State monitoring device, state monitoring method, and state monitoring program

Abstract

In the present invention, time-series data of a state signal is acquired by an operation of a robot, and evaluation values are calculated. A lifetime estimation unit obtains, as a predicted lifetime timing, a timing at which a trend line created from a plurality of the evaluation values reaches a lifetime threshold. When the operation of the robot is changed, a past pre-increase estimation evaluation value for an operation after the change is obtained. A coefficient indicating how many times larger the lifetime threshold applied to the operation before the change is than an early-stage evaluation value based on the operation before the change is called a lifetime threshold coefficient. By multiplying the past pre-increase estimation evaluation value by the lifetime threshold coefficient before the change, a lifetime threshold applied to the operation after the change is obtained. A timing at which a trend line created from a plurality of evaluation values based on the operation after the change reaches the lifetime threshold applied to the operation after the change is set as a predicted lifetime timing.

Description

Condition monitoring device, condition monitoring method, and condition monitoring program

[0001] The present disclosure relates primarily to lifespan prediction for robots.

[0002] When industrial robots are repeatedly operated in factories, it is inevitable that the various parts of the robot (e.g., mechanical components) will deteriorate. As this condition progresses, the robot will eventually break down. Since a robot breakdown causing a long-term shutdown of the line would result in significant losses, there is a strong demand for carrying out maintenance (preservation) before a robot breaks down. However, carrying out frequent maintenance is difficult due to factors such as maintenance costs.

[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 to determine the period until the current command value reaches a predetermined value based on the diagnosed change trend.

[0005] JP 2016-117148 A

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

[0007] One approach is to pre-program the robot to perform specific operations for data collection. The robot executes the program periodically or irregularly, allowing it to reproduce a specific motion pattern for evaluation. However, this approach requires the robot to perform special operations that differ from the actual work, which inevitably reduces operational efficiency.

[0008] Data for predicting remaining life can be collected while the robot is running a program for actual work. However, it is possible that the operation program used for actual work may change while the robot is in operation. If the operation 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 detect signs of robot failure and remaining lifespan even when the operation of the robot used for evaluation is changed.

[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, there is provided a condition monitoring device having the following configuration. Specifically, the condition monitoring device monitors the condition of an industrial robot capable of reproducing predetermined operations. The condition monitoring device includes a time-series data acquisition unit, a storage unit, and a lifespan estimation unit. The time-series data acquisition unit acquires time-series data of a status signal reflecting the state of the robot. The storage unit stores the time-series data acquired by the time-series data acquisition unit. The lifespan estimation unit acquires an evaluation value for evaluating the state of the robot based on the time-series data and creates a trend line representing the tendency of the evaluation value to change over time. The lifespan estimation unit determines the timing at which the trend line reaches a predetermined lifespan threshold as the predicted lifespan timing. When the robot's operation, which is the basis for acquiring the time-series data of the status signal, is changed, the lifespan estimation unit calculates a past pre-increase estimated evaluation value for the changed operation. Here, a coefficient indicating how many times the lifespan threshold applied to the operation before the change is multiplied by the evaluation value at the beginning based on the operation before the change is referred to as a lifespan threshold coefficient for the operation before the change. The lifespan estimation unit calculates a lifespan threshold to be applied to the changed operation by multiplying the past pre-increase estimated evaluation value for the changed operation by the lifespan threshold coefficient for the changed operation. The lifespan estimation unit creates a trend line based on the multiple evaluation values ​​based on the changed operation, and determines the timing when the trend line reaches the lifespan threshold to be applied to the changed operation as the predicted lifespan timing.

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

[0013] According to a third aspect of the present disclosure, there is provided a status monitoring program configured as follows for monitoring the status of an industrial robot capable of reproducing predetermined operations. Specifically, this status 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 status signal reflecting the status 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 status of the robot is acquired based on the time-series data, a trend line is created that represents the tendency of the evaluation value to change over time, and the timing at which the trend line reaches a predetermined lifespan threshold is determined as a predicted lifespan timing. When a robot operation that is the basis for acquiring the time-series data of the status signal is changed, the lifespan estimation step determines the predicted lifespan timing as follows: That is, a past pre-increase estimated evaluation value for the changed operation is determined. Here, a coefficient indicating how many times the lifespan threshold applied to the operation before the change is multiplied by the evaluation value at the beginning based on the operation before the change is referred to as a lifespan threshold coefficient for the operation before the change. A lifespan threshold to be applied to the changed operation is calculated by multiplying the past pre-increase estimated evaluation value for the changed operation by the lifespan threshold coefficient for the changed operation. 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 lifespan threshold to be applied to the changed operation is defined as the predicted lifespan timing.

[0014] This allows for consistent lifespan prediction even when the robot's behavior, which is the basis for the evaluation value, is changed by consistently treating the evaluation values ​​before and after the change. Therefore, it is possible to obtain time-series status data and evaluation values ​​based on the robot's actual behavior, thereby improving the robot's operating efficiency. Using the past estimated evaluation value before the increase allows for a simple calculation to obtain a lifespan threshold based on the changed behavior.

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

[0016] 1 is a perspective view showing a configuration of a robot according to an embodiment of the present disclosure. FIG. 1 is a block diagram schematically showing the electrical configuration of a robot and a status monitoring device. FIG. 2 is a graph showing an example of time series data of current values. FIG. 3 is a graph illustrating the transition of an evaluation value obtained from the time series data of current values ​​and a trend line. FIG. 4 is a flowchart illustrating a process of acquiring a reference pattern from position time series data. FIG. 5 is a graph illustrating a process of obtaining a reference pattern by extracting a time interval from position time series data. FIG. 6 is a flowchart mainly illustrating a pattern matching process of searching for a reference pattern from position time series data. FIG. 7 is a schematic diagram illustrating the pattern matching process. FIG. 8 is a schematic diagram illustrating the average error and maximum error of position time series data in a time interval in which a reference pattern is matched. FIG. 9 is a graph showing an example of time series data of current values ​​that has changed as the robot's motion pattern is changed. FIG. 10 is a graph illustrating a first method of consistently processing evaluation values ​​before and after a change in the robot's motion pattern. FIG. 11 is a graph illustrating a process when the robot's motion pattern is changed twice in the first method. FIG. 12 is a graph illustrating a second method. FIG. 13 is a graph illustrating a third method. FIG. 14 is a graph illustrating a fourth method. FIG. 15 is a graph illustrating a fifth method. FIG. 16 is a graph illustrating a sixth method. FIG. 17 is a graph illustrating a seventh method. FIG. 18 is a graph illustrating an eighth method. Graphs illustrating the ninth method, the tenth method, the eleventh method, and the twelfth method.

[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 the electrical configuration of the robot 1 and a status monitoring device 5. Fig. 3 is a graph showing an example of time-series data of current values. Fig. 4 is a graph illustrating the transition of evaluation values ​​and trend lines obtained from the time-series data of current values.

[0018] A condition 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. 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] The driving force from each servo motor constituting the arm driving device 21 is transmitted via a reducer to each joint of the articulated arm 11, the swivel 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 reproducing the motions recorded through instruction. 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 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, ROM, RAM, an auxiliary storage device, etc. The auxiliary storage device is configured as, for example, an HDD, an SSD, etc. The auxiliary storage device stores programs for operating the robot 1, etc.

[0027] 1, the condition monitoring device 5 is connected to the controller 90. The condition monitoring device 5 acquires, via the controller 90, the transition of the current value of the current flowing through the actuator (servomotor), etc.

[0028] If an abnormality occurs in the servo motor or the reducer connected to it, the current value of the servo motor is thought to fluctuate due to the influence. Therefore, this current value 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 status monitoring device 5 can determine whether or not there is an abnormality in the robot 1 by monitoring the acquired status time-series data. In this embodiment, the status monitoring device 5 determines whether or not there is an abnormality, mainly targeting 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 motor, reducer, or bearing.

[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, ROM, 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. This realizes the condition monitoring method of the present disclosure. Cooperation of these hardware and software allows the computer to operate 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 refers to a measurement value obtained by measuring the magnitude of the current flowing through the servo motor using 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, the current command value given to the servo motor by the servo driver may be used as the status signal. The servo driver performs feedback control on the servo motor so that the current current value approaches the current command value. Therefore, for the purpose of detecting abnormalities in the servo motor or 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 status signal may be 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. Typically, the servo driver multiplies this deviation by a gain and provides the result to the servo motor as a current command value. Therefore, the transition of the rotational position deviation shows a similar trend to the transition of the current command value.

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

[0037] In this embodiment, the movement patterns for which the time-series data acquisition unit 51 acquires state time-series data are movement patterns that have been taught to the robot 1 to perform actual work. In other words, the robot 1 does not operate according to any special movement 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 at which the acquisition start signal is received and the timing at which the acquisition end signal is received. The acquisition start signal and the acquisition end signal are output by, for example, the controller 90.

[0039] The graph in Figure 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 Figure 3, the vertical axis represents the current value and the horizontal axis represents time. As shown in Figure 3, before the reproduction movement program 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 articulated arm 11, etc. is maintained.

[0040] Next, the playback operation program for the robot 1 is started. This releases the brake, and almost simultaneously, 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 condition 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 are all completed, the servo motor is controlled to stop rotating. After the servo motor stops rotating, but before the program ends, the controller 90 outputs an acquisition end signal to the time-series data acquisition unit 51.

[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, etc.

[0044] In this embodiment, the time series data is a chronological arrangement of a large number of current values ​​obtained by repeatedly detecting the current at short, fixed time intervals. 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 timestamp indicating the date and time when the acquisition start signal was received.

[0046] Similarly, the storage unit 52 stores playback identification information indicating the movement pattern being performed by the robot 1 when the time-series data of the current values ​​was acquired, 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 program name assigned to uniquely identify the playback movement program.

[0047] The storage of time-series data will now be described in detail. Using the file system of the OS provided in the status monitoring device 5, which is a computer, a folder named after the program number or program name is created in the auxiliary storage device described above. A file of status time-series data obtained when the program with that number is executed is automatically saved in this folder. In this file, time-series data regarding the current values ​​of the servo motors of the six joints is written, for example, in comma-separated value format (CSV). The file name includes a timestamp string. In this way, the association between the timing information and the reproduction identification information for the status 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 the 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. Like the state time series data, the position time series data can be written in, for example, CSV format. The state time series data and the position time series data may be saved in a single 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 the acquisition start signal to the 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 the 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 thereof will be omitted.

[0051] The frequency analysis integrated value is obtained by performing frequency analysis on time-series data of current values ​​and integrating components from 0 Hz to several tens of Hz, for example, for the amplitude spectrum obtained. Because 0 Hz represents a DC component, the integrated value may be obtained by integrating components from several Hz to several tens of Hz, excluding DC. Frequency analysis can be performed using a known method, typically a fast Fourier transform. Using the frequency analysis integrated value may be useful in determining whether or not the robot 1 exhibits signs of failure, such as a tendency for vibration. Various causes of a vibration tendency can be considered, including, for example, increased lost motion due to wear in the reducer. Instead of the amplitude spectrum, a power spectrum or power spectral density may also be used for integration. The amplitude spectrum, power spectrum, and power spectral density are all types of frequency spectrum.

[0052] Assuming that the natural frequency of the robot 1 system is 8 Hz, the 8 Hz component in the amplitude spectrum will become large due to resonance. In this regard, the frequency analysis integrated value of this embodiment uses, for example, a value obtained by integrating components from 0 Hz to several tens of Hz for evaluation. Over time, vibrations due to the deterioration of various parts often occur in addition to the original main mode vibration. By observing a wide range of frequencies, the detection range for signs of failure 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 the current value of a low peak from the current value of a high peak of a current waveform. Both peak and PTP are types of "peak current" as defined in Patent Document 1. The evaluation value can also be the dissimilarity obtained by comparing the current waveform obtained this time with the current waveform at the start of condition monitoring. The similarity can be calculated by any known method, and for example, the sum or average of Euclidean distances can be used. The DTW distance, etc. can also be used as the similarity.

[0054] The lifespan estimation unit 54 can predict the timing at which each joint of the robot 1 will malfunction or become inoperable 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 lifespan timing.

[0055] The graph in Fig. 4 shows an example of the state of the robot 1 approximately 70 days after the start of monitoring the state 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 lifespan estimation unit 54 creates a trend line TL1 that represents the increasing trend of I2 over time. This trend line TL1 can be obtained, for example, by the well-known least squares method. A lifespan threshold TH1 is set in advance for I2. The point in time at which the obtained trend line TL1 reaches the lifespan threshold TH1 indicates the timing at which the joint is predicted to malfunction or become inoperable. Hereinafter, the predicted lifespan timing may be referred to as the predicted lifespan timing PL1. Similarly, the lifespan timing can be predicted for the frequency analysis integrated value by creating a trend line and determining the timing at which the frequency analysis integrated value reaches the lifespan threshold. After determining the predicted lifespan timing for both I2 and the frequency analysis integrated value, the lifespan estimation unit 54 determines whichever timing occurs first as the overall predicted lifespan timing.

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

[0058] The operator monitors the graphs of I2 and the frequency analysis integrated value to see if there are any unusual points that deviate from the usual trends. 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 the evaluation interval. As a prerequisite 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 interval from the position time-series data acquired when the status monitoring device 5 first starts monitoring.

[0063] In some teaching programs, the last teaching point of the previously played program is always aligned with the first teaching point of the next program to be played, for example by defining a home position. In other teaching programs, the last teaching point of the previously played program is allowed to differ from the first teaching point of the next program to be played. In such cases, the next program is played back with a movement between these two points. Because it may not be appropriate to include this positioning operation in the reference pattern, the reference pattern may be created from only 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] To properly perform pattern matching, a lower limit length and an upper limit length are predetermined for the reference pattern, for example, but not limited to, 10 seconds and 60 seconds.

[0065] After creating the reference pattern, the reference pattern creating unit 61 adds identification information that uniquely identifies the reference pattern and stores it 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, and identifies a time interval in which the reference pattern matches the position time series data as an evaluation interval.

[0067] The evaluation value calculation unit 63 calculates an evaluation value for the evaluation section identified 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 by, for example, 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 these.

[0069] The operation change monitoring unit 64 monitors whether a program containing a registered operation pattern of the robot 1 has been changed and is no longer in use, based on the results of the search processing unit 62 searching for a reference pattern from the position time series data.

[0070] Next, the extraction of the 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 the process of acquiring the reference pattern from the position time series data.

[0071] The reference pattern creation unit 61 first determines whether the robot 1 is in an operating state or an inactive 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 rotational position and the horizontal axis represents 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 inactive state refers to a state in which none of the six axes are moving. The operating or inactive state can be determined based on changes in the rotational position of each axis. The bottom of the graph in Figure 6 shows the operating / inactive state determination results at each point in time.

[0073] Normally, the robot 1 is in a non-operating state from the start of program execution until the aforementioned electromagnetic brake is released and the servo motor starts rotating. The robot 1 also becomes 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 six joint axes (in other words, 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 portions 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 a reference pattern that does not include the non-motion state portions.

[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 applied from the electrodes of the gun disposed at the tip of the robot 1. To prevent such a situation from being determined as an inactive state, it is preferable that in step S101, the robot 1 is determined to be inactive 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 prevents 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 motion section in the position time-series data as a candidate section for extracting a reference pattern (step S102). A continuous motion section is a section in which the above-mentioned motion state continues over time. Figure 6 shows the identified candidate section.

[0079] Next, the reference pattern creation unit 61 determines whether the temporal length of the candidate section is equal to or greater than the minimum length (step S103). If the temporal length of the candidate section is less than the minimum length, the process is terminated because it is not possible to extract a reference pattern of an appropriate length from the candidate section.

[0080] If the determination in step S103 is that the temporal length of the candidate section is equal to or greater than the lower limit, the reference pattern creation unit 61 divides the temporal length of the candidate section by the upper limit to obtain a ratio (step S104). Next, the reference pattern creation unit 61 rounds up the ratio to obtain an integer, and determines the resulting integer 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 is approximately 1.8, so the extraction number is 2.

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

[0082] If 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 consecutive time intervals from the candidate interval. By differentiating the start and end times of the time intervals extracted from the candidate interval, multiple reference patterns can be obtained from one candidate interval. In FIG. 6 , two reference patterns obtained from the candidate interval are shown enclosed in rectangles. For each reference pattern, the length of the extracted time interval matches the upper limit length. Multiple reference patterns are extracted from the candidate interval so that every 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 extracted multiple time intervals be evenly spaced within the candidate interval. When the length of the candidate interval is shorter than the upper limit length multiplied by the number of extractions, the multiple reference patterns are extracted so that they partially overlap each other. In the example of FIG. 6 , the two acquired reference patterns partially overlap.

[0084] 6, for the sake of simplicity, the upper limit length of the reference pattern is set to a certain extent. By setting the lower and upper limit lengths of the reference pattern to be sufficiently small, it is possible to obtain many reference patterns from one piece of position time-series data.

[0085] The reference pattern can be determined by the above series of processes. After obtaining a certain number of reference patterns as candidates, a selection process may be performed to eliminate inappropriate patterns, and the resulting pattern may be adopted as the reference pattern. For example, it is possible to eliminate patterns in which the change in one or more axes among the multiple axes is always zero or less than a predetermined amount throughout the entire time period of the pattern. Reference patterns in which the load applied to each joint of the robot 1 is light or the change in load is less than a predetermined amount may also be eliminated. The load can be evaluated using, for example, the aforementioned 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 extract from the candidate section. In this case, multiple reference patterns may be extracted from one candidate section without overlapping as described above. Rounding may be performed, for example, to the nearest whole number, or rounding down may be performed depending on the value after the decimal point.

[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 the extraction of the reference pattern 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 calculates the aforementioned evaluation value (i.e., I2 and the frequency analysis integrated value). This evaluation value is treated as the initial evaluation value. If there are multiple reference patterns, an evaluation value is calculated for each reference pattern.

[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] The robot 1 continues to operate in accordance with the program even after the reference pattern created by the reference pattern creating unit 61 is stored in the memory unit 52. At appropriate times, the time-series data acquiring 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] The search processing unit 62 included in the time-series data evaluation unit 53 performs pattern matching processing to search for a reference pattern from the obtained position time-series data.

[0093] When the flow shown in Figure 7 starts, the search processing unit 62 selects one reference pattern as the search target and appropriately determines the number of slides for the search, taking into account the length of the position time series data and the length of the reference pattern (step S201). As shown simply in Figure 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 along the time axis relative to the position time series data. If the number of slides increases, the amount of slide along the time axis per slide (slide amount S1 in Figure 8) decreases, thereby improving matching accuracy but increasing the calculation load. It is preferable to determine the number of slides taking these factors into consideration.

[0094] Next, the search processing unit 62 calculates the dissimilarity between the reference pattern and the position time-series data for each axis of the robot 1 while sliding the reference pattern along the time axis relative to the position time-series data (step S202). FIG. 8 illustrates an example of pattern matching for the position time-series data of one axis. The amount of sliding S1 of the reference pattern per time is calculated 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 position) 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 and 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 principle, 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 factors such as aging and noise, 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 below), the reference pattern can be flexibly matched to such position time series data.

[0096] The processing of step S203 obtains the position on the time axis at which the reference pattern best matches 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 obtains the median 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. Any method can be used to determine the representative match position; for example, the average value of the best match positions may be used instead of the median value.

[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 M between the position time-series data and the reference pattern at the representative match position for each axis of the robot 1, for the reference pattern matching section (step S206). This maximum error is the maximum absolute value of the difference in rotational position at each sampling timing for the reference pattern matching section. The formula for the maximum error M is shown in FIG. 9.

[0099] Next, the search processing unit 62 calculates the 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 E / A and M / A values ​​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 match section and the reference pattern, relative to the change in rotational position. For example, due to spike noise or other reasons, the difference in rotational position compared to the reference pattern may become extremely large in an extremely short time region of the position time series data in the reference pattern match section. If 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 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 for example, the first threshold value can be set to 3% and the second threshold value to 5%.

[0102] If 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 identifies the reference pattern matching section of the position time-series data as the evaluation section (step S211).Then, the evaluation value calculation unit 63 calculates the evaluation value for the evaluation section (step S212).

[0103] Furthermore, the search processing unit 62 stores in the storage unit 52 the fact 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, and therefore 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 of the reference patterns stored in the storage unit 52. When the process has been completed for all of the reference patterns, the series of processes shown in FIG.

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

[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 robot 1 described with reference to Figure 3 is no longer used for some reason, or where its frequency of use drops significantly. This situation occurs, for example, in the case of robots used in the automobile industry, when the model of the car being manufactured changes, or when the monthly production schedule for the main vehicle model changes in a mixed production line. In this case, 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 Figure 10.

[0109] A change in the movement pattern of the robot 1 is synonymous with a change in the program of the playback movement. When the robot 1 performs a new movement corresponding to Figure 10, the playback identification information is stored in the storage unit 52 in association with the time-series data, as in the case of Figure 3. As the program of the playback movement is changed, the playback identification information is also changed. Therefore, the CSV files for the movement in Figure 10 and the movement in Figure 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 determines whether the reference pattern stored in the storage unit 52 continues not to appear with sufficient frequency in the position time-series data. There are various methods for this determination, but it is possible to compare the count value of the number of consecutive days during which the reference pattern does not appear in the position time-series data with a threshold, or to compare the count value of appearances over N days with a threshold, for example.

[0111] If it is determined that the situation in which the stored reference pattern does not appear with sufficient frequency in the position time-series data continues, the operation change monitoring unit 64 deletes the reference pattern from the storage unit 52. When the reference pattern creation unit 61 detects this, it automatically performs the process shown in Fig. 5 to create a reference pattern from the position time-series data at that time and stores 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 switched 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 changing the robot's motion pattern will be described.

[0114] When the motion pattern of the robot 1 is changed, the load conditions of the servo motors and reducers of the six joints change. 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 or for calculating the remaining lifespan.

[0115] When the motion pattern of the robot 1 is changed, the reference pattern is also automatically changed, except in special cases. Figure 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, using one of the following 12 methods.

[0116] The first method is described below with reference to FIG.

[0117] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold TH1 for the initial reference pattern is the value obtained by multiplying the initial evaluation value p1 of the initial reference pattern by the life threshold coefficient k1 (TH1 = p1 × k1). The life threshold coefficient k1 is determined appropriately, 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 the increase ratio ir1 (ir1 = p2 / p1). [c] The initial evaluation value q1 based on the post-change reference pattern is divided by the increase ratio ir1 to obtain the past pre-increase estimated evaluation value s1 (s1 = q1 / ir1). [d] The past pre-increase estimated evaluation value s1 is multiplied by the life threshold coefficient k1 to obtain the life threshold TH2 based on the post-change reference pattern (TH2 = s1 × k1). [e] A trend line TL2 is created based on multiple evaluation values ​​based on the post-change reference pattern. The timing at which this trend line TL2 reaches the life threshold value TH2 applied to the changed reference pattern is set as the 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 operation of the first reference pattern is multiplied by the initial evaluation value p1 based on the operation of the first reference pattern. Given a life threshold TH1, 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 and subsequent methods described below.

[0119] In the first method, if the reference pattern is changed multiple times, steps [b] through [e] can be performed for each change. Figure 12 illustrates a case in which the robot 1's motion has been changed twice. However, from the second change onward, the increase ratio ir2 of [b] is calculated by dividing the final evaluation value q2 based on the reference pattern before the change by the previous pre-change estimated evaluation value s1 calculated for the reference pattern before the change (ir2 = q2 / s1). In Figure 12, if the initial evaluation value based on the reference pattern after the second change is r1, the previous pre-change estimated evaluation value s2 based on the reference pattern after the second change is calculated using the formula s2 = r1 / ir2. The lifespan threshold TH3 applied to the reference pattern after the second change is calculated by multiplying the previous pre-change estimated evaluation value s2 by the lifespan threshold coefficient k1 (TH3 = s2·k1). The lifespan threshold coefficient k1 can be used in common regardless of the number of motion changes.

[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 in [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 final evaluation value based on one reference pattern. The representative value is not limited to the median or the average.

[0123] Instead of the initial evaluation value of [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 early evaluation value 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 that can reproduce predetermined actions. 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 that reflects 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 represents the tendency for the evaluation value to change over time, and determines the timing at which the trend line TL1 reaches a predetermined lifespan threshold TH1 as the predicted lifespan timing PL1.

[0125] When the robot's motion, which is the basis for acquiring state time-series data, is changed as shown in FIG. 11 , the predicted lifespan timing PL2 is calculated as follows. That is, the lifespan estimation unit 54 calculates the past pre-increase estimated evaluation value s1 for the changed motion. Here, a coefficient indicating how many times the lifespan threshold TH1 applied to the motion before the change is multiplied by the evaluation value p1 at the beginning based on the motion before the change is referred to as the lifespan threshold coefficient k1 for the motion before the change. The lifespan estimation unit 54 calculates the lifespan 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 lifespan threshold coefficient k1 for the motion before the change. The lifespan estimation unit 54 creates a trend line TL2 based on multiple evaluation values ​​based on the changed motion, and determines the timing at which the trend line TL2 reaches the lifespan threshold TH2 applied to the changed motion as the predicted lifespan timing PL2.

[0126] As a result, even if the behavior of the robot 1 that is the basis for the evaluation value is changed, the evaluation values ​​before and after the change can be handled consistently, allowing for consistent lifespan prediction. Therefore, it is 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 estimated evaluation value before increase 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 behavior by dividing the final evaluation value p2 based on the pre-change behavior by the early evaluation value p1 based on the pre-change behavior. The lifespan estimation unit 54 obtains a past pre-increase estimated evaluation value s1 for the post-change behavior by dividing the early evaluation value q1 based on the post-change behavior by the increase ratio ir1 for the pre-change behavior.

[0128] As a result, by using the increase ratio and the past pre-increase estimated evaluation value, the life threshold value TH2 based on the changed operation can be obtained by simple calculation.

[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 changed two or more times as shown in Figure 12, the predicted lifespan timing is calculated as follows. That is, the lifespan estimation unit 54 calculates the increase ratio ir2 for the motion before the most recent change by dividing the final evaluation value q2 based on the motion before the most recent change by the past pre-ascension estimated evaluation value s1 for the motion before the most recent change. Except for the above, the calculation of the predicted lifespan timing is performed in the same way as for the first change in the motion of the robot 1. Regardless of the number of changes, the lifespan threshold coefficient k1 for the motion before the first change is commonly used as the lifespan threshold coefficient.

[0130] This allows the influence of multiple changes in the robot 1's behavior on the evaluation value to be estimated with high accuracy.

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

[0132] This simplifies the calculation.

[0133] In the condition 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 the 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 at the beginning based on the changed operation is the initial evaluation value q1 based on the changed operation.

[0136] This simplifies the calculation.

[0137] In the condition monitoring device 5 of this embodiment, the initial evaluation value based on the operation before the change can also be a representative value such as the median or average value 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 is described below with reference to FIG.

[0140] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold TH1 for the initial reference pattern is the product of the initial evaluation value p1 of the initial reference pattern multiplied by the life threshold coefficient k1 (TH1 = p1 * k1). [b] A trend line TL1 is created based on multiple evaluation values ​​based on the pre-change reference pattern, and the timing at which this trend line TL1 reaches the life threshold 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 pre-change reference pattern to the predicted life timing PL1 by the time from the timing tp1 of the initial evaluation value p1 based on the pre-change reference pattern to the predicted life timing 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 resulting value is multiplied by the remaining life rate tr1, and then 1 is added to obtain the life threshold coefficient k2 of the changed reference pattern: 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 multiple evaluation values ​​based on the changed reference pattern. The timing when this trend line TL2 reaches the life threshold TH2 to be applied to the changed reference pattern is defined as the predicted life timing PL2.

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

[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 is calculated by subtracting 1 from the life threshold coefficient k1 calculated for the first reference pattern, multiplying the result by the remaining life rates tr1, tr2, ..., trN calculated for the first to Nth reference patterns, 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 lifespan timing is determined as follows. That is, the lifespan estimation unit 54 determines a lifespan threshold coefficient k2 for the changed motion, which indicates how many times the lifespan threshold TH2 applied to the changed motion is multiplied by the evaluation value q1 at the beginning based on the changed motion. The lifespan estimation unit 54 multiplies the evaluation value q1 at the beginning based on the changed motion by the lifespan threshold coefficient k2 for the changed motion to determine the lifespan threshold TH2 applied to the changed motion. The lifespan estimation unit 54 creates a trend line TL2 based on multiple evaluation values ​​based on the changed motion, and determines the timing at which the trend line TL2 reaches the lifespan threshold TH2 applied to the changed motion as the predicted lifespan timing PL2.

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

[0145] 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 lifespan timing is calculated as follows. That is, the lifespan estimation unit 54 creates a trend line TL1 based on multiple evaluation values ​​based on the pre-change motion and calculates the predicted lifespan timing PL1 for the pre-change motion, which is the timing at which this trend line TL1 reaches the lifespan threshold TH1 applied to the pre-change motion. The lifespan estimation unit 54 calculates the remaining lifespan rate tr1 for the pre-change motion by dividing the time from the timing tp2 of the final evaluation value p2 based on the pre-change motion to the predicted lifespan timing PL1 by the time from the timing tp1 of the initial evaluation value p1 based on the pre-change motion to the predicted lifespan timing PL1. Here, the coefficient indicating how many times the lifespan threshold TH1 applied to the pre-change motion is relative to the initial evaluation value p1 based on the pre-change motion is referred to as the lifespan threshold coefficient k1 for the pre-change motion. 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 this value by the remaining life rate tr1 for the operation before the change, and then adding 1 to the result.

[0146] As a result, by using the life threshold coefficient and the remaining life rate, the life threshold TH2 based on the changed operation can be obtained by simple calculation.

[0147] In the second method, when the robot's motion, which is the basis for acquiring the time-series data of the status signal, has changed two or more times (N times), the predicted lifespan timing is calculated as follows. That is, when focusing on one of the first through Nth changes, the lifespan estimation unit 54 calculates the predicted lifespan timing PL for the motion immediately before the focused change. The predicted lifespan timing PL for the motion immediately before the focused change is calculated by creating a trend line TL based on multiple evaluation values ​​based on the motion immediately before the focused change, and calculating the timing at which this trend line TL reaches the lifespan threshold TH applied to the motion immediately before the focused change. Next, the lifespan estimation unit 54 calculates the remaining lifespan rate tr for the motion immediately before the focused change. The remaining lifespan rate tr for the motion immediately before the focused change is calculated by dividing the time from the timing of the final evaluation value based on the motion immediately before the focused change to the predicted lifespan 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 lifespan timing PL for the motion immediately before the focused change. The life estimation unit 54 focuses on each of the first through Nth changes and determines the predicted life timing PL1, PL2, ..., PLN and remaining life rates 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 result by the sum of the remaining life rates tr1, tr2, ..., trN for the first through Nth changes, and adds 1 to the result to determine the life threshold coefficient k(N+1) for the operation after the Nth change. The life estimation unit 54 multiplies the initial evaluation value based on the operation after the Nth change by the life threshold coefficient k(N+1) for the operation after the Nth change to determine 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 allows the influence of multiple changes in the robot 1's behavior on the evaluation value to be estimated with high accuracy.

[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 calculated 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 calculate 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 extended to [d2'] shown below so that it can be applied to changes from the second time onwards.

[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, ..., the (N+1)th reference pattern. The life threshold coefficient k(N+1) corresponding to the (N+1)th reference pattern is calculated as shown in the following formula, using the life threshold coefficient k1 calculated for the first reference pattern as the base and exponent Π, which is the sum of the remaining life rates tr1, tr2, ..., trN calculated from the first reference pattern to the Nth reference pattern: k(N+1)=k1 Π

[0153] Under the same conditions, the life threshold coefficients k2, ..., k(N+1) will have similar values ​​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 calculated as follows: That is, the life estimation unit 54 calculates, as the life threshold coefficient k2 for the changed motion, a power value obtained by using the life threshold coefficient k1 calculated for the motion before the change as the base and exponent of the remaining life rate tr1 for the motion before the change.

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

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

[0157] This allows the influence of multiple changes in the robot 1's behavior on the evaluation value to be estimated with high accuracy.

[0158] The third method will be explained below with reference to FIG.

[0159] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold TH1 of the initial reference pattern is calculated by multiplying the initial evaluation value p1 of the initial reference pattern by the life threshold coefficient k1 (TH1 = p1 * k1). [b1] The life threshold coefficient k2 of the changed reference pattern is calculated by dividing the life threshold TH1 of the original reference pattern by the final evaluation value p2 based on the original reference pattern (k2 = TH1 / p2). [c] The life threshold TH2 applied to the changed reference pattern is calculated by multiplying the initial evaluation value q1 based on the changed reference pattern by the life threshold coefficient k2 of the changed reference pattern (TH2 = q1 * k2). [d] A trend line TL2 is created based on multiple evaluation values ​​based on the changed reference pattern. The timing at which this trend line TL2 reaches the life threshold TH2 of the changed reference pattern is defined as the 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 of 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 third method described above, 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. Determine 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 TH1 of the pre-change reference pattern by the obtained value to determine the 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 variation 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 timing tp2 of the final evaluation value p2.

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

[0166] The fourth method will be described below with reference to FIG.

[0167] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold TH1 of the initial reference pattern is calculated by multiplying the initial evaluation value p1 of the initial reference pattern by the life threshold coefficient k1 (TH1 = p1 · k1). [b] An adjustment coefficient is estimated, which expresses the impact of a change to the reference pattern on the evaluation value as a ratio. For example, the adjustment coefficient ka1 can be 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). [c] The life threshold TH1 of the pre-change reference pattern is divided by the adjustment coefficient ka1 to calculate the life threshold TH2 of the post-change reference pattern (TH2 = TH1 / ka1). [d] A trend line TL2 is created based on multiple evaluation values ​​based on the post-change reference pattern. The timing at which this trend line TL2 reaches the life threshold TH2 applied to the post-change reference pattern is defined as the 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 also be used.

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

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

[0171] The fifth method will be described below with reference to FIG.

[0172] [a] The life threshold coefficient k1 is a predetermined value, and the life threshold TH1 of the initial reference pattern is the initial evaluation value p1 of the initial reference pattern multiplied by the life threshold coefficient k1 (TH1 = p1 * k1). [b] A trend line TL2 is created based on multiple evaluation values ​​based on the changed reference pattern, and the value indicated by this trend line TL2 at the time of zero elapsed time is determined as the past pre-increase estimated evaluation value st1. [c] The past pre-increase estimated evaluation value st1 is multiplied by the life threshold coefficient k1 to determine the life threshold TH2 based on the changed reference pattern (TH2 = st1 * k1). [d] The timing at which the trend line TL2 reaches the life threshold TH2 based on the changed reference pattern is determined 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 in Figs. 11 to 23 are primarily intended to provide an easy-to-understand explanation of the method. In actual practice, it is believed 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, steps [b] to [d] can be performed for each change. The life threshold coefficient k1 initially determined in step [a] can be used in common.

[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 time when elapsed time is zero is determined as the past pre-rise estimated evaluation value stN for the changed motion. The lifespan estimation unit 54 multiplies the past pre-rise estimated evaluation value stN for the changed motion by the above-mentioned lifespan threshold coefficient k1 to determine the lifespan threshold THN to be applied to the changed motion. The lifespan 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 lifespan threshold THN to be applied to the changed motion as the predicted lifespan timing PLN.

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

[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 below 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. Alternatively, in the sixth method, the past pre-increase estimated evaluation value st1x is calculated as follows.

[0180] First, a trend line TL2 is calculated based on multiple evaluation values ​​based on the post-change operation. In Fig. 17, the trend line TL2 is indicated by a dotted line. Next, the life estimation unit 54 calculates the slope of an auxiliary line AL1 (described below) based on the load corresponding to the evaluation values ​​used as the basis for calculating the trend line TL2 and the load for the pre-change operation.

[0181] Generally, the greater the load on a machine, the faster the deterioration of the machine progresses. 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. The slope of the trend line TL2 is ΔTL2, and the load corresponding to the evaluation value used as the basis for determining the trend line TL2 is 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 is ΔAL1=ΔTL2 / (L TL2 / L0). Hereinafter, L TL2 The value of / L0 is sometimes called the load correction factor.

[0182] Next, the lifespan estimation unit 54 creates an auxiliary line AL1 that has the obtained slope ΔAL1 and passes through the initial evaluation value q1 after the change. An example of the auxiliary line AL1 is shown in FIG. 17. The auxiliary line AL1 is used to estimate the transition of the evaluation value when it is assumed that the robot 1 was performing the changed operation before the change. L TL2 <L0, the load correction factor is smaller than 1, so the gradient of the auxiliary line AL1 is greater than the gradient of the trend line TL2.

[0183] In the sixth method, the value indicated by the auxiliary line AL1 at the time when the elapsed time is zero is set as the past pre-increase estimated evaluation value st1x for the changed operation. The past pre-increase estimated evaluation value st1x is multiplied by the life threshold coefficient k1 to obtain the life threshold TH2 based on the changed operation (TH2 = st1x k1). The timing when the trend line TL2 reaches the life threshold TH2 applied to the changed reference pattern is set as the predicted life timing PL2.

[0184] Load L TL2 , L0 can be set arbitrarily. For example, the root mean square value of the motor current (I2 mentioned above) can be used as the load. The value obtained by raising the mean cube of the absolute value of the current value to the 1 / 3 power can be used as the load.

[0185] The load may be a value obtained by raising the mean of the absolute value of the current to the power of 10 / 3 to the power of 3 / 10. Specifically, the value of the average load torque Tm shown in the following formula is multiplied by the load L TL2 , L0. where t1, t2, t3 are time, T1, T2, T3 are absolute values ​​of torque, and N1, N2, N3 are absolute values ​​of rotation speed. The value obtained by multiplying the above values ​​by the speed coefficient is the load L. TL2 , L0.

[0186] The trend line TL2 is calculated based on multiple evaluation values, so the load L of the operation after the change 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 lifespan timing is calculated as follows. That is, the lifespan estimation unit 54 calculates the load L based on the motion after the change by multiplying the load L based on the motion before the change by the load L TL2The life estimation unit 54 creates a trend line TL2 based on a plurality of evaluation values ​​based on the operation after the change. The life estimation unit 54 creates an auxiliary line AL1 that passes through the evaluation value q1 at the beginning after the change. The slope ΔAL1 of the auxiliary line AL1 is calculated by dividing the slope ΔTL2 of the trend line TL2 by the load correction rate (L TL2 / L0). The value indicated by this auxiliary line AL1 at the timing when the elapsed time is zero 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 post-change operation reaches the life threshold value TH2 applied to the post-change operation is defined as the predicted life timing PL2.

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

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

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

[0191] The adjustment coefficient ka1 can be considered an estimated value that expresses, as a ratio, the effect on the evaluation value due to a change in the movement of the robot 1. Even after a change in the movement pattern of the robot 1, 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 by this adjustment coefficient ka1. With this method, in order to obtain the trend line TL2, it is necessary to obtain evaluation values ​​for at least two times after a change in the movement of the robot 1. To improve the accuracy of the trend line, the trend line TL2 may be obtained on the condition that evaluation values ​​have been 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 lifespan timing is determined as follows. That is, the lifespan estimation unit 54 estimates an adjustment coefficient ka1 that expresses, as a ratio, the effect on the evaluation value due to the change in the motion of the robot 1. The lifespan 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 lifespan estimation unit 54 determines the timing at which the trend line TL2 reaches the lifespan threshold TH1 applied to the motion before the change as the predicted lifespan timing PL2.

[0193] In this way, the adjustment coefficient ka1 can be used to match the evaluation value obtained after the operation change 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 lifespan estimation unit 54 calculates the 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. Furthermore, the adjustment coefficient ka1 can be calculated even immediately after the robot's operation is changed.

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

[0197] [a] The 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 for the pre-change reference pattern is used as is. For the evaluation value obtained using the post-change reference pattern, the adjusted evaluation value obtained by multiplying the evaluation value by the adjustment coefficient ka1 is used instead. [c] An overall trend line (trend line) GTL1 is created based on multiple evaluation values ​​spanning both before and after the change. The timing when this overall trend line GTL1 reaches the life threshold TH1 set for the pre-change reference pattern is set as the predicted life timing PL2.

[0198] The predicted end-of-life timing PL2 obtained by the eighth method is approximately the same as the predicted end-of-life timing PL2 obtained by the seventh method described above.

[0199] In the eighth method, as in the seventh method, the adjustment coefficient ka1 can be considered as an estimated value that expresses, in percentage, the effect that a change in the behavior of the robot 1 has on the evaluation value. 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 accumulated as appropriate, a trend line TL2 can be generated based only on the changed evaluation values ​​multiplied by the adjustment coefficient ka1. This trend line TL2 is generated according to the seventh method described above. The display of the overall trend line GTL1 may be stopped simultaneously with the display of this trend line TL2 on the display unit 55, or after a predetermined time has passed since the display of the trend line TL2.

[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 lifespan timing is determined as follows. That is, the lifespan estimation unit 54 estimates an adjustment coefficient ka1 that expresses, as a ratio, the effect that a change in the motion of the robot 1 has on the evaluation value. The lifespan 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 lifespan estimation unit 54 determines the timing at which the overall trend line GTL1 reaches the lifespan threshold TH1 applied to the motion before the change as the predicted lifespan timing PL2.

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

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

[0204] [a] A trend line TL1 is created based on multiple evaluation values ​​based on the pre-change reference pattern. [b] The value indicated by the created trend line TL1 at timing tq1 of the initial evaluation value q1 based on the post-change reference pattern is calculated. Hereinafter, this value will be referred to as the trend line value tv1. [c] An adjustment coefficient ka2 is obtained by dividing the trend line value tv1 by the initial evaluation value q1 based on the post-change reference pattern (ka2 = tv1 / q1). [d] An adjusted evaluation value is obtained by multiplying the evaluation value obtained using the post-change reference pattern by the adjustment coefficient ka2. [e] A trend line TL2 is created based on multiple adjusted evaluation values ​​obtained for the post-change reference pattern. The timing at which this trend line TL2 reaches the life threshold TH1 defined for the post-change reference pattern is designated as the predicted life timing PL2.

[0205] The tenth method is described below with reference to FIG. 21 . [a] A trend line TL1 is created based on multiple evaluation values ​​based on the pre-change reference pattern. [b] The value of the created trend line TL1 at timing tq1 of the initial evaluation value q1 based on the post-change reference pattern is calculated. Hereinafter, this value will be referred to as the trend line value tv1. [c] An adjustment coefficient ka2 is obtained by dividing the trend line value tv1 by the initial evaluation value q1 based on the post-change reference pattern (ka2 = tv1 / q1). [d] The evaluation value of the pre-change reference pattern is used as is. For the evaluation value obtained using the post-change reference pattern, the adjusted evaluation value obtained by multiplying the evaluation value by the adjustment coefficient ka2 is used instead. [e] An overall trend line GTL1 is created based on multiple evaluation values ​​spanning both the pre-change and post-change periods. The timing at which this overall trend line GTL1 reaches the life threshold TH1 applied to the pre-change reference pattern 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 align the evaluation value of the robot 1 after changing the movement pattern 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 state time-series data, is changed as shown in Figure 20 or 21, the adjustment coefficient ka2 is estimated as follows. That is, the lifespan estimation unit 54 creates a trend line TL1 based on multiple evaluation values ​​based on the motion before the change, and calculates a trend line value tv1, which is the value indicated by the trend line TL1 at timing tq1 of the initial evaluation value q1 based on the motion after the change. The lifespan 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 calculation using the trend line TL1. Furthermore, the adjustment coefficient ka1 can be calculated even immediately after the operation of the robot 1 is changed.

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

[0210] [a] A trend line TL1 is created based on multiple evaluation values ​​based on the pre-change reference pattern. The value indicated by the created trend line TL1 at the timing of the reference pattern change is calculated. Hereinafter, this value will be referred to as the first trend line value tvx1. In the example of FIG. 22 , the timing of the reference pattern change is the timing when the operator instructs the controller 90 to change the operating program, but this is not limited to this. In this embodiment, typically, when a new reference pattern is registered, an evaluation value based on that reference pattern is also obtained at the same time. Therefore, the timing of the reference pattern change can also be determined by the timing when the initial evaluation value based on the new reference pattern is obtained. [b] A trend line TL2 is created based on multiple evaluation values ​​based on the changed reference pattern. The value indicated by the created trend line TL2 at the timing of the aforementioned reference pattern change is calculated. Hereinafter, this value will be referred to as the second trend line value tvx2. [c] An 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 from 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 adjusted evaluation values ​​obtained for the changed reference pattern. The timing at which this trend line TL2x reaches the life threshold TH1 defined for the original reference pattern is defined as the predicted life timing PL2.

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

[0212] [a] A trend line TL1 is created based on multiple evaluation values ​​based on the pre-change reference pattern. The value indicated by the created trend line TL1 at the timing of the reference pattern change (the above-mentioned first trend line value tvx1) is calculated. [b] A trend line TL2 is created based on multiple evaluation values ​​based on the post-change reference pattern. The value indicated by the created trend line TL2 at the timing of the reference pattern change (the above-mentioned second trend line value tvx2) is calculated. [c] An adjustment coefficient ka3 is obtained 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. For the evaluation value obtained using the post-change reference pattern, the adjusted evaluation value obtained by multiplying the evaluation value by the adjustment coefficient ka3 is used instead. [e] An overall trend line GTL1 is created based on multiple evaluation values ​​spanning both the pre-change and post-change periods. 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 align the evaluation value of the robot 1 after changing the movement pattern 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 23 , the adjustment coefficient ka3 is estimated as follows. The lifespan 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 change in the motion of the robot 1. The lifespan 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 change in the motion of the robot 1. The lifespan estimation unit 54 determines the adjustment coefficient ka3 by dividing the first trend line value tvx1 by the second trend line value tvx2.

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

[0216] In the seventh to twelfth methods, it is conceivable that the behavior 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," and the processing described for each method should be performed for each change.

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

[0218] A part of the robot 1 may be replaced for some reason, such as a failure. The robot 1 has many parts, but here we consider a case where a part that is dominant in terms of the lifespan of the robot 1 is replaced. 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 when, for example, an output gear of a reducer, a motor, or the like is replaced.

[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 lifespan prediction for the specified axis of the robot 1 without using the data that was used for the lifespan prediction before the reset. However, the basic operation pattern that was set before the reset continues to be used.

[0221] Data removed from the basis of life prediction by resetting the elapsed time includes, 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. Therefore, for example, if the elapsed time reset operation is performed at the "present" point in Figure 12, all evaluation values, trend lines, etc. that existed in the plot area are erased. After that, evaluation values ​​are plotted at appropriate times as the number of days elapses from day 0, 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 referenced in the future for purposes such as maintenance. The date and time when the elapsed time is 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 lifespan 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 lifespan estimation unit 54 multiplies the initial evaluation value based on the behavior after the elapsed time is reset to zero by a predetermined lifespan threshold coefficient to determine the lifespan threshold after the reset. A trendline is created based on multiple evaluation values ​​based on the behavior after the elapsed time has been reset to zero, and the timing at which the trendline reaches the lifespan threshold after the reset is determined to be the predicted lifespan timing.

[0224] This allows for appropriate lifespan estimation, taking into account resetting of 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 instead 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 saves the current values ​​in real time during the playback operation, and transmits the time-series data of the current values ​​together with the program number, information identifying the reference pattern, the date and time the current values ​​were 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 the teaching pendant of the robot 1, for example.

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

[0229] In the first method, it is possible that the increase ratio is less than 1. In this case, the increase ratio can be considered to be 1. In the second method or its modified example, it is possible that, for example, the trend line is horizontal or slopes downward to the right, making it impossible to determine the timing at which the lifespan threshold is reached. In this case, the remaining life rate can be considered to be 1. These two situations are examples of possible problems with appropriate robot lifespan prediction, but are not limited to these. Such situations may be caused by an inappropriate reference pattern on which the evaluation value is based. Therefore, the condition monitoring device 5 may discard the currently used reference pattern and obtain a new reference pattern when it becomes clear that a problem is occurring in the calculation for lifespan prediction.

[0230] In the second and subsequent methods, the evaluation value in the early stages can be a representative value such as the median or average of multiple evaluation values ​​obtained around the first time.

[0231] In the second and subsequent methods, the final evaluation value based on the pre-change behavior can also be a representative value such as the median or average of the 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. This method of evaluating the effect of a 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. Alternatively, or in addition, 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 can be set by any other appropriate method instead of pattern matching with the reference pattern.

[0235] The flowcharts shown in the above embodiments 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, ASICs (Application Specific Integrated Circuits), conventional circuits, and / or combinations thereof, configured or programmed to perform the disclosed functions. A processor is considered a processing circuit or circuit because it includes transistors and other circuitry. 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. Where hardware is a processor, which is considered a type of circuit, the circuit, means, or unit is a combination of hardware and software, and software is used to configure the hardware and / or processor.

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

[0238] a search processing unit that acquires repeatedly played back position time series data for each axis of the robot's motion state and determines whether or not there is a match with the stored basic data; a time series data acquisition unit that acquires time series data of state signals for each axis that reflects the robot's state during a time interval that matches the basic data; an evaluation value calculation unit that acquires an evaluation value for evaluating the state of the robot based on the time series data acquired by the time series data acquisition unit; and a lifespan estimation unit that creates a trend line that shows the tendency of the evaluation value to change over time and determines the timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing.

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

[0240] [2] The status monitoring device of [1], further comprising an operation change monitoring unit that monitors the status signals of each axis that are repeatedly played back, wherein the operation change monitoring unit monitors whether or not a portion that matches the basic data appears in the status signals of each axis that reflect the state of the robot, and when the number of times that the basic data appears 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 that appears equal to or less than the certain number of times.

[0241] This allows the basic data to be changed accordingly even when the robot's operation for the actual work is changed, ensuring continuity in the collection of evaluation values.

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

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

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

[0245] This allows the robot to be determined to be in an operating state including the short stoppage period even if all axes stop for a short period of time during operation.

[0246] [5] The state monitoring device according to [1] or [2], wherein the state monitoring device 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 in a state where all axes of the robot are suitable for condition evaluation.

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

[0249] This prevents the basic data from including a positioning operation or the like when the robot performs a positioning operation or the like at the beginning of the execution of the program.

[0250] [7] A status monitoring device according to any one of [1] to [6], wherein the number of basic data acquisitions is a value obtained by dividing the operating state time by the basic data upper limit length, and rounding up the decimal point 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] The condition monitoring device according to [7], wherein 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 of extraction, it is possible to obtain as many basic data as necessary from the position time series data.

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

[0255] In this case, an appropriate number of basic data can be obtained depending on the situation from 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 status monitoring device according to any one of [1] to [6], wherein the basic data processing unit acquires one of the basic data from the position time series data of each axis of the operating state of the robot corresponding to the operating state time, and stores the basic data.

[0257] In this case, one basic data is acquired from the position time-series data, which simplifies the processing.

[0258]

[11] The status monitoring device of

[10] , wherein the basic data processing unit acquires one basic data of the basic data upper limit length from the end of the position time series data of each axis of the operating state of the robot corresponding to the operating state time.

[0259] This prevents the basic data from including a positioning operation or the like when the robot performs a positioning operation or the like at the beginning of the execution of the program.

[0260]

[12] A status monitoring device that monitors the status of a robot that can reproduce predetermined actions, comprising: a time series data acquisition unit that acquires time series data of a status signal that reflects the status of the robot; a memory unit that stores the time series data acquired by the time series data acquisition unit; and a lifespan estimation unit that acquires an evaluation value for evaluating the status of the robot based on the time series data, creates a trend line that shows the tendency of the evaluation value to change over time, and determines the timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing, wherein when the robot action that is the basis for acquiring the time series data of the status signal is changed, the lifespan estimation unit determines the lifespan threshold to be applied to the changed action, creates a trend line based on a plurality of the evaluation values ​​based on the changed action, and determines the timing at which the trend line reaches the lifespan threshold to be applied to the changed action as the predicted lifespan timing.

[0261]

[13] A status monitoring device that monitors the status of a robot that can reproduce predetermined actions, comprising: a time series data acquisition unit that acquires time series data of a status signal that reflects the status of the robot; a storage unit that stores the time series data acquired by the time series data acquisition unit; and a lifespan estimation unit that acquires an evaluation value for evaluating the status of the robot based on the time series data, creates a trend line that shows the tendency of the evaluation value to change over time, and determines the timing at which the trend line will reach a predetermined lifespan threshold as a predicted lifespan timing, wherein when a robot action that is the basis for acquiring the time series data of the status signal is changed, the lifespan estimation unit: (1) determines the lifespan threshold to be applied to the changed action, creates a trend line based on a plurality of the evaluation values ​​based on the changed action, and determines the timing at which the trend line will reach the lifespan threshold to be applied to the changed action, or (2) estimates an adjustment coefficient that represents the influence of a change in the robot action on the evaluation value as a ratio, multiplies the evaluation value based on the changed action by the adjustment coefficient to obtain an adjusted evaluation value, creates a trend line based on the plurality of adjusted evaluation values ​​based on the changed action, and determines the timing at which the trend line will reach the lifespan threshold to be applied to the action before the change. A condition monitoring device in which the predicted lifespan timing is set as the predicted lifespan timing.

Claims

1. A status monitoring device that monitors the status of a robot that can reproduce predetermined actions, comprising: a time series data acquisition unit that acquires time series data of a status signal that reflects the status of the robot; a memory unit that stores the time series data acquired by the time series data acquisition unit; and a lifespan estimation unit that acquires an evaluation value for evaluating the status of the robot based on the time series data, creates a trend line that shows the tendency of the evaluation value to change over time, and determines the timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing, wherein when a robot action that is the basis for acquiring the time series data of the status signal is changed, the lifespan estimation unit: calculates a past pre-rise estimated evaluation value for the changed action, and when a coefficient indicating how many times the lifespan threshold applied to the action before the change is a multiple of the evaluation value at the beginning based on the action before the change is called a lifespan threshold coefficient for the action before the change, multiplies the past pre-rise estimated evaluation value for the changed action by the lifespan threshold coefficient for the action before the change to determine the lifespan threshold to be applied to the changed action, A condition monitoring device that creates a trend line based on a plurality of evaluation values ​​based on the changed operation, and determines the predicted life timing as the timing when the trend line reaches the life threshold applied to the changed operation.

2. A condition monitoring device as described in claim 1, wherein, when the robot's motion, which is the basis for acquiring the time series data of the status signal, is changed, the lifespan estimation unit obtains an increase ratio for the motion before the change by dividing the final evaluation value based on the motion before the change by the evaluation value in the early stage based on the motion before the change, and obtains the past pre-increase estimated evaluation value for the motion after the change by dividing the evaluation value in the early stage based on the motion after the change by the increase ratio for the motion before the change.

3. A status monitoring device as set forth in claim 2, wherein, when the robot's motion, which is the basis for obtaining the time-series data of the status signal, has been changed two or more times, the lifespan estimation unit: obtains an increase ratio for the motion before the most recent change by dividing the final evaluation value based on the motion before the most recent change by the past pre-increase estimated evaluation value for the motion before the most recent change; obtains a past pre-increase estimated evaluation value for the motion after the most recent change by dividing the initial evaluation value based on the motion after the most recent change by the increase ratio for the motion before the most recent change; obtains a lifespan threshold to be applied to the motion after the most recent change by multiplying the past pre-increase estimated evaluation value for the motion after the most recent change by the lifespan threshold coefficient for the first motion before the change; creates a trendline based on the multiple evaluation values ​​based on the motion after the most recent change, and defines the predicted lifespan timing as the time when the trendline reaches the lifespan threshold to be applied to the motion after the most recent change.

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

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

6. A status monitoring device according to claim 2 or 3, 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. A condition monitoring device according to claim 2 or 3, wherein the initial evaluation value based on the pre-change operation is the median or average of multiple evaluation values ​​obtained around the initial time, including the initial evaluation value based on the pre-change operation.

8. A status monitoring device as defined in claim 1, wherein, when the robot's behavior that is the basis for obtaining the time-series data of the status signal is changed, the lifespan estimation unit creates a trend line based on the multiple evaluation values ​​based on the changed behavior, and determines the value indicated by the trend line at the timing when the elapsed time is zero as the past pre-rise estimated evaluation value for the changed behavior, and, when a coefficient indicating how many times the lifespan threshold applied to the behavior before the change is greater than the evaluation value at the beginning based on the behavior before the change is called the lifespan threshold coefficient for the behavior before the change, determines the lifespan threshold to be applied to the changed behavior by multiplying the past pre-rise estimated evaluation value for the changed behavior by the lifespan threshold coefficient for the behavior before the change, and determines the timing at which the trend line created based on the multiple evaluation values ​​based on the behavior after the change reaches the lifespan threshold to be applied to the changed behavior as the predicted lifespan timing.

9. A condition monitoring device as described in claim 1, wherein, when the robot's operation that is the basis for obtaining the time-series data of the status signal is changed, the lifespan estimation unit: calculates a load correction factor by dividing the load based on the changed operation by the load based on the operation before the change; creates a trend line based on the multiple evaluation values ​​based on the changed operation; creates an auxiliary line that has a slope obtained by dividing the slope of the trend line by the load correction factor and passes through the evaluation values ​​at the beginning of the changed operation; calculates the value indicated by the auxiliary line at the time when the elapsed time is zero as the past pre-rise estimated evaluation value for the changed operation; and determines the predicted lifespan timing to be the time when the trend line created based on the multiple evaluation values ​​based on the changed operation reaches the lifespan threshold applied to the changed operation.

10. A status monitoring device that monitors the status of a robot that can reproduce predetermined actions, comprising: a time series data acquisition unit that acquires time series data of status signals that reflect the status of the robot; a memory unit that stores the time series data acquired by the time series data acquisition unit; and a lifespan estimation unit that acquires an evaluation value for evaluating the status of the robot based on the time series data, creates a trend line that shows the tendency of the evaluation value to change over time, and determines the timing at which the trend line reaches a predetermined lifespan threshold as the predicted lifespan timing, wherein when the robot action that is the basis for acquiring the time series data of the status signal is changed, the lifespan estimation unit: calculates a lifespan threshold coefficient for the changed action that indicates how many times the lifespan threshold to be applied to the changed action is greater than the evaluation value at the beginning based on the changed action; multiplies the evaluation value at the beginning based on the changed action by the lifespan threshold coefficient for the changed action to determine the lifespan threshold to be applied to the changed action; and creates a trend line based on multiple evaluation values ​​based on the changed action, and determines the timing at which the trend line reaches the lifespan threshold to be applied to the changed action as the predicted lifespan timing.

11. A status monitoring device according to claim 10, wherein, when the robot's motion, which is the basis for obtaining the time-series data of the status signal, is changed, the lifespan estimation unit: creates a trend line based on the plurality of evaluation values ​​based on the motion before the change, and calculates the predicted lifespan timing for the motion before the change, which is the timing when the trend line will reach the lifespan threshold applied to the motion before the change; obtains a remaining lifespan rate for the motion before the change by dividing the time from the timing of the final evaluation value based on the motion before the change to the predicted lifespan timing by the time from the timing of the initial evaluation value based on the motion before the change to the predicted lifespan timing; and, when a coefficient indicating how many times the lifespan threshold applied to the motion before the change is greater than the initial evaluation value based on the motion before the change is called the lifespan threshold coefficient for the motion before the change, obtains the lifespan threshold coefficient for the motion after the change by subtracting 1 from the lifespan threshold coefficient for the motion before the change, multiplying the subtracted value by the remaining lifespan rate for the motion before the change, and adding 1 to the multiplied value.

12. A status monitoring device as set forth in claim 11, wherein, when the robot's motion, which is the basis for obtaining the time-series data of the status signal, has been changed N times (where N is an integer of 2 or more), the lifespan estimation unit: for each of the first to Nth changes, creates a trend line based on the plurality of evaluation values ​​based on the motion immediately before the change, and calculates the predicted lifespan timing for the motion immediately before the change, which is the timing at which the trend line reaches the lifespan threshold applied to the motion immediately before the change; obtains a remaining lifespan rate for the motion immediately before the change by dividing the time from the timing of the final evaluation value based on the motion immediately before the change to the predicted lifespan timing by the time from the timing of the initial evaluation value based on the motion immediately before the change to the predicted lifespan timing; obtains a lifespan threshold coefficient for the motion before the first change by subtracting 1 from the lifespan threshold coefficient for the motion before the first change, multiplying the value after the subtraction by the sum of the remaining lifespan rates from the first to Nth times, and adding 1 to the value after the multiplication; a condition monitoring device that multiplies the evaluation value at an early stage based on the operation after the Nth change by the life threshold coefficient for the operation after the Nth change to determine a life threshold to be applied to the operation after the Nth change, creates a trend line based on the multiple evaluation values ​​based on the operation after the Nth change, and determines the timing at which the trend line reaches the life threshold to be applied to the operation after the Nth change as the predicted life timing.

13. A status monitoring device according to claim 10, wherein, when the robot's operation, which is the basis for obtaining the time-series data of the status signal, is changed, the lifespan estimation unit: creates a trend line based on the plurality of evaluation values ​​based on the operation before the change, and calculates the predicted lifespan timing for the operation before the change, which is the timing when the trend line will reach the lifespan threshold applied to the operation before the change; obtains a remaining lifespan rate for the operation before the change by dividing the time from the timing of the final evaluation value based on the operation before the change to the predicted lifespan timing by the time from the timing of the initial evaluation value based on the operation before the change to the predicted lifespan timing; and when a coefficient indicating how many times the lifespan 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 lifespan threshold coefficient for the operation before the change, the lifespan threshold coefficient for the operation after the change is calculated as a power value using the lifespan threshold coefficient obtained for the operation before the change as the base and exponent of the remaining lifespan rate for the operation before the change.

14. A status monitoring device according to claim 13, wherein when the robot's motion, which is the basis for acquiring the time-series data of the status signal, has been changed N times (where N is an integer of 2 or more), the lifespan estimation unit: for each of the first to Nth changes, creates a trend line based on the plurality of evaluation values ​​based on the motion immediately prior to the change, and calculates the predicted lifespan timing for the motion immediately prior to the change, which is the timing at which the trend line reaches the lifespan threshold applied to the motion immediately prior to the change; obtains a remaining lifespan rate for the motion immediately prior to the change by dividing the time from the timing of the final evaluation value based on the motion immediately prior to the change to the predicted lifespan timing by the time from the timing of the initial evaluation value based on the motion immediately prior to the change to the predicted lifespan timing; obtains a lifespan threshold coefficient for the motion after the Nth change, using the lifespan threshold coefficient for the motion before the first change as a base and exponenting the sum of the remaining lifespan rates from the first to Nth times; and obtains a lifespan threshold coefficient for the motion after the Nth change by multiplying the evaluation value at the initial stage based on the motion after the Nth change by the lifespan threshold coefficient for the motion after the Nth change. A condition monitoring device that creates a trend line based on a plurality of evaluation values ​​based on operation after the Nth change, and defines the predicted life timing as the timing when the trend line reaches the life threshold applied to operation after the Nth change.

15. A status monitoring device as described in claim 10, wherein, when the robot's operation that is the basis for obtaining the time-series data of the status signal is changed, the lifespan estimation unit determines the lifespan threshold coefficient for the changed operation by dividing the lifespan 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, where the 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 the lifespan threshold coefficient for the operation before the change.

16. A condition monitoring device as described in claim 10, wherein, when the robot's operation, which is the basis for obtaining the time series data of the condition signal, is changed, the lifespan estimation unit estimates an adjustment coefficient that expresses, as a ratio, the effect on the evaluation value caused by the change in the robot's operation, and obtains the lifespan threshold for the changed operation by dividing the lifespan threshold applied to the operation before the change by the adjustment coefficient.

17. A status monitoring device that monitors the status of a robot that can reproduce predetermined actions, comprising: a time series data acquisition unit that acquires time series data of a status signal that reflects the status of the robot; a memory unit that stores the time series data acquired by the time series data acquisition unit; and a lifespan estimation unit that acquires an evaluation value for evaluating the status of the robot based on the time series data, creates a trend line that shows the tendency of the evaluation value to change over time, and determines the timing at which the trend line reaches a predetermined lifespan threshold as the predicted lifespan timing, wherein when the robot's action that is the basis for acquiring the time series data of the status signal is changed, the lifespan estimation unit estimates an adjustment coefficient that expresses, as a ratio, the effect that the change in robot action has on the evaluation value, creates a trend line based on the adjusted evaluation value obtained by multiplying the evaluation value based on the changed action by the adjustment coefficient, and determines the timing at which the trend line reaches the lifespan threshold applied to the action before the change as the predicted lifespan timing.

18. A condition monitoring device as described in claim 17, wherein, when the robot's operation, which is the basis for acquiring the time series data of the condition signal, is changed, the lifespan estimation unit 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. A condition monitoring device as claimed in claim 17 or 18, wherein the lifespan estimation unit calculates the adjustment coefficient by dividing the final evaluation value based on the operation before the change by the initial evaluation value based on the reference pattern after the change.

20. A condition monitoring device as claimed in any one of claims 16 to 18, wherein the life estimation unit creates a trend line based on the plurality of evaluation values ​​based on operation before the change, calculates a trend line value which is the value indicated by the trend line at the timing of the evaluation value at the beginning based on operation after the change, and calculates the adjustment coefficient by dividing the trend line value by the evaluation value at the beginning based on operation after the change.

21. A condition monitoring device as claimed in any one of claims 16 to 18, wherein the lifespan estimation unit: creates a first trend line based on a plurality of evaluation values ​​based on the operation before the change, and determines a first trend line value that is a value indicated by the first trend line at the timing of the change in the operation of the robot; creates a second trend line based on a plurality of evaluation values ​​based on the operation after the change, and determines a second trend line value that is a value indicated by the second trend line at the timing of the change in the operation of the robot; and determines the adjustment coefficient by dividing the first trend line value by the second trend line value.

22. A condition monitoring device as described in claim 17, wherein, when the robot's operation, which is the basis for obtaining the time-series data of the condition signal, has changed two or more times, the lifespan estimation unit estimates the adjustment coefficient for each change in the robot's operation, and creates the trend line based on: an adjusted evaluation value obtained by multiplying the evaluation value based on the operation before the most recent change by the adjustment coefficient before the most recent change; and an adjusted evaluation value obtained by multiplying the evaluation value based on the operation after the most recent change by the adjustment coefficient after the most recent change.

23. A condition monitoring device as set forth in claim 22, wherein, when the robot's operation, which is the basis for obtaining the time-series data of the condition signal, has changed two or more times, the lifespan estimation unit obtains a final adjustment evaluation value by multiplying the final evaluation value based on the operation before the most recent change by the adjustment coefficient before the most recent change, and obtains the adjustment coefficient after the most recent change by dividing the final adjustment evaluation value by the initial evaluation value based on the reference pattern after the most recent change.

24. A condition monitoring device as described in claim 22, wherein, when the robot's operation, which is the basis for obtaining the time-series data of the status signal, has changed two or more times, the lifespan estimation unit: obtains the plurality of adjusted evaluation values ​​before the most recent change by multiplying the plurality of evaluation values ​​based on the operation before the most recent change by the adjustment coefficient before the most recent change; creates a trend line based on the plurality of adjusted evaluation values ​​before the most recent change, and obtains a trend line value that is a value indicated by the trend line at the timing of the initial evaluation value based on the operation after the most recent change; and obtains the adjustment coefficient after the most recent change by dividing the trend line value by the initial evaluation value based on the operation after the most recent change.

25. A condition monitoring device as described in claim 22, wherein, when the robot's operation, which is the basis for obtaining the time-series data of the status signal, has changed two or more times, the lifespan estimation unit: obtains the plurality of adjustment evaluation values ​​before the most recent change by multiplying the plurality of evaluation values ​​based on the operation before the most recent change by the adjustment coefficient before the most recent change; creates a first trend line based on the plurality of adjustment evaluation values ​​before the most recent change, and obtains a first trend line value that is the value indicated by the first trend line at the timing of the most recent change in the robot's operation; creates a second trend line based on the plurality of evaluation values ​​based on the operation after the most recent change, and obtains a second trend line value that is the value indicated by the second trend line at the timing of the change in the robot's operation; and obtains the adjustment coefficient after the most recent change by dividing the first trend line value by the second trend line value.

26. A condition monitoring device as set forth in any one of claims 1 to 3, claims 8 to 18, or claims 22 to 25, wherein the lifespan 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 behavior, which is the basis for obtaining the time-series data of the status signal, is assumed to be the same before and after the elapsed time is reset; the evaluation value at the beginning, which is based on the behavior after the elapsed time has been reset to zero, is multiplied by a predetermined lifespan threshold coefficient to determine the lifespan threshold after the reset; a trendline is created based on multiple evaluation values, which are based on the behavior after the elapsed time has been reset to zero; and the timing at which the trendline reaches the lifespan threshold after the reset is defined as the predicted lifespan timing.

27. A status monitoring method for monitoring the status of a robot capable of reproducing predetermined actions, comprising the steps of: acquiring time-series data of a status signal reflecting the status of the robot; storing the acquired time-series data; acquiring an evaluation value for evaluating the status of the robot based on the time-series data; creating a trend line that shows the tendency of the evaluation value to change over time; and determining the timing at which the trend line reaches a predetermined lifespan threshold as the predicted lifespan timing; when the robot action that is the basis for acquiring the time-series data of the status signal is changed, determining a past pre-rise estimated evaluation value for the changed action; where a coefficient indicating how many times the lifespan threshold applied to the action before the change is a multiple of the evaluation value at the beginning based on the action before the change is called the lifespan threshold coefficient for the action before the change, determining the lifespan threshold to be applied to the changed action by multiplying the past pre-rise estimated evaluation value for the changed action by the lifespan threshold coefficient for the action before the change; creating a trend line based on multiple evaluation values ​​based on the action after the change; and determining the timing at which the trend line reaches the lifespan threshold to be applied to the action after the change as the predicted lifespan timing.

28. A status monitoring program for monitoring the status of a robot capable of reproducing predetermined actions, comprising: a time-series data acquisition step for acquiring time-series data of status signals reflecting the status of the robot; a storage step for storing the time-series data acquired in the time-series data acquisition step; and a lifespan estimation step for acquiring an evaluation value for evaluating the status of the robot based on the time-series data, creating a trend line showing the tendency of the evaluation value to change over time, and determining the timing at which the trend line reaches a predetermined lifespan threshold as a predicted lifespan timing, wherein when the robot action that is the basis for acquiring the time-series data of the status signal is changed, the lifespan estimation step: calculates a past pre-rise estimated evaluation value for the changed action, and when a coefficient indicating how many times the lifespan threshold applied to the action before the change is a multiple of the evaluation value at the beginning based on the action before the change is called a lifespan threshold coefficient for the action before the change, multiplies the past pre-rise estimated evaluation value for the changed action by the lifespan threshold coefficient for the action before the change to determine the lifespan threshold to be applied to the changed action, A status monitoring program that creates a trend line based on a plurality of evaluation values ​​based on the changed operation, and determines the predicted life timing as the timing when the trend line reaches the life threshold applied to the changed operation.

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