is obtained as follows: That is, the life estimation unit 54 determines a power value in which the life threshold coefficient k1 for the motion before the first change is used as the base and the sum of the remaining life rates tr1, tr2,..., trN from the first to the Nth change is used as the exponent, as the life threshold coefficient k(N+1) for the motion after the Nth change.

[0157] This makes it possible to accurately estimate the effect on the evaluation value of multiple changes in the behavior of the robot 1.

[0158] The third method will be described with reference to FIG. 14 as follows.

[0159] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold coefficient TH1 of the first reference pattern is the initial evaluation value p1 of the first reference pattern multiplied by the life threshold coefficient k1 (TH1=p1·k1). [b1] A life threshold coefficient k2 of the changed reference pattern is calculated by dividing the life threshold TH1 of the pre-change reference pattern by the final evaluation value p2 based on the pre-change reference pattern (k2=TH1 / p2). [c] The initial evaluation value q1 based on the changed reference pattern is multiplied by the life threshold coefficient k2 of the changed reference pattern to obtain the life threshold TH2 to be applied to the changed reference pattern (TH2 = q1 · k2). [d] A trend line TL2 is created based on a plurality of evaluation values ​​based on the changed reference pattern. The timing at which this trend line TL2 reaches a life threshold value TH2 of the changed reference pattern is set as a predicted life timing PL2.

[0160] In the third method, for example, if the life threshold coefficient k1 of the initial (before change) reference pattern is 1.20 and the life threshold coefficient k2 of the changed reference pattern is 1.15, it can be considered that the remaining life of robot 1 has been consumed by the difference, that is, 0.05.

[0161] In the third method, if the reference pattern is changed multiple times, the processes [b1], [c], and [d] may be performed for each change.

[0162] As a modification of the above-mentioned third method, the following [b2] can be performed instead of [b1].

[0163] [b2] Create a trend line TL1 based on multiple evaluation values ​​based on the pre-change reference pattern. Obtain the value indicated by this trend line TL1 at timing tp2 of the final evaluation value p2 based on the pre-change reference pattern. Divide the life threshold value TH1 of the pre-change reference pattern by the obtained value to obtain a life threshold coefficient k2 of the post-change reference pattern.

[0164] As described above, in the third method, when the robot's motion, which is the basis for acquiring state time-series data, is changed as shown in Fig. 14, the life threshold coefficient k2 for the changed motion is obtained by dividing the life threshold coefficient k1 for the motion before the change by the final evaluation value p2 based on the motion before the change. In a modified example of the third method, the life threshold coefficient k2 for the changed motion is obtained by dividing the life threshold coefficient k1 for the motion before the change by the value indicated by the trend line TL1 based on the motion before the change at the timing tp2 of the final evaluation value p2.

[0165] This allows the life threshold value TH2 based on the changed operation to be obtained by simple calculation.

[0166] The fourth method will be described with reference to FIG. 15 as follows.

[0167] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold coefficient TH1 of the first reference pattern is the initial evaluation value p1 of the first reference pattern multiplied by the life threshold coefficient k1 (TH1=p1·k1). [b] Estimate an adjustment coefficient that expresses the ratio of the effect of changing the reference pattern on the evaluation value. For example, the adjustment coefficient ka1 can be calculated by dividing the final evaluation value p2 based on the reference pattern before the change by the initial evaluation value q1 based on the reference pattern after the change (ka1=p2 / q1). [c] The life threshold value TH1 of the reference pattern before the change is divided by the adjustment coefficient ka1 to obtain the life threshold value TH2 of the reference pattern after the change (TH2=TH1 / ka1). [d] A trend line TL2 is created based on a plurality of evaluation values ​​based on the changed reference pattern. The timing at which this trend line TL2 reaches a life threshold value TH2 applied to the changed reference pattern is set as a predicted life timing PL2.

[0168] The adjustment coefficient ka1 described in [b] above is the same as the adjustment coefficient ka1 described in the seventh method described below. Instead of this adjustment coefficient ka1, the adjustment coefficient ka2 described in the ninth method described below, or the adjustment coefficient ka3 described in the eleventh method, etc. may be used.

[0169] As described above, in the fourth method, when the robot's motion, which is the basis for acquiring the state time series data, is changed as shown in FIG. 15, the predicted life timing is obtained as follows. That is, the life estimation unit 54 estimates an adjustment coefficient ka1 that expresses, in a ratio, the influence on the evaluation value caused by the change in the motion of the robot 1. The life estimation unit 54 obtains a life threshold TH2 for the changed motion by dividing the life threshold TH1 applied to the motion before the change by the adjustment coefficient ka1. The life estimation unit 54 creates a trend line TL2 based on the evaluation value based on the changed motion. The life estimation unit 54 determines the timing at which the trend line TL2 reaches the life threshold TH2 applied to the changed motion as the predicted life timing PL2.

[0170] This allows the life threshold value TH2 based on the changed operation to be obtained by simple calculation.

[0171] The fifth method will be described with reference to FIG. 16 as follows.

[0172] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold coefficient TH1 of the first reference pattern is the initial evaluation value p1 of the first reference pattern multiplied by the life threshold coefficient k1 (TH1=p1·k1). [b] A trend line TL2 is created based on a plurality of evaluation values ​​based on the changed reference pattern, and the value indicated by this trend line TL2 at the timing when the elapsed time is zero is calculated as the past pre-rise estimated evaluation value st1. [c] The lifetime threshold value TH2 based on the changed reference pattern is calculated by multiplying the past pre-increase estimated evaluation value st1 by the lifetime threshold coefficient k1 (TH2 = st1 · k1). [d] The timing at which the trend line TL2 mentioned above reaches the life threshold value TH2 based on the changed reference pattern is defined as the predicted life timing PL2.

[0173] In the example of Fig. 16, the predicted life timing PL1 based on the pre-change reference pattern and the predicted life timing PL2 based on the post-change reference pattern are significantly different. However, this is because the graphs of Fig. 11 to Fig. 23 are primarily intended to explain the method in an easy-to-understand manner. In actual operation, it is considered that the predicted life timing PL2 based on the post-change reference pattern can be obtained with good accuracy.

[0174] In the fifth method, if the reference pattern is changed multiple times, the processes [b] to [d] can be performed for each change. The life threshold coefficient k1 can be the same as the one initially determined in [a].

[0175] In the fifth method, when the robot's motion is changed multiple times, an Nth trend line TLN is created, and the value indicated by the trend line TLN at the timing of zero elapsed time is determined as the past pre-increase estimated evaluation value stN for the changed motion. The life estimation unit 54 multiplies the past pre-increase estimated evaluation value stN for the changed motion by the above-mentioned life threshold coefficient k1 to determine the life threshold THN to be applied to the changed motion. The life estimation unit 54 determines the timing at which the trend line TLN created based on multiple evaluation values ​​based on the changed motion reaches the life threshold THN to be applied to the changed motion as the predicted life timing PLN.

[0176] As described above, in the fifth method, when the robot's motion, which is the basis for acquiring the state time series data, is changed as shown in FIG. 16, the predicted life timing is obtained as follows. That is, the life estimation unit 54 creates a trend line TL2 based on a plurality of evaluation values ​​based on the changed motion, and obtains the value indicated by the trend line TL2 at the timing of zero elapsed time as the past pre-increase estimated evaluation value st1 for the changed motion. Here, a coefficient indicating how many times the life threshold TH1 applied to the changed motion is the initial evaluation value p1 based on the changed motion is called the life threshold coefficient k1 for the changed motion. The life estimation unit 54 obtains the life threshold TH2 applied to the changed motion by multiplying the past pre-increase estimated evaluation value st1 for the changed motion by the life threshold coefficient k1 for the changed motion. The life estimation unit 54 determines the timing tp2 at which the trend line TL2 created based on a plurality of evaluation values ​​based on the changed motion reaches the life threshold TH2 applied to the changed motion as the predicted life timing PL2.

[0177] As a result, by using the life threshold coefficient k1 and the trend line TLN, the life threshold TH2 based on the changed operation can be obtained by simple calculation.

[0178] The sixth method will be described with reference to FIG.

[0179] In the fifth method described above, the value indicated by the trend line TL2 at the timing of elapsed time zero is calculated as the past pre-increase estimated evaluation value st1 for the changed operation, as shown in Fig. 16. Instead, in the sixth method, the past pre-increase estimated evaluation value st1x is calculated as follows.

[0180] First, a trend line TL2 is obtained based on a plurality of evaluation values ​​based on the operation after the change. In Fig. 17, the trend line TL2 is shown by a dashed line. Next, the life estimation unit 54 obtains the slope of an auxiliary line AL1 (to be described later) based on the load corresponding to the evaluation value on which the trend line TL2 is obtained and the load for the operation before the change.

[0181] Generally, the deterioration of a machine progresses faster as the load increases. The slope of the auxiliary line AL1 corresponds to the slope of the trend line TL2 corrected in consideration of the difference in load before and after the change in operation. Let the slope of the trend line TL2 be ΔTL2, and the load corresponding to the evaluation value on which the trend line TL2 is based be L TL2 If the load corresponding to the evaluation value of the operation before the change is L0, the slope ΔAL1 of the auxiliary line AL1 can be obtained by ΔAL1 = ΔTL2 / (L TL2 / L0). Hereinafter, the value of L TL2 / L0 may be referred to as the load correction rate.

[0182] Subsequently, the life estimation unit 54 creates an auxiliary line AL1 that has the obtained slope ΔAL1 and passes through the first evaluation value q1 after the change. An example of the auxiliary line AL1 is shown in FIG. 17. The auxiliary line AL1 estimates the transition of the evaluation value assuming that the robot 1 had also performed the operation after the change before the change. L TL2 When L

[0183] < L0, since the load correction rate is less than 1, the slope of the auxiliary line AL1 becomes larger compared to the slope of the trend line TL2.

[0184] Load L TL2 , L0 can be arbitrarily selected as to what value to use. For example, as the load, the root mean square value of the square of the motor current (I2 described above) can be used. As the load, a value obtained by taking the cube root of the cube mean of the absolute value of the current value can be used.

[0185] The load can be calculated by multiplying the average load torque Tm by the following formula: TL2 , L0.

number

[0186] The trend line TL2 is calculated based on multiple evaluation values, so the load L TL2 It is preferable that L0 is a representative value (for example, a median or an average value) of the corresponding multiple loads. The load L0 of the operation before the change can also be a representative value such as the median or average value of the multiple loads.

[0187] As described above, in the sixth method, when the robot's motion, which is the basis for acquiring the state time series data, is changed as shown in FIG. 17, the predicted life timing is obtained as follows. That is, the life estimation unit 54 calculates the load L0 based on the motion after the change by multiplying the load L based on the motion before the change by the load L TL2 The load correction factor is calculated by dividing the load correction factor (L TL2 / L0). The value indicated by this auxiliary line AL1 at the timing of zero elapsed time is obtained as the past pre-rise estimated evaluation value st1x. The timing when the trend line TL2 created based on multiple evaluation values ​​based on the changed operation reaches the life threshold value TH2 applied to the changed operation is defined as the predicted life timing PL2.

[0188] This allows the change in load before and after the operation change to be taken into consideration, making it possible to more appropriately predict the lifespan based on the operation after the change.

[0189] The seventh method will be described with reference to FIG.

[0190] [a] An adjustment coefficient ka1 is calculated by dividing the final evaluation value p2 based on the pre-change reference pattern by the initial evaluation value q1 based on the post-change reference pattern (ka1=p2 / q1). [b] The evaluation value obtained from the changed reference pattern is multiplied by an adjustment coefficient ka1 to obtain an adjusted evaluation value. [c] A trend line TL2 is created based on the multiple adjustment evaluation values ​​obtained for the changed reference pattern. The timing at which this trend line TL2 reaches the life threshold value TH1 set for the reference pattern before the change is set as the predicted life timing PL2.

[0191] The adjustment coefficient ka1 can be considered as an estimate that expresses, in a ratio, the effect on the evaluation value due to a change in the motion of the robot 1. Even after the motion pattern of the robot 1 is changed, the evaluation value obtained after the change can be made consistent with the evaluation value obtained before the change by a simple calculation of multiplying the adjustment coefficient ka1. In this method, in order to obtain the trend line TL2, it is necessary to obtain evaluation values ​​at least twice after the motion of the robot 1 is changed. In order to improve the accuracy of the trend line, the trend line TL2 may be obtained on the condition that evaluation values ​​are obtained a predetermined number of times, for example, five times.

[0192] As described above, in the seventh method, when the robot's motion, which is the basis for acquiring the state time-series data, is changed as shown in Fig. 18, the predicted life timing is obtained as follows. That is, the life estimation unit 54 estimates an adjustment coefficient ka1 that expresses, as a ratio, the influence that the change in the motion of the robot 1 has on the evaluation value. The life estimation unit 54 creates a trend line TL2 based on an adjusted evaluation value obtained by multiplying the evaluation value based on the changed motion by the adjustment coefficient ka1. The life estimation unit 54 determines the timing at which the trend line TL2 reaches the life threshold value TH1 applied to the motion before the change as the predicted life timing PL2.

[0193] In this way, the adjustment coefficient ka1 can be used to make the evaluation value obtained after the operation change consistent with the evaluation value obtained before the change.

[0194] In the seventh method, when the robot's behavior, which is the basis for acquiring the state time-series data, is changed as shown in FIG. 18, the life estimation unit 54 obtains an adjustment coefficient ka1 by dividing the final evaluation value p2 based on the behavior before the change by the initial evaluation value q1 based on the changed reference pattern.

[0195] This allows the adjustment coefficient ka1 to be calculated by a simple calculation. Also, the adjustment coefficient ka1 can be calculated even immediately after the robot's operation is changed.

[0196] The eighth method will be described with reference to FIG.

[0197] [a] An adjustment coefficient ka1 is calculated by dividing the final evaluation value p2 based on the pre-change reference pattern by the initial evaluation value q1 based on the post-change reference pattern (ka1=p2 / q1). [b] The evaluation value of the pre-change reference pattern is used as is. For the evaluation value obtained from the post-change reference pattern, the adjusted evaluation value obtained by multiplying the evaluation value by the adjustment coefficient ka1 is used instead. [c] Based on multiple evaluation values ​​spanning both before and after the change, an overall trend line (trend line) GTL1 is created. The timing at which this overall trend line GTL1 reaches the life threshold value TH1 defined for the reference pattern before the change is set as the predicted life timing PL2.

[0198] The predicted life timing PL2 obtained by the eighth method is approximately equal to the predicted life timing PL2 obtained by the above-mentioned seventh method.

[0199] In the eighth method, as in the seventh method, the adjustment coefficient ka1 can be considered as an estimate that expresses, in percentage, the effect on the evaluation value caused by a change in the behavior of the robot 1. By using this adjustment coefficient ka1, it is possible to appropriately generate a comprehensive trend line GTL1 that spans the evaluation values ​​before and after the change.

[0200] As time passes and evaluation values ​​based on the changed reference pattern are appropriately accumulated, a trend line TL2 based only on the changed evaluation values ​​multiplied by the adjustment coefficient ka1 can be generated. This trend line TL2 is generated according to the seventh method described above. At the same time that this trend line TL2 is displayed on the display unit 55, or after a predetermined time has passed since the trend line TL2 was displayed, the display of the overall trend line GTL1 may be stopped.

[0201] As described above, in the eighth method, when the robot's motion, which is the basis for acquiring the state time-series data, is changed as shown in Fig. 19, the predicted life timing is obtained as follows. That is, the life estimation unit 54 estimates an adjustment coefficient ka1 that expresses, as a ratio, the influence that the evaluation value receives due to the change in the motion of the robot 1. The life estimation unit 54 creates an overall trend line GTL1 based on the evaluation value based on the motion before the change and an adjusted evaluation value obtained by multiplying the evaluation value based on the motion after the change by the adjustment coefficient ka1. The life estimation unit 54 determines the timing at which the overall trend line GTL1 reaches the life threshold value TH1 applied to the motion before the change as the predicted life timing PL2.

[0202] In this way, the adjustment coefficient ka1 can be used to make the evaluation value obtained after the operation change consistent with the evaluation value obtained before the change.

[0203] The ninth method will be described with reference to FIG.

[0204] [a] A trend line TL1 is created based on multiple evaluation values ​​based on the base pattern before the change. [b] Calculate the value of the created trend line TL1 at timing tq1 of the initial evaluation value q1 based on the changed reference pattern. Hereinafter, this value will be referred to as the trend line value tv1. [c] The trend line value tv1 is divided by the initial evaluation value q1 based on the changed reference pattern to obtain an adjustment coefficient ka2 (ka2=tv1 / q1). [d] The evaluation value obtained from the changed reference pattern is multiplied by the adjustment coefficient ka2 to obtain an adjusted evaluation value. [e] A trend line TL2 is created based on the multiple adjustment evaluation values ​​obtained for the changed reference pattern. The timing at which this trend line TL2 reaches the life threshold value TH1 set for the reference pattern before the change is set as the predicted life timing PL2.

[0205] The tenth method will be described with reference to FIG. 21 as follows. [a] A trend line TL1 is created based on multiple evaluation values ​​based on the base pattern before the change. [b] Calculate the value of the created trend line TL1 at timing tq1 of the initial evaluation value q1 based on the changed reference pattern. Hereinafter, this value will be referred to as the trend line value tv1. [c] The trend line value tv1 is divided by the initial evaluation value q1 based on the changed reference pattern to obtain an adjustment coefficient ka2 (ka2=tv1 / q1). [d] The evaluation value of the pre-change reference pattern is used as is. For the evaluation value obtained from the post-change reference pattern, the adjusted evaluation value obtained by multiplying the evaluation value by the adjustment coefficient ka2 is used instead. [e] Create an overall trend line GTL1 based on multiple evaluation values ​​spanning both before and after the change. The timing at which this overall trend line GTL1 reaches the life threshold value TH1 applied to the reference pattern before the change is set as the predicted life timing PL2.

[0206] Similarly to the seventh method and the like, the ninth and tenth methods also make it possible to match the evaluation value after the change in the movement pattern of the robot 1 with the evaluation value before the change.

[0207] As described above, in the ninth and tenth methods, when the robot's motion, which is the basis for acquiring the state time-series data, is changed as shown in Fig. 20 or 21, the adjustment coefficient ka2 is estimated as follows. That is, the life estimation unit 54 creates a trend line TL1 based on a plurality of evaluation values ​​based on the motion before the change, and calculates a trend line value tv1, which is a value indicated by the trend line TL1 at timing tq1 of the initial evaluation value q1 based on the motion after the change. The life estimation unit 54 calculates the adjustment coefficient ka2 by dividing the trend line value tv1 by the initial evaluation value q1 based on the motion after the change.

[0208] This allows the adjustment coefficient ka2 to be calculated by a simple method using the trend line TL1. Also, the adjustment coefficient ka1 can be calculated even immediately after the operation of the robot 1 is changed.

[0209] The eleventh method is described below with reference to FIG.

[0210] [a] A trend line TL1 is created based on a plurality of evaluation values ​​based on the reference pattern before the change. A value indicated by the created trend line TL1 at the timing of changing the reference pattern is calculated. Hereinafter, this value is referred to as a first trend line value tvx1. In the example of FIG. 22, the timing of changing the reference pattern is the timing when the operator instructs the controller 90 to change the operation program, but is not limited to this. In this embodiment, usually, when a new reference pattern is registered, an evaluation value based on the new reference pattern is also obtained at the same time. Therefore, the timing when the first evaluation value based on the new reference pattern is obtained can also be the timing of changing the reference pattern. [b] Create a trend line TL2 based on multiple evaluation values ​​based on the changed reference pattern. Calculate the value that the created trend line TL2 indicates at the timing of the change of the reference pattern. Hereinafter, this value will be referred to as the second trend line value tvx2. [c] The adjustment coefficient ka3 is calculated by dividing the first trend line value tvx1 by the second trend line value tvx2 (ka3=tvx1 / tvx2). [d] The evaluation value obtained using the changed reference pattern is multiplied by an adjustment coefficient ka3 to obtain an adjusted evaluation value. [e] A trend line TL2x is created based on the multiple adjustment evaluation values ​​obtained for the changed reference pattern. The timing at which this trend line TL2x reaches the life threshold value TH1 set for the original reference pattern is set as the predicted life timing PL2.

[0211] The twelfth method is described below with reference to FIG.

[0212] [a] A trend line TL1 is created based on a plurality of evaluation values ​​based on the reference pattern before the change. The value indicated by the created trend line TL1 at the timing of the change of the reference pattern (the above-mentioned first trend line value tvx1) is calculated. [b] A trend line TL2 is created based on a plurality of evaluation values ​​based on the changed reference pattern. The value indicated by the created trend line TL2 at the timing of the change of the reference pattern (the above-mentioned second trend line value tvx2) is calculated. [c] The adjustment coefficient ka3 is calculated by dividing the first trend line value tvx1 by the second trend line value tvx2 (ka3=tvx1 / tvx2). [d] The evaluation value of the pre-change reference pattern is used as is, and the evaluation value obtained from the post-change reference pattern is replaced with an adjusted evaluation value obtained by multiplying the evaluation value by an adjustment coefficient ka3. [e] Create an overall trend line GTL1 based on multiple evaluation values ​​spanning both before and after the change. The timing at which this overall trend line GTL1 reaches the life threshold value TH1 applied to the reference pattern before the change is set as the predicted life timing PL2.

[0213] Similarly to the seventh method and the like, the eleventh and twelfth methods also make it possible to match the evaluation value after the change in the movement pattern of the robot 1 with the evaluation value before the change.

[0214] As described above, in the eleventh and twelfth methods, when the robot's motion, which is the basis for acquiring the state time-series data, is changed as shown in FIG. 22 or FIG. 23, the adjustment coefficient ka3 is estimated as follows. The life estimation unit 54 creates a trend line TL1 based on a plurality of evaluation values ​​based on the motion before the change, and determines a first trend line value tvx1 indicated by the trend line TL1 at the timing of the motion change of the robot 1. The life estimation unit 54 creates a trend line TL2 based on a plurality of evaluation values ​​based on the motion after the change, and determines a second trend line value tvx2 indicated by the trend line TL2 at the timing of the motion change of the robot 1. The life estimation unit 54 determines the adjustment coefficient ka3 by dividing the first trend line value tvx1 by the second trend line value tvx2.

[0215] As a result, the adjustment coefficient ka3, which is a ratio representing the effect on the evaluation value due to a change in the operation of the robot 1, can be accurately estimated using the two trend lines TL1 and TL2.

[0216] In the seventh to twelfth methods, the operation of the robot 1 may be changed multiple times. In this case, "before a change" should be read as "before the most recent change" and "after a change" should be read as "after the most recent change", respectively, and the processing described for each method should be carried out for each change.

[0217] Next, the estimation of the life span when a part of the robot 1 is replaced will be described.

[0218] Due to some reason such as the occurrence of a failure, a part of the robot 1 may be replaced. The robot 1 has many parts, but here, a case where a part that is dominant in terms of the lifespan of the robot 1 is replaced is considered. A typical example of such a part is a reducer. The lifespan estimation described here may be applied not only to cases where a reducer is replaced, but also, for example, when replacing an output gear of a reducer, a motor, or the like.

[0219] When a part has been replaced, the worker operates the status monitoring device 5 to identify the axis of the robot 1 whose part has been replaced and instructs it to be reset. As a result, an elapsed time reset signal is input to the status monitoring device 5.

[0220] When an operation to reset the elapsed time is performed, the status monitor 5 performs a life prediction for the specified axis of the robot 1 without using the data used for the life prediction before the reset. However, the basic operation pattern set before the reset continues to be used.

[0221] Data that is removed from the basis of life prediction by resetting the elapsed time include, for example, time series data of the status signal, evaluation value, increase value, estimated evaluation value before increase, predicted life timing, trend line, load correction factor, life threshold coefficient other than the predetermined life threshold coefficient, life threshold other than the predetermined life threshold, remaining life rate, adjustment coefficient, and adjusted evaluation value. When the elapsed time is reset, the values ​​before the reset are not carried over for these data. Therefore, for example, when the elapsed time reset operation is performed at the "present" point in FIG. 12, all evaluation values, trend lines, etc. that existed in the plot area are erased. After that, evaluation values ​​are plotted at appropriate timing according to the number of days that have passed since 0 days, and trend lines, etc. are recreated.

[0222] The data removed from the basis for life prediction by resetting the elapsed time is deleted from the storage unit 52 of the condition monitoring device 5. However, the data may be continuously stored so that the data can be referred to in the future for the purpose of, for example, maintenance. The date and time when the elapsed time was reset may be stored in the storage unit 52, and this date and time may be displayed on the display unit 55, for example, simultaneously with the trend line graph.

[0223] As described above, the life estimation unit 54 resets the elapsed time to zero when an elapsed time reset signal indicating that a part of the robot 1 has been replaced is input. The behavior of the robot 1, which is the basis for acquiring the time series data of the status signal, is the same before and after the elapsed time is reset. The life estimation unit 54 multiplies the initial evaluation value based on the behavior after the elapsed time is reset to zero by a predetermined life threshold coefficient to obtain the life threshold after the reset. A trend line is created based on multiple evaluation values ​​based on the behavior after the elapsed time is reset to zero, and the timing at which the trend line reaches the life threshold after the reset is set as the predicted life timing.

[0224] This allows appropriate life estimation to be performed, taking into account resetting deterioration due to part replacement.

[0225] Although the preferred embodiment of the present disclosure has been described above, the above configuration can be modified, for example, as follows. A single modification may be made, or multiple modifications may be made in any combination.

[0226] The status monitoring device 5 does not need to be directly connected to the robot 1, and may acquire time-series data reflecting the status of the robot 1 from the controller 90 of the robot 1 via a communication line such as the Internet. In this case, the controller 90 acquires and stores the current value in real time during the reproducing operation, and transmits the time-series data of the current value together with the program number, information identifying the reference pattern, the date and time when the current value was acquired, and information identifying the servo motor by batch processing or the like to the status monitoring device 5.

[0227] The status monitoring device 5 may not be provided separately from the controller 90, but may be built into the controller 90. Furthermore, the status monitoring device 5 may be realized using the computer of the controller 90 of the robot 1, without providing a computer that functions as a CPU, ROM, RAM, auxiliary storage device, etc. of the status monitoring device 5. In this case, the display unit 55 may be configured as a part of a teaching pendant of the robot 1, for example.

[0228] The reference pattern may be extracted from the position time series data manually instead of automatically. For example, an operator may specify a program number, a program name, etc., display a graph of the position time series data as shown in Fig. 6 on the display unit 55, and specify the start and end of a time interval on the screen to extract the reference pattern.

[0229] In the first method, the increase ratio may be less than 1. In this case, the increase ratio may be considered to be 1. In the second method or its modified example, for example, the trend line may be horizontal or sloping downward to the right, and the timing of reaching the life threshold may not be obtained. In this case, the remaining life rate may be considered to be 1. These two situations are examples of possible problems with appropriate robot life prediction, but are not limited to these. Such a situation may be caused by the reference pattern on which the evaluation value is based being inappropriate. Therefore, the state monitoring device 5 may discard the currently used reference pattern and obtain a new reference pattern when it is found that a situation is causing a problem in the calculation for life prediction.

[0230] In the second and subsequent methods, the evaluation value in the early stages may be a representative value, such as the median or average value, of multiple evaluation values ​​acquired near the initial stage.

[0231] In the second and subsequent methods, the final evaluation value based on the operation before the change may be a representative value such as the median or average of the multiple evaluation values ​​obtained most recently.

[0232] In the eleventh method, the predicted life timing is calculated using the first trend line value and the second trend line value. In this way, the method of evaluating the influence of the change in the robot's operation on the evaluation value using two trend line values ​​can be applied to other methods as long as there is no contradiction.

[0233] In the above-mentioned 12 methods, the predicted life timing is calculated by consistently handling the evaluation values ​​before and after the change in the robot's operation by changing the life threshold or adjusting the evaluation value, etc. Instead of or in addition to this, the time axis of the transition of the evaluation value after the change in operation may be corrected by an appropriate method.

[0234] The evaluation interval in the state time-series data may be set by any other appropriate method instead of pattern matching with the reference pattern.

[0235] The flowcharts shown in the above embodiment are merely examples, and some processes may be omitted, the contents of some processes may be changed, or new processes may be added.

[0236] The functions of the elements disclosed herein can be performed using circuits or processing circuits, including general purpose processors, special purpose processors, integrated circuits, Application Specific Integrated Circuits (ASICs), conventional circuits, and / or combinations thereof, configured or programmed to perform the disclosed functions. Processors are considered processing circuits or circuits because they include transistors and other circuits. In this disclosure, a circuit, unit, or means is hardware that performs the recited functions or hardware that is programmed to perform the recited functions. The hardware may be hardware disclosed herein or other known hardware that is programmed or configured to perform the recited functions. In the case where the hardware is a processor, which is considered a type of circuit, the circuit, means, or unit is a combination of hardware and software, and the software is used to configure the hardware and / or the processor.

[0237] From the above disclosure, at least the following technical ideas can be grasped.

[0238] [1] A status monitoring device for monitoring a status of a robot capable of reproducing a predetermined motion, comprising: a motion state time measurement unit that determines a motion state of the robot and measures a motion state time that is a time during which the motion state of the robot continues; a basic data processing unit that calculates a number of basic data acquisitions by dividing the motion state time by a preset basic data upper limit length, acquires basic data by the number of basic data acquisitions from position time series data of each axis of the motion state of the robot corresponding to the motion state time, and stores at least one basic data; a search processing unit that acquires the repeatedly reproduced position time series data of each axis and judges whether or not the data matches the stored basic data; a time series data acquisition unit that acquires time series data of a state signal of each axis that reflects a state of the robot in a time interval that matches the basic data; an evaluation value calculation unit that acquires an evaluation value for evaluating a state of the robot based on the time series data acquired by the time series data acquisition unit; a life estimation unit that creates a trend line that indicates a tendency for the evaluation value to change over time and determines a timing at which the trend line reaches a predetermined life threshold as a predicted life timing; A condition monitoring device comprising:

[0239] This makes it possible to appropriately obtain basic data for predicting the remaining life from position time-series data while the robot is actually operating by executing a program for an actual task.

[0240] [2] [1] A condition monitoring device comprising: Further comprising an operation change monitoring unit for monitoring the repeatedly reproduced state signals of each axis; the operation change monitoring unit monitors whether or not a portion matching the basic data appears in a state signal of each axis that reflects a state of the robot; When the number of occurrences within a predetermined monitoring period is equal to or less than a certain number, the basic data processing unit acquires new basic data and replaces the basic data whose number of occurrences is equal to or less than the certain number.

[0241] This makes it possible to change the basic data accordingly even if the robot's operation for the actual work is changed, ensuring continuity in the collection of evaluation values.

[0242] [3] A condition monitoring device according to [1] or [2], A status monitoring device that determines that the robot is in an operating state when one or more of the multiple axes possessed by the robot are moving.

[0243] This makes it possible to collect basic data and obtain an evaluation value in a state suitable for state evaluation for at least one or more axes of the robot.

[0244] [4] A condition monitoring device according to any one of [1] to [3], A status monitoring device that determines that the robot is not in operation when all axes of the robot are stopped for a predetermined period of time or longer.

[0245] As a result, even if a state occurs in which all axes stop for a short period of time while the robot is operating, the operating state can be determined to include the short period of stoppage.

[0246] [5] A condition monitoring device according to [1] or [2], A status monitoring device that determines that the robot is in an operating state when all of the multiple axes of the robot are moving.

[0247] This allows basic data to be collected and evaluation values ​​to be obtained with all axes of the robot in a state suitable for condition evaluation.

[0248] [6] A condition monitoring device according to any one of [1] to [5], A status monitoring device which does not determine that a robot is in an operating state during an initial specified time period during which the operating state of the robot is determined.

[0249] This makes it possible to prevent, when the robot performs a positioning operation or the like at the beginning of execution of the program, such an operation from being included in the basic data.

[0250] [7] A condition monitoring device according to any one of [1] to [6], The number of basic data acquisitions is a value obtained by dividing the operating state time by the basic data upper limit length, rounding up any decimal places to an integer.

[0251] In this case, an appropriate number of basic data can be obtained from position time series data corresponding to one operating state time, depending on the situation. Even if the position time series data is long, basic data can be obtained to cover the entire data.

[0252] [8][7] A condition monitoring device comprising: The basic data processing unit extracts a plurality of the basic data from the position time series data so that the basic data overlap with each other.

[0253] By allowing partial overlap in extraction, a required number of basic data can be obtained from the position time series data.

[0254] [9] A condition monitoring device according to any one of [1] to [6], The number of basic data acquisitions is an integer obtained by rounding down the value obtained by dividing the operating state time by the basic data upper limit length, to the nearest integer.

[0255] In this case, an appropriate number of basic data can be obtained according to the situation from the position time-series data corresponding to one operating state time. Since multiple basic data can be extracted without overlapping with each other, substantial duplication of processing can be prevented.

[0256]

[10] A condition monitoring device according to any one of [1] to [6], The basic data processing unit obtains one piece of basic data from position time series data of each axis of the motion state of the robot corresponding to the motion state time, and stores the basic data.

[0257] In this case, since one basic data is obtained from the position time series data, the processing is simplified.

[0258]

[11]

[10] A condition monitoring device comprising: The basic data processing unit acquires one piece of basic data having a basic data upper limit length from the end of position time series data of each axis of the motion state of the robot corresponding to the motion state time.

[0259] This makes it possible to prevent, when the robot performs a positioning operation or the like at the beginning of execution of the program, such an operation from being included in the basic data.

[0260]

[12] A status monitoring device for monitoring a status of a robot capable of reproducing a predetermined motion, comprising: a time series data acquisition unit that acquires time series data of a status signal that reflects a status of the robot; a storage unit that stores the time series data acquired by the time series data acquisition unit; a lifespan estimation unit that acquires an evaluation value for evaluating the state of the robot based on the time-series data, creates a trend line that represents a tendency for the evaluation value to change over time, and determines a timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing; Equipped with when a motion of the robot that is a basis for acquiring the time-series data of the state signal is changed, the life estimation unit calculates the life threshold value to be applied to the changed motion; A condition monitoring device that creates a trend line based on a plurality of evaluation values ​​based on the changed operation, and defines the predicted life timing as the timing when the trend line reaches the life threshold applied to the changed operation.

[0261]

[13] A status monitoring device for monitoring a status of a robot capable of reproducing a predetermined motion, comprising: a time series data acquisition unit that acquires time series data of a status signal that reflects a status of the robot; a storage unit that stores the time series data acquired by the time series data acquisition unit; a lifespan estimation unit that acquires an evaluation value for evaluating the state of the robot based on the time-series data, creates a trend line that represents a tendency for the evaluation value to change over time, and determines a timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing; Equipped with When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit (1) determining the life threshold value to be applied to the changed operation, creating a trend line based on a plurality of the evaluation values ​​based on the changed operation, and determining the timing at which the trend line reaches the life threshold value to be applied to the changed operation; or (2) estimating an adjustment coefficient that expresses, as a ratio, the effect of a change in the robot's motion on the evaluation value, multiplying the evaluation value based on the changed motion by the adjustment coefficient to obtain an adjusted evaluation value, creating a trend line based on a plurality of the adjusted evaluation values ​​based on the changed motion, and predicting the timing at which the trend line reaches the life threshold applied to the motion before the change; A condition monitoring device in which the predicted life timing is set as the predicted life timing.

Claims

1. A state monitoring device that monitors a state of a robot capable of reproducing a predetermined motion, comprising: a time series data acquisition unit that acquires time series data of a status signal that reflects a status of the robot; a storage unit that stores the time series data acquired by the time series data acquisition unit; a lifespan estimation unit that acquires an evaluation value for evaluating the state of the robot based on the time-series data, creates a trend line that represents a tendency for the evaluation value to change over time, and determines a timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing; Equipped with When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit Calculate a past pre-increase estimated evaluation value for the post-change behavior, When a coefficient indicating how many times the life threshold applied to the pre-change behavior is larger than the evaluation value in the beginning based on the pre-change behavior is called a life threshold coefficient for the pre-change behavior, the past pre-increase estimated evaluation value for the post-change behavior is multiplied by the life threshold coefficient for the pre-change behavior to obtain a life threshold applied to the post-change behavior; A condition monitoring device that creates a trend line based on a plurality of evaluation values ​​based on the changed operation, and defines the predicted life timing as the timing when the trend line reaches the life threshold applied to the changed operation.

2. The condition monitoring device according to claim 1, When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit obtaining an increase ratio for the pre-change action by dividing the evaluation value of the final game based on the pre-change action by the evaluation value of the opening game based on the pre-change action; The status monitoring device obtains the past pre-increase estimated evaluation value for the post-change behavior by dividing the evaluation value at the beginning based on the post-change behavior by the increase ratio for the pre-change behavior.

3. The condition monitoring device according to claim 2, When the robot's motion, which is the basis for acquiring the time series data of the status signal, has been changed two or more times, the life estimation unit calculating an increase ratio for the most recent pre-change behavior by dividing the final evaluation value based on the most recent pre-change behavior by the past pre-increase estimated evaluation value for the most recent pre-change behavior; obtaining a pre-increase estimated evaluation value for the most recent changed behavior by dividing the evaluation value at the beginning based on the most recent changed behavior by the increase ratio for the most recent changed behavior; multiplying the past pre-increase estimated evaluation value for the most recent post-change operation by the life threshold coefficient for the first pre-change operation to obtain a life threshold to be applied to the most recent post-change operation; A condition monitoring device that creates a trend line based on a plurality of the evaluation values ​​based on the operation after the most recent change, and determines the predicted life timing as the timing when the trend line reaches the life threshold applied to the operation after the most recent change.

4. The condition monitoring device according to claim 2 or 3, A status monitoring device, wherein the final evaluation value based on the operation before the change is an evaluation value for a final time based on the operation before the change.

5. The condition monitoring device according to claim 2 or 3, A status monitoring device, wherein the final evaluation value based on the operation before the change is the median or average of multiple evaluation values ​​obtained most recently, including the final evaluation value based on the operation before the change.

6. The condition monitoring device according to claim 2 or 3, A status monitoring device, wherein the initial evaluation value based on the operation before the change is an initial evaluation value based on the operation before the change.

7. The condition monitoring device according to claim 2 or 3, A status monitoring device, wherein the early evaluation value based on the operation before the change is the median or average of multiple evaluation values ​​obtained near the initial time, including the initial evaluation value based on the operation before the change.

8. The condition monitoring device according to claim 1, When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit A trend line is created based on the plurality of evaluation values ​​based on the changed behavior, and a value indicated by the trend line at a timing when an elapsed time is zero is calculated as a past pre-increase estimated evaluation value for the changed behavior; When a coefficient indicating how many times the life threshold applied to the pre-change behavior is larger than the evaluation value in the beginning based on the pre-change behavior is called a life threshold coefficient for the pre-change behavior, the past pre-increase estimated evaluation value for the post-change behavior is multiplied by the life threshold coefficient for the pre-change behavior to obtain a life threshold applied to the post-change behavior; A condition monitoring device, wherein the predicted life timing is the timing when the trend line created based on a plurality of evaluation values ​​based on the changed operation reaches the life threshold applied to the changed operation.

9. The condition monitoring device according to claim 1, When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit The load based on the changed operation is divided by the load based on the previous operation to obtain the load correction factor. creating a trend line based on the plurality of evaluation values ​​based on the modified behavior; creating an auxiliary line having a slope obtained by dividing the slope of the trend line by the load correction factor and passing through the evaluation value at the beginning of the change, and calculating the value indicated by the auxiliary line at a timing when the elapsed time is zero as a past pre-increase estimated evaluation value for the operation after the change; A condition monitoring device, wherein the predicted life timing is the timing when the trend line created based on a plurality of evaluation values ​​based on the changed operation reaches the life threshold applied to the changed operation.

10. A state monitoring device that monitors a state of a robot capable of reproducing a predetermined motion, comprising: a time series data acquisition unit that acquires time series data of a status signal that reflects a status of the robot; a storage unit that stores the time series data acquired by the time series data acquisition unit; a lifespan estimation unit that acquires an evaluation value for evaluating the state of the robot based on the time-series data, creates a trend line that represents a tendency for the evaluation value to change over time, and determines a timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing; Equipped with When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit determining a lifespan threshold coefficient for the changed behavior, the lifespan threshold coefficient indicating how many times the evaluation value at the beginning of the changed behavior is based on the changed behavior; multiplying the evaluation value at the beginning based on the changed behavior by the life threshold coefficient for the changed behavior to obtain a life threshold to be applied to the changed behavior; A condition monitoring device that creates a trend line based on a plurality of evaluation values ​​based on the changed operation, and defines the predicted life timing as the timing when the trend line reaches the life threshold applied to the changed operation.

11. The condition monitoring device according to claim 10, When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit creating a trendline based on the plurality of evaluation values ​​based on the pre-change operation, and calculating a predicted life timing for the pre-change operation, the predicted life timing being the timing when the trendline reaches the life threshold applied to the pre-change operation; A remaining life rate for the operation before the change is obtained by dividing the time from the timing of the evaluation value at the end based on the operation before the change to the timing of the predicted life by the time from the timing of the evaluation value at the beginning based on the operation before the change to the timing of the predicted life; When the coefficient indicating how many times the life threshold applied to the operation before the change is compared to the initial evaluation value based on the operation before the change is called the life threshold coefficient for the operation before the change, a status monitoring device calculates the life threshold coefficient for the operation after the change by subtracting 1 from the life threshold coefficient for the operation before the change, multiplying the subtracted value by the remaining life rate for the operation before the change, and adding 1 to the multiplied value.

12. The condition monitoring device according to claim 11, When the robot's motion, which is the basis for acquiring the time series data of the status signal, has been changed N times or more (where N is an integer equal to or greater than 2), the life estimation unit For each of the first through Nth changes, creating a trendline based on the plurality of evaluation values ​​based on the operation immediately prior to the change; and calculating a predicted life timing for the operation immediately prior to the change, the predicted life timing being the timing when the trendline reaches the life threshold applied to the operation immediately prior to the change; A remaining life rate for the operation immediately before the change is obtained by dividing the time from the timing of the evaluation value at the end based on the operation immediately before the change to the timing of the predicted life by the time from the timing of the evaluation value at the beginning based on the operation immediately before the change to the timing of the predicted life; subtracting 1 from the life threshold coefficient for the operation before the first change, multiplying the subtracted value by the sum of the remaining life rates from the first change to the Nth change, and adding 1 to the multiplied value, thereby obtaining a life threshold coefficient for the operation after the Nth change; multiplying the evaluation value at the beginning based on the N-th changed motion by the life threshold coefficient for the N-th changed motion to obtain a life threshold to be applied to the N-th changed motion; A condition monitoring device that creates a trend line based on a plurality of evaluation values ​​based on the operation after the Nth change, and defines the predicted life timing as the timing when the trend line reaches the life threshold applied to the operation after the Nth change.

13. The condition monitoring device according to claim 10, When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit creating a trendline based on the plurality of evaluation values ​​based on the pre-change operation, and calculating a predicted life timing for the pre-change operation, the predicted life timing being the timing when the trendline reaches the life threshold applied to the pre-change operation; A remaining life rate for the operation before the change is obtained by dividing the time from the timing of the evaluation value at the end based on the operation before the change to the timing of the predicted life by the time from the timing of the evaluation value at the beginning based on the operation before the change to the timing of the predicted life; When a coefficient indicating how many times the life threshold applied to the operation before the change is compared to the initial evaluation value based on the operation before the change is called the life threshold coefficient for the operation before the change, a status monitoring device calculates a power value for the operation after the change using the life threshold coefficient calculated for the operation before the change as the base and the remaining life rate for the operation before the change as the exponent.

14. The condition monitoring device according to claim 13, When the robot's motion, which is the basis for acquiring the time series data of the status signal, has been changed N times or more (where N is an integer equal to or greater than 2), the life estimation unit For each of the first through Nth changes, creating a trendline based on the plurality of evaluation values ​​based on the operation immediately prior to the change; and calculating a predicted life timing for the operation immediately prior to the change, the predicted life timing being the timing when the trendline reaches the life threshold applied to the operation immediately prior to the change; A remaining life rate for the operation immediately before the change is obtained by dividing the time from the timing of the evaluation value at the end based on the operation immediately before the change to the timing of the predicted life by the time from the timing of the evaluation value at the beginning based on the operation immediately before the change to the timing of the predicted life; A power value having the life threshold coefficient for the operation before the first change as a base and the sum of the remaining life rates from the first to the Nth change as an exponent is calculated as a life threshold coefficient for the operation after the Nth change; multiplying the evaluation value at the beginning based on the N-th changed motion by the life threshold coefficient for the N-th changed motion to obtain a life threshold to be applied to the N-th changed motion; A condition monitoring device that creates a trend line based on a plurality of evaluation values ​​based on the operation after the Nth change, and defines the predicted life timing as the timing when the trend line reaches the life threshold applied to the operation after the Nth change.

15. The condition monitoring device according to claim 10, When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit When the coefficient indicating how many times the life threshold applied to the operation before the change is greater than the initial evaluation value based on the operation before the change is called the life threshold coefficient for the operation before the change, the status monitoring device calculates the life threshold coefficient for the operation after the change by dividing the life threshold for the operation before the change by the final evaluation value based on the operation before the change, or by the value indicated by a trend line based on multiple evaluation values ​​based on the operation before the change at the timing of the final evaluation value.

16. The condition monitoring device according to claim 10, When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit estimating an adjustment coefficient that represents a ratio of an effect of a change in the robot's movement on the evaluation value; A condition monitoring device that obtains a life threshold for the changed operation by dividing the life threshold applied to the operation before the change by the adjustment coefficient.

17. A state monitoring device that monitors a state of a robot capable of reproducing a predetermined motion, comprising: a time series data acquisition unit that acquires time series data of a status signal that reflects a status of the robot; a storage unit that stores the time series data acquired by the time series data acquisition unit; a lifespan estimation unit that acquires an evaluation value for evaluating the state of the robot based on the time-series data, creates a trend line that represents a tendency for the evaluation value to change over time, and determines a timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing; Equipped with When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit estimating an adjustment coefficient that represents a ratio of an effect of a change in the robot's movement on the evaluation value; creating a trend line based on an adjusted evaluation value obtained by multiplying the evaluation value based on the changed behavior by the adjustment coefficient; A condition monitoring device that determines the predicted life timing as the timing at which the trend line reaches the life threshold applied to the operation before the change.

18. The condition monitoring device according to claim 17, When the operation of the robot that is the basis for acquiring the time series data of the status signal is changed, the life estimation unit A status monitoring device that creates the trend line based on the evaluation value based on the operation before the change and an adjusted evaluation value obtained by multiplying the evaluation value based on the operation after the change by the adjustment coefficient.

19. 19. A condition monitoring device according to claim 17 or 18, The life estimation unit determines the adjustment coefficient by dividing the evaluation value at the end based on the operation before the change by the evaluation value at the beginning based on the reference pattern after the change.

20. A condition monitoring device according to any one of claims 16 to 18, The life estimation unit is creating a trend line based on the plurality of evaluation values ​​based on the behavior before the change, and determining a trend line value that is a value indicated by the trend line at a timing of the evaluation value at the beginning of the behavior after the change; The condition monitoring device calculates the adjustment coefficient by dividing the trend line value by the evaluation value at the beginning based on the changed operation.

21. A condition monitoring device according to any one of claims 16 to 18, The life estimation unit is creating a first trend line based on the plurality of evaluation values ​​based on the motion before the change, and determining a first trend line value that is a value indicated by the first trend line at a timing of the motion change of the robot; creating a second trend line based on the plurality of evaluation values ​​based on the changed motion, and determining a second trend line value that is a value indicated by the second trend line at the timing of the change in the motion of the robot; The condition monitoring device determines the adjustment factor by dividing the first trend line value by the second trend line value.

22. The condition monitoring device according to claim 17, When the robot's motion, which is the basis for acquiring the time series data of the status signal, has been changed two or more times, the life estimation unit estimating the adjustment coefficient for each change in the robot's motion; an adjusted evaluation value obtained by multiplying the evaluation value based on the most recent pre-change behavior by the adjustment coefficient before the most recent change; and an adjusted evaluation value obtained by multiplying the evaluation value based on the most recently changed behavior by the adjustment coefficient after the most recently changed behavior; and The condition monitoring device creates the trend line based on the above.

23. 23. A condition monitoring device according to claim 22, When the robot's motion, which is the basis for acquiring the time series data of the status signal, has been changed two or more times, the life estimation unit A final adjustment evaluation value is obtained by multiplying the evaluation value of the final game based on the operation before the most recent change by the adjustment coefficient before the most recent change; The state monitoring device determines the adjustment coefficient after the most recent change by dividing the final adjustment evaluation value by an evaluation value at an early stage based on the reference pattern after the most recent change.

24. 23. A condition monitoring device according to claim 22, When the robot's motion, which is the basis for acquiring the time series data of the status signal, has been changed two or more times, the life estimation unit multiplying the plurality of evaluation values ​​based on the behavior before the most recent change by the adjustment coefficient before the most recent change to obtain the plurality of adjusted evaluation values ​​before the most recent change; creating a trend line based on the plurality of adjusted evaluation values ​​before the most recent change, and determining a trend line value that is a value that the trend line indicates at a timing of the evaluation value at the beginning based on the behavior after the most recent change; The condition monitoring device determines the adjustment coefficient after the most recent change by dividing the trend line value by the evaluation value at an early stage based on the operation after the most recent change.

25. 23. A condition monitoring device according to claim 22, When the robot's motion, which is the basis for acquiring the time series data of the status signal, has been changed two or more times, the life estimation unit multiplying the plurality of evaluation values ​​based on the behavior before the most recent change by the adjustment coefficient before the most recent change to obtain the plurality of adjusted evaluation values ​​before the most recent change; creating a first trend line based on the plurality of adjustment evaluation values ​​before the most recent change, and determining a first trend line value that is a value indicated by the first trend line at the timing of the most recent operation change of the robot; creating a second trend line based on the plurality of evaluation values ​​based on the most recent changed motion, and determining a second trend line value that is a value indicated by the second trend line at the timing of the change in the motion of the robot; The condition monitoring device determines the most recently changed adjustment factor by dividing the first trend line value by the second trend line value.

26. A condition monitoring device according to any one of claims 1 to 3, claims 8 to 18, and claims 22 to 25, the life estimation unit resets the elapsed time to zero when an elapsed time reset signal indicating that a part of the robot has been replaced is input; The robot's motion, which is the basis for acquiring the time-series data of the status signal, is the same before and after the elapsed time is reset; multiplying the evaluation value at the beginning based on the operation after the elapsed time is reset to zero by a predetermined life threshold coefficient to obtain the life threshold after reset; A condition monitoring device that creates a trend line based on a plurality of evaluation values ​​based on operation after an elapsed time is reset to zero, and determines the timing at which the trend line reaches the life threshold value after the reset as the predicted life timing.

27. A state monitoring method for monitoring a state of a robot capable of reproducing a predetermined motion, comprising the steps of: Acquire time series data of a state signal that reflects the state of the robot; storing the acquired time series data; obtaining an evaluation value for evaluating the state of the robot based on the time-series data, creating a trend line that represents a tendency for the evaluation value to change over time, and determining a timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing; When the operation of the robot that is the basis for acquiring the time series data of the state signal is changed, Calculate a past pre-increase estimated evaluation value for the post-change behavior, When a coefficient indicating how many times the life threshold applied to the pre-change behavior is larger than the evaluation value in the beginning based on the pre-change behavior is called a life threshold coefficient for the pre-change behavior, the past pre-increase estimated evaluation value for the post-change behavior is multiplied by the life threshold coefficient for the pre-change behavior to obtain a life threshold applied to the post-change behavior; A condition monitoring method comprising: creating a trend line based on a plurality of evaluation values ​​based on the changed operation; and determining the predicted life timing as the timing when the trend line reaches the life threshold applied to the changed operation.

28. A status monitoring program for monitoring a status of a robot capable of reproducing a predetermined motion, a time series data acquisition step of acquiring time series data of a state signal reflecting a state of the robot; a storage step of storing the time series data acquired in the time series data acquisition step; a lifespan estimation step of acquiring an evaluation value for evaluating the state of the robot based on the time-series data, creating a trend line that represents a tendency for the evaluation value to change over time, and determining a timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing; Run the following on your computer: When the operation of the robot that is the basis for acquiring the time series data of the state signal is changed, in the life estimation step, Calculate a past pre-increase estimated evaluation value for the post-change behavior, When a coefficient indicating how many times the life threshold applied to the pre-change behavior is larger than the evaluation value in the beginning based on the pre-change behavior is called a life threshold coefficient for the pre-change behavior, the past pre-increase estimated evaluation value for the post-change behavior is multiplied by the life threshold coefficient for the pre-change behavior to obtain a life threshold applied to the post-change behavior; A status monitoring program that creates a trend line based on a plurality of evaluation values ​​based on the changed operation, and defines the predicted life timing as the timing when the trend line reaches the life threshold applied to the changed operation.

Images