Abnormal equipment judgment system

Abstract

The present invention provides an abnormal device judgment system, which detects the abnormality of any multiple power consumption devices based on the total current waveform while reducing the sensing cost required during operation. The abnormal device judgment system includes: a storage unit for storing a reference waveform, which records the current supplied to the power consumption device that consumes power during normal operation and the current supplied to the control circuit board that supplies power to the multiple power consumption devices in time series; a current acquisition unit for acquiring the current supplied to the control circuit board during operation in time series; an abnormal judgment unit, which uses the current supplied to the control circuit board acquired by the current acquisition unit and the reference waveform to calculate the current change rate of each power consumption device and judge the abnormality of the power consumption device; and a judgment result display unit for displaying the result obtained by the abnormal judgment unit.

Description

Technical Field

[0001] The present invention relates to an abnormal device judgment system. The present invention claims priority to Japanese Patent Application No. 2020-093852 filed on May 29, 2020, and the contents described in the application are incorporated into the present application by reference for designated countries that recognize citation based on literature reference. Background Art

[0002] Patent document 1 discloses a technology for determining abnormalities in each driving device of a printer having multiple driving devices based on the current value of each driving device in a normal state obtained in advance and the overall current consumption obtained successively during operation.

[0003] Existing technical problems

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent Application Laid-Open No. 2008-76292 Summary of the invention

[0006] Technical problem to be solved by the invention

[0007] In Patent Document 1, when the current waveforms of a plurality of drive devices vary, it is difficult to obtain the operating current waveform of each drive device from the operating total current waveform by subtraction.

[0008] An object of the present invention is to detect abnormalities in any plurality of power consumption devices based on a total current waveform while reducing the cost of sensing required for operation.

[0009] Technical means for solving technical problems

[0010] The present application includes a plurality of technical means for solving at least a part of the above-mentioned technical problems, one example of which is described as follows.

[0011] One embodiment of the present invention is an abnormal device judgment system, characterized in that it includes: a storage unit for storing a reference waveform, which records, in time series, the current supplied to a power consumption device that consumes power during normal operation and the current supplied to a control circuit board that supplies power to a plurality of the power consumption devices; a current acquisition unit for acquiring, in time series, the current supplied to the control circuit board during operation (operation); an abnormality judgment unit, which uses the current supplied to the control circuit board acquired by the current acquisition unit and the reference waveform to calculate a current change rate of each of the power consumption devices and judge the abnormality of the power consumption device; and a judgment result display unit for displaying the result of the judgment made by the abnormality judgment unit.

[0012] Effects of the Invention

[0013] According to the present invention, it is possible to detect abnormalities in any plurality of power consumption devices based on the total current waveform while reducing the sensing cost required for operation.

[0014] Technical problems, structures, and effects other than those described above will become clear through the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a diagram showing an example of an abnormal device determination device and a target device.

[0016] Figure 2 This is a diagram showing an example of a power supply system circuit of a target device for abnormal device determination.

[0017] Figure 3 This figure shows an example of a current waveform in one manufacturing process for each motor.

[0018] Figure 4 It is a diagram showing an example of a total current waveform.

[0019] Figure 5 This is a flowchart showing an example of the flow of abnormality determination processing.

[0020] Figure 6 This is a block diagram showing a configuration example of an abnormal device determination device. Figure 6 (a) is a block diagram showing an example of a structure related to learning. Figure 6 (b) is a block diagram showing an example of a structure related to the runtime.

[0021] Figure 7 This is a diagram showing an example of a current waveform during operation. Figure 7 (a) shows the total current waveform obtained, Figure 7 (b) shows an example of a graph obtained by integrating the current waveforms of individual devices.

[0022] Figure 8 This is a diagram showing an example of a waveform of a current in one manufacturing process for each motor during operation.

[0023] Fig. 9 It is a diagram showing an example of the processing flow of the abnormality determination processing (first embodiment).

[0024] Fig.10 This is a graph that extrapolates the current waveform of individual devices using the calculated rate of change.

[0025] Fig.11 It is a diagram showing an example of a display screen of a change rate displayed on a determination result display unit.

[0026] Fig.12 It is a diagram showing an example of the processing flow of the abnormality determination processing (second embodiment).

[0027] Fig.13 This is a diagram that estimates the current waveform of individual equipment during abnormal conditions.

[0028] Fig.14 It is a diagram showing an example of a display screen of a change rate displayed on a determination result display unit.

[0029] Fig.15 It is a diagram showing an example of the processing flow of the abnormality determination processing (third embodiment).

[0030] Fig.16 It is a diagram showing an example of a display screen of a change rate displayed on a determination result display unit.

[0031] Fig.17 It is a diagram showing an example of the processing flow of the abnormality determination processing (fourth embodiment).

[0032] Fig.18 This is a diagram showing an example of the relationship between the total current waveform and the current waveform of each individual device. Fig.18 (a) shows the total current waveform obtained, Fig.18 (b) shows an example of a graph obtained by integrating the current waveforms of individual devices.

[0033] Fig.19 This is a graph that extrapolates the current waveform of individual devices using the calculated rate of change.

[0034] Fig. 20 This is a diagram showing an example of the relationship between the total current waveform and the current waveform of each individual device. Fig. 20 (a) shows the total current waveform obtained, Fig. 20 (b) shows an example in which the graphs of the current×voltage ratio of the individual devices are integrated.

[0035] Fig.21 FIG. 1 is a diagram showing an example of a power supply system circuit of a target device according to the fifth embodiment.

[0036] Fig. 22 It is a diagram showing an example of the processing flow of the abnormality determination processing (fifth embodiment).

[0037] Fig.23 This is a diagram showing an example of the relationship between the total current waveform and the current waveform of each individual device. Fig.23 (a) shows the total current waveform obtained, Fig.23 (b) is a diagram showing an example in which graphs of current×voltage ratios of individual devices converted from voltage ratios are integrated.

[0038] Fig.24 It is a diagram showing a schematic structure of a target device according to the sixth embodiment.

[0039] Fig.25 This is a diagram showing an example of the schematic configuration of a power supply system circuit of a numerically controlled metal processing machine.

[0040] Fig.26 It is a diagram showing an example of the processing flow of the abnormality determination processing (sixth embodiment).

[0041] Fig. 27 It is a diagram showing a hardware configuration example of the abnormal device determination device. DETAILED DESCRIPTION

[0042] In the following embodiments, for the sake of convenience of explanation, they are divided into multiple parts or embodiments for description when necessary, but except for cases where it is specifically noted, they are not unrelated to each other, but are in a relationship where one is a partial or complete modification example, detailed description, supplementary description, etc. of the other.

[0043] In addition, in the following embodiments, when referring to the quantity of elements (including number, value, amount, range, etc.), except for cases where it is specifically noted and cases where it is clearly limited to a specific number in principle, it is not limited to the specific number and can be above or below the specific number.

[0044] Furthermore, in the following embodiments, the constituent elements (including element steps, etc.) are not essential unless otherwise specified or unless otherwise clearly considered to be essential in principle.

[0045] Likewise, in the following embodiments, when referring to the shapes, positional relationships, etc. of constituent elements, except for cases specifically mentioned or cases clearly considered not to be so in principle, shapes substantially similar or similar to the shapes, etc. are included. The same applies to the above-mentioned numerical values ​​and ranges.

[0046] In addition, in all the drawings for explaining the embodiments, the same reference numerals are given to the same components in principle, and their repeated descriptions are omitted. Hereinafter, each embodiment of the present invention will be explained using the drawings.

[0047] Conventionally, there are many devices driven by motors in industrial equipment, and there is a technology that uses a detector called an encoder to obtain the actual rotation state of the motor and change the control input value to the motor to perform high-precision control. By using the mechanism that the control input value changes according to the actual state, a current sensor is used to obtain the control input value, especially the current value, from the outside to determine the abnormal state of the motor.

[0048] In such a technology, when the rotation is transmitted from the motor using a gear such as a speed reducer, the abnormal state of the gear can be determined. In recent years, it is not uncommon for industrial equipment such as robots and known machinery to have multiple drive units in one device, and each of them is controlled by a motor. Therefore, it can be considered that the abnormality of each drive unit can be determined by obtaining the current value for each drive unit.

[0049] However, when judging an abnormality of a driving device such as an electric motor equipped with multiple devices, if a sensor such as a current sensor is installed in each driving device, in addition to the cost of the sensor itself, each driving device will also need an analog-to-digital converter that converts analog sensor data into a digital signal, and a recording device for reading the digital signal. As a result, the cost of these devices is increased, and the sensing cost increases.

[0050] In the system described in the above-mentioned patent document 1, a learning phase is set before application, the current of each driving device is measured and stored, and during operation, a sensor is used to measure only the consumption current of the control circuit board at the source of each driving device, which is the sum of the consumption currents of each driving device (hereinafter referred to as the total current), to detect abnormalities.

[0051] In such a technology, when calculating the current waveform of the target drive device, the current waveform of the target drive device in operation is obtained by subtracting the current of other drive devices other than the target drive device from the total current measured in operation. When abnormality is determined, the obtained current waveform of the target drive device in operation is compared with the current waveform of the target drive device stored in advance to make a judgment.

[0052] However, this method has two disadvantages. The first is that when the current waveforms of multiple drive devices change at the same time, it is difficult to obtain the operating current waveform of each drive device based on the total current waveform during operation by subtraction. The second is that even if the current waveform changes in only one drive device, only the drive device that first had an abnormality can be used as the object.

[0053] In the following embodiments, since various structures that may be adopted by actual devices are supported, they have high versatility as an abnormal device judgment system, which is an advantage.

[0054] Figure 1: is a diagram showing an example of an abnormal device judgment device and an object device. The object device includes a control box 200 and a robot unit 300. The power supply of the object device is obtained from a distribution cabinet (distribution board) 100. In the robot unit 300, there are multiple working axes, and the first working axis 301, the second working axis 302, the third working axis 303, the fourth working axis 304, the fifth working axis 305, and the sixth working axis 306 can rotate in the direction of the arrows. Each working axis can be considered as a power-consuming device that consumes power during normal operation. The control box 200 controls the action of the robot unit 300.

[0055] The abnormal device determination device 1 is connected to a control box 200 of a target device. The abnormal device determination device 1 includes a storage unit 10 and a processing unit 20. The storage unit 10 includes a reference waveform storage unit 11 for storing a waveform serving as a reference. More specifically, the storage unit 10 stores reference waveforms that record, in time series, the current supplied to the power consumption device and the current supplied to the control circuit board that supplies power to the plurality of power consumption devices.

[0056] The processing unit 20 includes a current acquisition unit 21, an abnormality determination unit 22, and a determination result display unit 23. The current acquisition unit 21 acquires the current supplied to the control circuit board 220 in a time series via a current sensor described later. The abnormality determination unit 22 determines whether the acquired current value is abnormal. The determination result display unit 23 displays the determination result of the abnormality determination unit 22.

[0057] Fig. 27 : This is a diagram showing an example of the hardware structure of an abnormal device judgment device. The abnormal device judgment device 1 can be implemented by a common computer including a central processing unit (CPU) 2, a memory 3, an external storage device 4 such as a hard disk device (HDD), a reading device 6 for reading information from a portable storage medium 5 such as a CD (Compact Disk) and a DVD (Digital Versatile Disk), an input device 7 such as a keyboard, a mouse, a barcode reader, an output device 8 such as a display, and a communication device 9 for communicating with other computers via a communication network such as the Internet, or a network system including multiple such computers. In addition, the reading device 6 can of course not only read the portable storage medium 5, but also write it.

[0058] For example, the current acquisition unit 21, the abnormality judgment unit 22, and the judgment result display unit 23 included in the processing unit 20 can be realized by loading a prescribed program stored in the external storage device 4 into the memory 3 and executing it by the CPU 2, and the storage unit 10 can be realized by the CPU 2 using the memory 3 or the external storage device 4.

[0059] The prescribed program may also be downloaded from the portable storage medium 5 to the external storage device 4 via the reading device 6, or downloaded from the network to the external storage device 4 via the communication device 9, and then loaded into the memory 3 and executed by the CPU 2. Alternatively, the prescribed program may be directly loaded from the portable storage medium 5 to the memory 3 via the reading device 6, or directly loaded from the network to the memory 3 via the communication device 9, and executed by the CPU 2.

[0060] Figure 2 1 is a diagram showing an example of a power system circuit of an object device for abnormal device judgment. Power is supplied to each motor as a driving motor in the robot part 300 from the power distribution cabinet 100 through the control box 200. The robot part 300 includes a plurality of driving motors, and the rotation of each driving motor is transmitted to the speed reducer, thereby causing the working shaft provided at the front end thereof to operate. The speed reducer is a mechanism that reduces the input rotation speed and increases the torque.

[0061] exist Figure 2 In the example of FIG. 1 , the control box 200 includes a power supply circuit 210 and a control circuit board 220. The power supplied from the power distribution cabinet 100 is transmitted to the power supply circuit 210 and supplied to each motor through the control circuit board 220. The control circuit board 220 can supply power to a plurality of power consuming devices. Regarding the driving motors, the robot part 300 is equipped with a first motor 321, a second motor 322, a third motor 323, a fourth motor 324, a fifth motor 325, and a sixth motor 326.

[0062] The first reducer 311, the second reducer 312, the third reducer 313, the fourth reducer 314, the fifth reducer 315, and the sixth reducer 316 are connected to each driving motor. In addition, the first working shaft 301, the second working shaft 302, the third working shaft 303, the fourth working shaft 304, the fifth working shaft 305, and the sixth working shaft 306 are connected to each reducer.

[0063] Figure 3 An example of a waveform of a current in a manufacturing process for each motor. The current waveform shown in the present invention records a waveform obtained by extracting an envelope from an AC waveform, but an effective value or a torque current value obtained by converting an AC component can also be used in addition to the envelope. The robot part 300 is configured to drive the motor connected to each working axis according to the magnitude and frequency of the current output from the control circuit board 220 in accordance with a prescribed action program to make it operate. Therefore, it is known that these current waveforms are important physical quantities that reflect the deterioration state of the motor of each working axis of the robot, the reducer as a mechanical component connected to the motor, and the working axis.

[0064] The current waveform 501 is a waveform of the current of the first motor 321 connected to the first working shaft 301. Similarly, the current waveform 502, the current waveform 503, the current waveform 504, the current waveform 505, and the current waveform 506 are respectively waveforms of the current of the second motor 322 connected to the second working shaft 302, the current waveform of the third motor 323 connected to the third working shaft 303, the current waveform of the fourth motor 324 connected to the fourth working shaft 304, the current waveform of the fifth motor 325 connected to the fifth working shaft 305, and the current waveform of the sixth motor 326 connected to the sixth working shaft 306.

[0065] Figure 4 601 is a diagram showing an example of a total current waveform. The total current waveform 601 is a current waveform flowing from the switchboard 100 through the power supply circuit 210 into the control circuit board 220 , and the total current waveform details 602 show the details of each axis in the total current waveform 601 .

[0066] The total current waveform 601 and the total current waveform details 602 are equal current waveforms, indicating that the sum of the currents of each working axis is equal to the total current, which is expressed by the following formula (1). w(t) Represents the total current at a certain time t, I i(t) The current of each working axis at a certain time t (hereinafter referred to as individual device current as a physical quantity including the states of the working axis, the speed reducer, and the motor) is shown, and i represents the number of the working axis.

[0067] [Number 1]

[0068] I w(t) =I 1(t) +I 2(t) +I 3(t) +I 4(t) +I 5(t) +I 6(t) ……(1)

[0069] In the above, the focus is on the current, but the condition for focusing only on the current is that the voltage at each part is constant (constant), and the current is proportional to the electric power (electric power). In the existing examples and the first to third embodiments of the present invention described later, the condition of constant voltage (constant voltage) is used as a premise.

[0070] <Example of prior art>

[0071] Next, for comparison, a configuration example of a conventional abnormal device determination device is used. Figures 5 to 8 For explanation. Figure 5 In the example of the process flow of the abnormal device judgment device of the prior art shown in the form of a flowchart, Figure 62 shows an example of the prior art and a block diagram of the abnormal device determination device of the present invention.

[0072] Figure 6 (a) is a block diagram showing an example of a structure of the abnormal device determination device 1 related to learning. Figure 6 (b) is a block diagram showing an example of a structure related to operation of the abnormal device judgment device 1. During learning, the current acquisition unit 21 included in the processing unit 20 of the abnormal device judgment device 1 acquires the total current value from the total current sensor 701. The total current sensor 701 is provided in the wiring between the distribution cabinet 100 and the power circuit 210, or in the wiring between the power circuit 210 and the control circuit board 220.

[0073] During learning, the current acquisition unit 21 acquires the current value of each axis (individual device) from the axis 1 current sensor 702 to the axis 6 current sensor 703. The axis 1 current sensor 702 to the axis 6 current sensor 703 are provided in the wiring between the control circuit board 220 and each motor.

[0074] When the current acquisition unit 21 acquires the current value, the current acquisition unit 21 stores all waveforms as reference waveforms in the reference waveform storage unit 11. The stored reference waveforms are waveforms such as current waveforms 501 to 506, and the sum of the currents of the operating axes must be equal to the total current.

[0075] The current acquisition unit 21 analyzes the correlation between past fault records and current data, and calculates and sets an index (abnormality degree) and a threshold value thereof for determining a fault (abnormality) using a predetermined algorithm.

[0076] During operation, the current acquisition unit 21 included in the processing unit 20 of the abnormal device determination device 1 acquires the total current value from the total current sensor 701 in the same manner as during learning. The total current sensor 701 is provided in the wiring between the distribution cabinet 100 and the power circuit 210, or in the wiring between the power circuit 210 and the control circuit board 220 in the same manner as during learning.

[0077] The current acquisition unit 21 sends the acquired total current to the abnormality judgment unit 22, and the abnormality judgment unit 22 obtains the reference waveform from the reference waveform storage unit 11 to judge whether the total current corresponds to an abnormality. When the abnormality judgment unit 22 judges that it is abnormal, the judgment result display unit 23 determines the message and the abnormal working axis, generates display information, and displays it on a display of the device judgment device 1 (not shown).

[0078] Here, in the processing during operation based on the prior art, the abnormality judgment unit 22 infers the current waveform of the target device for abnormality judgment based on the total current waveform acquired during operation. In this processing, calculation is performed based on the mathematical formula shown in the following formula (2). Among them, the subscript 0 in the brackets represents the current waveform during learning, and the subscript t in the brackets represents a certain time during operation. In addition, the following formula (2) is an example in which the abnormal detection object is the first working axis.

[0079] [Number 2]

[0080] I 1(t) =I w(t) -I 2(0) -I 3(0) -I 4(0) -I 5(0) -I 6(0) ……(2)

[0081] That is, when abnormality determination is performed according to the conventional technique, the current waveform of the current target device, ie, the individual device, is calculated by subtracting the current waveform of the individual device other than the target device during learning from the current total current.

[0082] Figure 7 FIG. 1 is a diagram showing an example of a current waveform during operation obtained in abnormality judgment based on the prior art. Figure 7 In (a), the total current waveform 611 obtained is shown. Figure 7 (b) shows an example of the current waveform 612 of the individual device 1 obtained by the above equation (2).

[0083] Figure 8 This figure shows an example of the waveform of the current of one manufacturing process of each motor when it is running. Figure 8 The example shown is similar to Figure 3 The example of learning is basically the same, but the current waveform 511 of the first working axis 301 is different from the current waveform 501. In this case, in order to detect abnormalities using the conventional technology, the current waveforms 512 to 516 of the second working axis 302 to the sixth working axis 306 during operation need to be the same waveforms as the current waveforms 502 to 506 during learning. If the waveforms are different, the current waveform 511 of the first working axis 301 cannot be correctly obtained, and the abnormality judgment cannot be considered to be a correct judgment. That is, it is impossible to detect abnormalities in multiple devices.

[0084] Figure 5 1 is a flowchart showing an example of a flow of an abnormality determination process in the prior art. The flow consists of steps S001 to S009, the process during learning is the process of steps S001 to S003, and the process during operation is the process of steps S004 to S009.

[0085] First, the current acquisition unit 21 acquires the total current and the current of each axis (individual device) (step S001). Specifically, the current acquisition unit 21 acquires the total current value from the total current sensor 701 and acquires the current value of each axis (individual device) from the axis 1 current sensor 702 to the axis 6 current sensor 703.

[0086] Then, the current acquisition unit 21 stores the current waveform of each axis (individual current waveform during learning) (step S002 ). Specifically, the current acquisition unit 21 stores all waveforms as reference waveforms in the reference waveform storage unit 11 .

[0087] Then, the current acquisition unit 21 sets a threshold value of the abnormality (correlation coefficient) judged as abnormal (step S003). Specifically, the current acquisition unit 21 analyzes the correlation between past fault records and current data, and uses a predetermined algorithm to calculate and set an index (abnormality) and its threshold value for judging as a fault (abnormality). The above is the processing flow during learning.

[0088] Next, the current acquisition unit 21 acquires the total current waveform of one process (the total current during operation) (step S004 ). Specifically, the current acquisition unit 21 acquires the total current value from the total current sensor 701 .

[0089] Then, the abnormality determination unit 22 eliminates the acquired total current during operation using the reference waveform of each axis other than the abnormality detection target (step S005). Specifically, the abnormality determination unit 22 acquires the reference waveform from the reference waveform storage unit 11 and subtracts it from the total current.

[0090] Then, the abnormality determination unit 22 calculates the abnormality degree of the extracted waveform seen from the reference waveform (step S006). Specifically, the abnormality determination unit 22 calculates the correlation coefficient between the extracted waveform and the reference waveform during learning to calculate the abnormality degree.

[0091] Then, the abnormality determination unit 22 determines whether the abnormality degree is greater than or equal to a threshold value (step S007). Specifically, the abnormality determination unit 22 determines whether the correlation coefficient calculated in step S006 is greater than or equal to the abnormality degree threshold value set in step S003.

[0092] When the abnormality degree is not equal to or greater than the threshold value (NO in step S007 ), the abnormality determination unit 22 determines that the condition is normal, and returns control to step S004 to advance the process to abnormality detection in the next step (step S008 ).

[0093] When the abnormality degree is equal to or greater than the threshold value (YES in step S007 ), the abnormality determination unit 22 determines that there is an abnormality (step S009 ).

[0094] The above is an example of the processing content of the abnormality determination process (conventional technology).

[0095] <First embodiment>

[0096] Next, use Figures 9 to 11 , the first embodiment of the present invention is described. In the first embodiment, the structure is basically the same as the above-mentioned prior art, but there are differences. The following description will focus on the differences.

[0097] Fig. 9 This is a diagram showing an example of the processing flow of the abnormality judgment process (first embodiment). In the process during learning, after storing the current waveform of each axis, the current acquisition unit 21 sets the threshold value of the change rate as the abnormality degree judged as abnormal (step S103), which is different from the prior art. Specifically, the current acquisition unit 21 analyzes the correlation between past fault records and current data, uses the change rate of the current waveform as an indicator for judging a fault (abnormality), calculates and sets its threshold value using a prescribed algorithm.

[0098] Then, in the processing during operation, after obtaining the total current waveform of one process, the abnormality judgment unit 22 performs multivariate analysis, such as multiple regression analysis, on the total current obtained during operation, and calculates the change rate of the current waveform of each axis (step S105). Specifically, first, assuming that all working axes (individual devices) have changed compared to the time of learning, the abnormality judgment unit 22 constructs the formula shown in the following formula (3). Among them, α1~α6 are the change rates of the proportions of the current waveform changes of each individual device, which are unknown numbers at the time of processing.

[0099] [Number 3]

[0100] I w(t)

[0101] =α1×I 1(0) +α2×I 2(0) +α3×I 3(0) +α4×I 4(0) +α5×I 5(0) +α6×I 6(0)

[0102] ……Formula (3)

[0103] In the above formula (3), there are 6 unknowns (α1 to α6). In this embodiment, the abnormality to be targeted is based on the condition that the unknown value does not change within one process. That is, the condition is that the entire waveform becomes α i If this condition is met, then the above formula (3) holds true at each time point in the process. Therefore, for example, Figure 3In the 15-second process shown, if the current value is acquired every 1 second, 15 equations (3) can be generated by changing the value of t.

[0104] Since there are 15 equations for the 6 unknown change rates, it can be said that the change rates α1 to α6 can be solved from the viewpoint of simultaneous equations. In this way, it is a feature of this embodiment that all individual devices are assumed to have changed and multiple regression analysis, which is a method of solving multiple unknowns, is used for processing. In addition, in this embodiment, the abnormality determination unit 22 calculates the current change rate by using multiple regression analysis that satisfies the condition that the sum of the products of the current supplied to the plurality of power consumption devices and the current change rate is equal to the current supplied to the control circuit board.

[0105] When the abnormality rate (change rate) is equal to or greater than the threshold value (YES in step S007 ), the abnormality determination unit 22 determines that there is an abnormality, and the determination result display unit 23 displays the change rates α1 to α6 on the display unit (step S109 ).

[0106] Fig.10 This is a graph in which the individual device current waveform is estimated using the calculated rate of change. Each individual device current waveform is expressed by the following equation (4).

[0107] [Number 4]

[0108] I i(t) =α1×I i(t) ……Formula (4)

[0109] Specifically, Fig.10 The current waveform 523 of axis 3 shown is Figure 8 Compared with the current waveform 513 during operation shown, the rate of change is α3 times.

[0110] Fig.11 2 is a diagram showing an example of a display screen showing the rate of change displayed on the determination result display unit. i Take "1" as the initial value, the current waveform at this time is similar to Figure 3 The current waveform shown during learning is the same as the waveform of the individual device during learning.

[0111] The display screen 800 includes a graph 801 showing the transition of the rate of change in each process unit for each work axis. Fig.11 The change rate α recorded in each graph ib , indicating that Fig. 9 The threshold value of the abnormality level is set in step S103 in the flowchart of the first embodiment shown. Fig.11 One plot of the graph 801 in FIG. 8 represents one process, and one plot is generated for each process of step S004 to step S007 of the flowchart.

[0112] To summarize the above, the abnormality determination unit 22 uses the current supplied to the control circuit board acquired by the current acquisition unit 21 and the reference waveform to calculate the current change rate of each power consumption device through multivariate analysis, and determines that the power consumption device is abnormal when the current change rate exceeds a threshold value. In addition, the judgment result display unit 23 displays the result of the judgment made by the abnormality determination unit 22.

[0113] In this embodiment, Fig.11 As shown, the situation where the axis 1 and the axis 3 change at the same time can be known through multivariate analysis, and the effect of being able to judge the abnormality of multiple devices that cannot be used as a target in the prior art can be obtained. In addition, the following effect can be obtained, that is, the effect of being able to observe the situation that is gradually shifting to abnormality even if it is not judged as abnormal through the judgment result display unit 23. The above is the first embodiment of the present invention.

[0114] <Second embodiment>

[0115] Next, use Figure 12 to Figure 14 , the second embodiment of the present invention is described. In the second embodiment, the structure is basically the same as the first embodiment described above, but there are differences. The following description will focus on the differences.

[0116] Fig.12 This is a diagram showing an example of the processing flow of the abnormality judgment processing (second embodiment). The processing during learning is the same as that of the first embodiment. In the processing during operation, after obtaining the total current waveform of one process, the change rate is calculated by multivariate analysis, but in this processing, a time window is set, and part of the data of a specified period is intercepted from the total current waveform and the reference waveform during operation, and multiple multivariate analyses are performed, which is different. That is, the reference waveform is divided into multiple intervals according to the specified time window, and the current change rate is calculated for each interval to judge the abnormality.

[0117] As described in the first embodiment, the number of equations (3) to be constructed in order to calculate the change rates α1 to α6 by multivariate analysis (multivariate regression analysis) is sufficient as the number of unknowns (6 in the case of α1 to α6). For example, if the current value is recorded every 1 second during a 15-second process, the change rate α can be calculated using only 6 seconds of data. i That is, multiple regression analysis can be performed as long as the number of consecutive samples is greater than the number of working axes.

[0118] In the second embodiment, let the data during learning be Figure 3 The current waveform shown is Fig.13 However, in actual processing, no Fig.13 The current waveform group shown is recorded here for illustration purposes only.

[0119] Specifically, the abnormality determination unit 22 first divides and extracts the total current waveform during operation according to the minimum time width (time window) (step S205). The minimum time width in the above example is 6 seconds to ensure the number of samples of unknown numbers, but it is not limited to this and can be longer than this. Then, the abnormality determination unit 22 divides and extracts the total current waveform during learning according to the minimum time width (time window) used in step S205 (step S206).

[0120] Then, the abnormality determination unit 22 calculates the change rate of the current for each axis and each time using multivariate regression analysis (step S207). Specifically, the abnormality determination unit 22 calculates the change rate determined based on the samples within the set time window.

[0121] Through the processing of the above steps S205 to S207, the rate of change is calculated every 6 seconds using multiple regression analysis, so it is possible to obtain Fig.14 The change rate shown in the figure shows the process transition. Since the change rate is calculated by acquiring 6 seconds of data with a shift of 1 second each time, it is discrete but draws a smooth curve like a sine wave. The smaller the number of samples, the more dynamic the change rate response will be.

[0122] Fig.14 810 is a diagram showing an example of a display screen of the change rate displayed on the judgment result display unit. The display screen 810 includes a graph 811 showing the transition of the change rate in time units for each working axis. As shown in the graph 811, in the first working axis 301, the change rate increases only in the first half of the process, and in the third working axis 303, the change rate increases only in the second half of the process, and local abnormalities can be found.

[0123] According to the invention of the second embodiment, even if the abnormal state does not appear uniformly in the process but appears partially in the process, it can be detected with good accuracy. In particular, when a process takes a long time, the accuracy of abnormality detection can be greatly improved.

[0124] <Third embodiment>

[0125] Next, use Fig.15 , Fig.16 , the third embodiment of the present invention is described. In the third embodiment, the structure is basically the same as that of the first embodiment, but there are differences. The following description will focus on the differences.

[0126] Fig.15This is a diagram showing an example of the processing flow of the abnormality judgment process (third embodiment). The process during learning is basically the same as that of the first embodiment, but in addition to the threshold value of the abnormality degree judged as abnormal, a second threshold value of the abnormality degree that should not be judged as abnormal is calculated based on the deviation (variation) of the current waveform during learning, thereby achieving improved judgment accuracy. That is, the threshold value (second threshold value) that becomes the indicator of whether to detect abnormality is determined based on the reference waveform of the current supplied to the control circuit board, and when the current change rate does not exceed the second threshold value, it is not judged as abnormal, thereby preventing erroneous judgment.

[0127] Specifically, after the abnormality threshold is set in step S103, as a process during learning, a second abnormality threshold that takes into account the deviation is set based on the current waveform during learning (step S304). Because the current of each individual device is inferred based on the total current, if a deviation occurs in the total current, there is a concern that the inference accuracy of the current of each individual device will also deviate. The condition for avoiding this situation is that the change in the current of each individual device is larger than the deviation of the total current, which can be expressed by the following formula (5). Among them, SI w(0) Indicates the deviation of the total current waveform during learning.

[0128] [Number 5]

[0129] α i ×I i(0) >I i(0) +SI w(0) ……Formula (5)

[0130] Transform the above formula (5) and let the abnormality threshold considering the deviation be α ia When α ia It can be expressed by formula (6).

[0131] [Number 6]

[0132]

[0133] The α obtained by formula (6) ia In the abnormality determination process during operation, a process for determining that an abnormality is detected when both the abnormality and the second abnormality threshold are exceeded is used (step S307). That is, the quotient obtained by dividing the sum of the variation amount of the reference waveform of the current supplied to the control circuit board 220 and the current supplied to the power consumption device by the current supplied to the power consumption device can be determined as the second abnormality threshold.

[0134] Fig.16 820 is a diagram showing an example of a display screen of the change rate displayed in the judgment result display unit. The display screen 820 includes a graph 821 showing the transition of the change rate for each working axis by process unit. Here, Fig.16The change rate α recorded in each graph ib Indicated in Fig.15 The abnormality threshold value set in step S103 in the flowchart of the third embodiment shown in FIG. ia Indicated in Fig.15 The second abnormality threshold is set in step S304 in the flowchart of the third embodiment shown. Fig.16 One plot in the graph 821 in FIG. 8 represents one process, and one plot is generated for each process of step S004 to step S307 in the flowchart.

[0135] For example, regarding the first working axis, since the rate of change exceeds both the abnormality threshold and the second abnormality threshold, it can be determined to be abnormal. ib , but not yet exceeding α ia If α is not set ia , then the third working axis will be judged as abnormal, but in fact there is a possibility of false alarm caused by the deviation of the total current. In the invention of this embodiment, there is an effect of reducing false alarms caused by sudden phenomena such as the deviation of the total current.

[0136] In addition, as another effect of the invention of the third embodiment, when the deviation of the current of an individual device relative to the total current is small and there is a concern that even if an abnormality occurs, it will be buried in the deviation, a countermeasure can be taken by pre-installing a current sensor only for the individual device. That is, this is also an indicator that can be used to optimize the number of sensors, and it can be said that the effect of appropriately reducing the number of sensors can be achieved while maintaining accuracy.

[0137] <Fourth embodiment>

[0138] Next, use Figures 17 to 20 , a fourth embodiment of the present invention is described. In the fourth embodiment, the structure is basically the same as that of the first embodiment, but there are differences. The following description will focus on the differences.

[0139] Fig.17 404 is a diagram showing an example of a processing flow of an abnormality judgment process (the fourth embodiment). In the fourth embodiment, a countermeasure is added when the voltage supplied to each individual device is different. Specifically, the current acquisition unit 21 calculates the voltage ratio as the ratio of the power supply voltage of each individual device to the source by multivariate regression analysis during learning (step S403), and stores the individual voltage ratio of each axis (step S404). Thus, even when the voltage supplied to the individual device is different, the voltage ratio can be calculated without acquiring the voltage value.

[0140] Fig.18This is a diagram showing the relationship between the total current waveform and the current waveform of each individual device as the object of abnormality determination in the fourth embodiment. Fig.18 (a) is the curve diagram of the total current, Fig.18 (b) is an example of a graph obtained by integrating the current waveforms of individual devices. Fig.18 (a) with Fig.18 (b) The total current is not the same as the sum of the currents of the individual devices. This means that the total current cannot be expressed by simply adding the current waveforms, because the voltage is different in each individual device.

[0141] In the first embodiment, the current relationship is established because the sum of electric power (electric power) is equal to the total electric power and the voltage is constant. In this embodiment, the original relationship that the sum of electric power is equal is used. Here, the method of obtaining the above voltage ratio is explained. Voltage ratio k i is the physical quantity shown in the following formula (7). w Indicates the voltage at the point where the total current is obtained, V i Indicates the voltage at which each individual device draws current.

[0142] [Number 7]

[0143]

[0144] Using the relationship in equation (7) above, equation (8) is used to express the relationship between the voltage ratio and current of each individual device.

[0145] [Number 8]

[0146] I w(0)

[0147] =k1×I 1(0) +k2×I 2(0) +k3×I 3(0) +k4×I 4(0) +k5×I 5(0) +k6×I 6(0)

[0148] ……Formula (8)

[0149] By constructing equation (8) at each moment of the current waveform during learning and performing multiple regression analysis, the voltage ratio k of each individual device can be calculated. i .

[0150] Fig.19 This is a graph that uses the calculated rate of change to infer the current waveform of each individual device. i When calculating the current waveform of each individual device, we can get Fig.19The waveform 541 of the first working axis, the waveform 542 of the second working axis, the waveform 543 of the third working axis, the waveform 544 of the fourth working axis, the waveform 545 of the fifth working axis, and the waveform 546 of the sixth working axis. The vertical axis of each graph is the current×voltage ratio.

[0151] Fig. 20 This is a diagram showing an example of the relationship between the total current waveform and the current waveform of each individual device. Fig. 20 (a) shows the total current waveform obtained. Fig. 20 (b) shows an example of integrating the current × voltage ratio curves of individual devices. Fig. 20 (a) and Fig. 20 Here, the following formula (9) is used to express Fig.17 The mathematical formula used in the multiple regression analysis performed in S405 in the flowchart of FIG.

[0152] [Number 9]

[0153] I w(t)

[0154] =α1×k1×I 1(0) +α2×k2×I 2(0) +α3×k3×I 3(0) +α4×k4×I 4(0) +α5×k5×I 5(0) +α6×k6×I 6(0)

[0155] ……Formula (9)

[0156] When using the above formula (9), since the voltage ratio k of each individual device has been calculated, i , so multiple regression analysis can be performed without problems.

[0157] Thus, according to the fourth embodiment, even when it is unknown whether the voltages of the individual devices are equal, the voltage ratio can be calculated by using equation (8) without adding a voltage sensor, so it can be applied to devices where the voltages of the individual devices are different while maintaining accuracy. That is, the range of applicable devices can be expanded.

[0158] <Fifth embodiment>

[0159] Next, use Figure 21 to Figure 23 , the fifth embodiment of the present invention is described. In the fifth embodiment, the structure is basically the same as that of the first embodiment described above, but there are differences. The following description will focus on the differences.

[0160] Fig.211 is a diagram showing an example of a power system circuit of a target device of the fifth embodiment. In addition to the configurations of the first to fourth embodiments, devices (constituent elements) such as cooling fans 231 and 232 and a non-excitation electromagnetic brake 330 are further added to the control circuit board 220.

[0161] Fig. 22 This is a diagram showing an example of the processing flow of abnormality judgment processing (fifth embodiment). In the fifth embodiment, by collectively processing individual devices other than the main components and other devices that are temporarily difficult to measure during learning, etc., abnormal device judgment can be performed without measuring other devices alone.

[0162] Basically, the processing flow of the abnormality judgment processing of the fifth embodiment is the same as the processing flow of the abnormality judgment processing of the fourth embodiment. However, during learning, the current acquisition unit 21 calculates the individual voltage ratio and constant for each axis using multivariate regression analysis (step S503), regards the constant (residual) as the current of other equipment, and stores the current waveform (step S504).

[0163] That is, in the abnormality determination process of the fifth embodiment, the constant I is used. else(0) Expression and calculation in obtaining individual voltage ratio k i The individual voltage ratio k is shown in the following equation (10): i And other individual equipment current I else(0) The relationship formula.

[0164] [Number 10]

[0165] I w(0)

[0166] =k1×I 1(0) +k2×I 2(0) +k3×I 3(0) +k4×I 4(0) +k5×I 5(0) +k6×I 6(0) +I else(0)

[0167] ……Formula (10)

[0168] Here, I else(0) Yes Fig.21 The values ​​of the current consumption of the cooling fans 231 , 232 and the non-excitation electromagnetic brake 330 .

[0169] The non-excitation electromagnetic brake 330 is a brake generally used in robots. This brake has the characteristics of being in a braking state when no power is supplied, and not being braked when power is supplied. Therefore, it has the characteristics of consuming a fixed amount of power when powered. Fig.21 The dashed arrow in the figure only indicates the direction of the braking action. The above equation (10) can be used to construct the simultaneous equations required for multivariate regression analysis to calculate the voltage ratio k i , and other individual equipment current I else(0) .

[0170] Fig.23 This is a diagram showing an example of the relationship between a total current waveform and a graph obtained by integrating graphs of current×voltage ratios of individual devices obtained by conversion from voltage ratios. Fig.23 (a) represents the total current during learning, Fig.23 (b) indicates that the voltage ratio k obtained by the above formula (10) is used. i The converted current × voltage ratio of each individual device and other individual device current I else(0) The accumulated curve graph. Fig.23 (a) and Fig.23 Comparing with (b), the sizes of the curves of both sides are equal.

[0171] Here, we use formula (11) to express Fig. 22 The mathematical formula used in the multiple regression analysis is shown in the flowchart.

[0172] [Number 11]

[0173] I w(t)

[0174] =α1×k1×I 1(0) +α2×k2×I 2(0) +α3×k3×I 3(0) +α4×k4×I 4(0) +α5×k5×I 5(0) +α6×k6×I 6(0) +α else ×I else(0)

[0175] ……Formula (11)

[0176] When using formula (11), since the voltage ratio k of each individual device has been calculated, i , so multiple regression analysis can be performed without problems, although the resolution is poor, but it is possible to use other individual device current change rates α else That is, the current acquisition unit 21 can calculate the voltage ratio and the change rate by regarding the power consumption devices that do not store the reference waveform in the storage unit 10 but receive power supply from the control circuit board 220 as power consumption devices.

[0177] According to this embodiment, even when multiple individual devices are included in addition to the main device, it is possible to accurately determine abnormalities. In addition, even when other individual devices change within a process, by using multiple waveforms of the same process and using the average value, the above formulas (10) and (11) can be used to achieve the same effect.

[0178] <Sixth embodiment>

[0179] Next, use Figure 24 to Figure 26 , the sixth embodiment of the present invention is described. In the sixth embodiment, the structure is basically the same as the first embodiment described above, but there are differences. The following description will focus on the differences.

[0180] Fig.24 This is a diagram showing an example of the structure of the object device of the sixth embodiment. The object device of the sixth embodiment is a numerically controlled metal processing machine 400 with four-axis motion (X axis 431, Y axis 432, Z axis 433, spindle 434), and a tool exchanger (tool exchange axis 475) for exchanging a tool 461 is also attached as an accessory.

[0181] Fig.25 This is a diagram showing an example of the structure of a power supply system circuit of a numerically controlled metal processing machine. The control device 401 includes a power supply circuit 410, a first control circuit board 411, a second control circuit board 412, and a third control circuit board 413. The power supply circuit 410 receives power supply from the power distribution cabinet 100. Each control circuit board is supplied with power from the power supply circuit 410. Each control circuit board is provided with individual equipment.

[0182] The first control circuit board 411 is provided with a first motor 451, a first speed reducer 441 for reducing the rotation of the first motor 451, and a stage X-axis 431 that slides in the X-axis direction due to the force transmitted by the first speed reducer 441. In addition, the first control circuit board 411 is provided with a second motor 452, a second speed reducer 442 for reducing the rotation of the first motor 451, and a stage Y-axis 432 that slides in the Y-axis direction due to the force transmitted by the second speed reducer 442. In addition, the first control circuit board 411 is provided with a third motor 453, a third speed reducer 443 for reducing the rotation of the first motor 451, and a tool Z-axis 433 that slides in the Z-axis direction due to the force transmitted by the third speed reducer 443.

[0183] The second control circuit board 412 is provided with a fourth motor 454 , a fourth speed reducer 444 for reducing the rotation speed of the fourth motor 454 , and a main shaft 434 that is rotated by the force transmitted from the fourth speed reducer 444 .

[0184] The third control circuit board 413 is provided with a fifth motor 495 , a fifth speed reducer 485 for reducing the rotation of the fifth motor 495 , and a tool exchange shaft 475 that is rotated by the force transmitted from the fifth speed reducer 485 .

[0185] Fig.26 1 is a diagram showing an example of a processing flow of an abnormality determination process (sixth embodiment). In the sixth embodiment, even when power is supplied to the control circuit board in a branched manner, current estimation and abnormality determination of individual devices can be performed at various locations.

[0186] Basically, the processing flow of the abnormality judgment processing of the sixth embodiment is the same as the processing flow of the abnormality judgment processing of the first embodiment. However, during learning, the current acquisition unit 21 acquires the total current and the current of each control circuit board and the accompanying individual device (step S601), and stores the current waveform of each control circuit board and each individual device in the reference waveform storage unit 11 (step S602). Then, during operation, after acquiring the total current waveform of one process (step S004), the abnormality judgment unit 22 calculates the change rate of each control circuit board current through multivariate regression analysis (step S605), calculates and acquires each control circuit board current using the calculated change rate (step S606), and calculates the change rate of each individual device current through multivariate regression analysis (step S607). That is, when there is a control circuit board, recursive processing is performed as a nested structure.

[0187] That is, in the sixth embodiment, the relationship between the control circuit board and the individual devices shown in the first to fifth embodiments is expanded, and the relationship is also applied between multiple control circuit boards and power supply circuits to infer the rate of change of the current from the source through the individual devices of the control circuit board.

[0188] Here, the flow of estimating the current waveforms of the stage X-axis 431 , the stage Y-axis 432 , and the tool Z-axis 433 from the power supply circuit 410 via the first control circuit board 411 will be described in detail.

[0189] Fig.25 In the example, let the current flowing through the wiring between the power distribution cabinet 100 and the power supply circuit 410 be the total current I w(t) , let the current flowing through the wiring between the power supply circuit 410 and each control circuit board be the current I of each control circuit board Ci(t) , let the current flowing through the wiring between the first control circuit board 411 and the first to third motors be the individual device current I i(t) For them, the current acquisition unit 21 uses a current sensor to acquire the waveform during learning, and uses it as I w(0) ,I Ci(0) ,I i(0) Stored in the reference waveform storage unit 11.

[0190] During operation, the current acquisition unit 21 first acquires the total current I w(t) , use the formula shown in the following formula (12) to perform multiple regression analysis and calculate the current change rate α of each control circuit board Ci .

[0191] [Number 12]

[0192] I w(t) =α C1 ×I C1(0) +α C2 ×I C2(0) +α C3 ×I C3(0) ……Formula (12)

[0193] Next, use α C1 ×I C1(0) The total current I is used to infer the current waveform of the individual devices attached to the first control circuit board 411. w(t) Replace with α C1 ×I C1(0) The following formula (13).

[0194] [Number 13]

[0195] α C1 ×I C1(0) =α1×I 1(0) +α2×I 2(0) +α3×I 3(0) ……Formula (13)

[0196] Since the change rates α1 to α3 of the current waveforms of the table X-axis 431 , the table Y-axis 432 , and the tool Z-axis 433 can be calculated according to the above formula (13), the abnormality of the individual devices attached to the first control circuit board 411 can be calculated.

[0197] Thus, the numerically controlled metal processing machine 400 according to the sixth embodiment has the following effects: even if it is a device with a multi-layer branched power system circuit, as long as the reference waveform of the object line can be obtained, abnormality judgment can be implemented while reducing the cost of sensors in operation.

[0198] In addition, the present invention is not limited to the above-mentioned embodiment, and includes various modified examples. For example, the numerically controlled metal processing machine 400 may also be other multi-axis controlled working machines (machine tools). In addition, in the above-mentioned sixth embodiment, a nested structure with one layer of control circuit boards is used as an example, but it is not limited to this, and a nested structure with multiple layers of control circuit boards may also be used.

[0199] Furthermore, although multiple regression analysis was performed as an example of multivariate analysis, the present invention is not limited thereto, and other analysis methods such as principal component analysis may be performed.

[0200] Furthermore, a part of the structure of a certain embodiment may be replaced with the structure of another embodiment, and the structure of another embodiment may be added to the structure of a certain embodiment.

[0201] Furthermore, with respect to a part of the structure of the embodiment, other structures can be added, deleted, or replaced.

[0202] In addition, each structure, function, processing part, processing unit, etc. of the abnormal device judgment device 1 can be partially or completely implemented by hardware, for example, by designing with an integrated circuit. In addition, each of the above structures, functions, etc. can also be implemented by software by interpreting and executing programs that implement each function by a processor. Information such as programs, tables, files, etc. that implement each function can be stored in a recording device such as a memory, a hard disk, an SSD, or a recording medium such as an IC card, an SD card, or a DVD.

[0203] In addition, control lines and information lines are shown as necessary for explanation, and not all control lines and information lines on the product are necessarily shown. In fact, it can be considered that almost all structures are connected to each other through a communication network, bus, etc.

[0204] The technology of the present invention is not limited to the abnormal device determination device, but can be provided in various forms such as an abnormal device determination system, a server device, a computer-readable program, and an abnormal device determination service (method).

[0205] Description of Reference Numerals

[0206] 1... abnormal equipment judgment device, 10... storage unit, 11... reference waveform storage unit, 20... processing unit, 21... current acquisition unit, 22... abnormal judgment unit, 23... judgment result display unit, 100... power distribution cabinet, 200... control box, 300... robot unit, 301... first working axis, 302... second working axis, 303... third working axis, 304... fourth working axis, 305... fifth working axis, 306... sixth working axis.

Claims

1. An abnormal device judgment system, characterized in that: include: a storage unit for storing a reference waveform that records, in time series, currents supplied to power consuming devices that consume power during normal operation and currents supplied to a control circuit board that supplies power to a plurality of the power consuming devices; a current acquisition unit for acquiring, in time series, a current supplied to the control circuit board during operation; an abnormality determination unit that uses the current supplied to the control circuit board acquired by the current acquisition unit and the reference waveform to calculate a current change rate of each of the power consumption devices, thereby determining an abnormality of the power consumption device; and a judgment result display unit for displaying the judgment result of the abnormality judgment unit; The abnormality determination unit calculates the current change rate by using a multivariate regression analysis that satisfies a condition that a sum of products of currents supplied to the plurality of power consumption devices and the current change rate is equal to a current supplied to the control circuit board.

2. The abnormal device determination system according to claim 1, characterized in that: The abnormality determination unit determines abnormality of the plurality of power consumption devices using the current change rate.

3. The abnormal equipment determination system according to claim 1, characterized in that: The abnormality determination unit calculates the current change rate through multivariate analysis.

4. The abnormal equipment determination system according to claim 1, characterized in that: In the multivariate regression analysis, the current change rate is calculated for each interval after the reference waveform is divided into a plurality of intervals in a predetermined time window.

5. The abnormal equipment determination system according to claim 1, characterized in that: The abnormality determination unit determines that the power consumption device is abnormal when the current change rate exceeds a predetermined threshold value.

6. The abnormal equipment determination system according to claim 1, characterized in that: The abnormality determination unit determines a threshold value serving as an index of whether to detect abnormality based on the reference waveform of the current supplied to the control circuit board, and does not determine that an abnormality is present when the current change rate does not exceed the threshold value.

7. The abnormal equipment determination system according to claim 1, characterized in that: The current acquisition section calculates a voltage ratio of each of the consumption devices using the reference waveform of the current supplied to the control circuit board and the reference waveform of the current supplied to the consumption devices.

8. The abnormal equipment determination system according to claim 1, characterized in that: The power consumption device is a drive motor for moving the working axis of the robot.

9. The abnormal equipment determination system according to claim 1, characterized in that: The power consumption device is a driving motor for moving the working axis of the robot. The abnormality includes an abnormality of the driving motor and an abnormality of other devices that affects a current waveform of the driving motor.

10. The abnormal equipment determination system according to claim 1, characterized in that: One or more of the power consuming devices include the control circuit board for supplying power to one or more other power consuming devices, The storage unit stores a reference waveform that records the current supplied to the other power consumption device in time series. The abnormality determination unit also calculates the current change rate for the other power consumption device, and determines the abnormality of the power consumption device.

11. An abnormal device judgment system, characterized in that: include: a storage unit for storing a reference waveform that records, in time series, currents supplied to power consuming devices that consume power during normal operation and currents supplied to a control circuit board that supplies power to a plurality of the power consuming devices; a current acquisition unit for acquiring, in time series, a current supplied to the control circuit board during operation; an abnormality determination unit that uses the current supplied to the control circuit board acquired by the current acquisition unit and the reference waveform to calculate a current change rate of each of the power consumption devices, thereby determining an abnormality of the power consumption device; and a judgment result display unit for displaying the judgment result of the abnormality judgment unit; The abnormality determination unit determines a quotient obtained by dividing a sum of a variation amount of the reference waveform of the current supplied to the control circuit board and the current supplied to the power consumption device by the current supplied to the power consumption device as a threshold value, and does not determine an abnormality when the current change rate does not exceed the threshold value.

12. An abnormal device judgment system, characterized in that: include: a storage unit for storing a reference waveform that records, in time series, currents supplied to power consuming devices that consume power during normal operation and currents supplied to a control circuit board that supplies power to a plurality of the power consuming devices; a current acquisition unit for acquiring, in time series, a current supplied to the control circuit board during operation; an abnormality determination unit that uses the current supplied to the control circuit board acquired by the current acquisition unit and the reference waveform to calculate a current change rate of each of the power consumption devices, thereby determining an abnormality of the power consumption device; and a judgment result display unit for displaying the judgment result of the abnormality judgment unit; The current acquisition unit calculates the voltage ratio of each of the consumption devices by using a multivariate regression analysis that satisfies a condition that a sum of products of the reference waveform of the current supplied to the consumption devices and the voltage ratio of each of the consumption devices is equal to the current supplied to the control circuit board.

13. An abnormal device judgment system, characterized in that: include: a storage unit for storing a reference waveform that records, in time series, currents supplied to power consuming devices that consume power during normal operation and currents supplied to a control circuit board that supplies power to a plurality of the power consuming devices; a current acquisition unit for acquiring, in time series, a current supplied to the control circuit board during operation; an abnormality determination unit that uses the current supplied to the control circuit board acquired by the current acquisition unit and the reference waveform to calculate a current change rate of each of the power consumption devices, thereby determining an abnormality of the power consumption device; and a judgment result display unit for displaying the judgment result of the abnormality judgment unit; the current acquisition unit calculates the voltage ratio of each of the consumption devices by using a multivariate regression analysis that satisfies a condition that the sum of the products of the reference waveform of the current supplied to the consumption devices and the voltage ratio of each of the consumption devices is equal to the current supplied to the control circuit board, The abnormality determination unit calculates the current change rate by using a multivariate regression analysis that satisfies a condition that a sum of products of currents supplied to the plurality of power consumption devices, the current change rate, and a voltage ratio of each of the power consumption devices is equal to a current supplied to the control circuit board.

14. An abnormal device judgment system, characterized in that: include: a storage unit for storing a reference waveform that records, in time series, currents supplied to power consuming devices that consume power during normal operation and currents supplied to a control circuit board that supplies power to a plurality of the power consuming devices; a current acquisition unit for acquiring, in time series, a current supplied to the control circuit board during operation; an abnormality determination unit that uses the current supplied to the control circuit board acquired by the current acquisition unit and the reference waveform to calculate a current change rate of each of the power consumption devices, thereby determining an abnormality of the power consumption device; and a judgment result display unit for displaying the judgment result of the abnormality judgment unit; The current acquisition unit regards power consumption devices that do not store the reference waveform in the storage unit but receive power from the control circuit board as power consumption devices, and calculates the voltage ratio of each of the power consumption devices by using a multivariate regression analysis that satisfies a condition that a sum of products of the reference waveform of the current supplied to the power consumption devices and the voltage ratio of each of the power consumption devices is equal to the current supplied to the control circuit board.

15. An abnormal device judgment system, characterized in that: include: a storage unit for storing a reference waveform that records, in time series, currents supplied to power consuming devices that consume power during normal operation and currents supplied to a control circuit board that supplies power to a plurality of the power consuming devices; a current acquisition unit for acquiring, in time series, a current supplied to the control circuit board during operation; an abnormality determination unit that uses the current supplied to the control circuit board acquired by the current acquisition unit and the reference waveform to calculate a current change rate of each of the power consumption devices, thereby determining an abnormality of the power consumption device; and a judgment result display unit for displaying the judgment result of the abnormality judgment unit; the current acquisition unit regards the power consumption devices that do not store the reference waveform in the storage unit and receive power supply from the control circuit board as power consumption devices, and calculates the voltage ratio of each of the power consumption devices by using a multivariate regression analysis that satisfies a condition that the sum of the products of the reference waveform of the current supplied to the power consumption devices and the voltage ratio of each of the power consumption devices is equal to the current supplied to the control circuit board, The abnormality determination unit calculates the current change rate by using a multivariate regression analysis that satisfies a condition that a sum of products of currents supplied to the plurality of power consumption devices, the current change rate, and a voltage ratio of each of the power consumption devices is equal to a current supplied to the control circuit board.

Images