WO2021240959A1 - Abnormal device determination system - Google Patents

Abstract

The present invention detects an abnormality in given multiple power consumption devices from a total current waveform in a state where a sensing cost necessary during an operation is reduced.　This abnormal device determination system is provided with: a storage unit that stores reference waveforms obtained by time-sequentially recording current supplied to power consumption devices which consume power during a normal operation and current supplied to a control board through which power is supplied to the multiple power consumption devices; a current acquisition unit that time-sequentially acquires the current supplied to the control board during an operation; an abnormality determination unit that calculates a current change rate for each of the power consumption devices by using the reference waveforms and the current which is supplied to the control board and is acquired by the current acquisition unit, and that determines an abnormality in the power consumption device; and a determination result display unit that displays the determination result of the abnormality determination unit.

Description

Abnormal device judgment system

The present invention relates to an abnormal device determination system. The present invention claims the priority of application number 2020-093852 of the Japanese patent filed on May 29, 2020, and for designated countries where incorporation by reference to the literature is permitted, the content described in the application is Incorporated into this application by reference.

Patent Document 1 describes an abnormality of each drive device of a printer having a plurality of drive devices from the current value of each drive device in a normal state that has been acquired in advance and the total current consumption that is sequentially acquired during operation. A technique for determining an abnormality in each drive device is disclosed.

JP-A-2008-76292

In the above-mentioned Patent Document 1, when the current waveform changes in a plurality of drive devices, it is difficult to obtain the operating current waveform of each drive device from the total current waveform during operation by using subtraction.

An object of the present invention is to detect an abnormality of an arbitrary and a plurality of power consuming devices from a total current waveform while reducing the sensing cost required during operation.

The present application includes a plurality of means for solving at least a part of the above problems, and examples thereof are as follows.

One aspect of the present invention is an abnormal device determination system, in which a current supplied to a power consuming device that consumes power during normal operation and a current supplied to a control board that supplies power to the plurality of the power consuming devices. A storage unit that stores a reference waveform in which each of these is recorded in time series, a current acquisition unit that acquires the current supplied to the control board in time series during operation, and the control acquired by the current acquisition unit. An abnormality determination unit that calculates the current change rate for each power consumption device using the current supplied to the substrate and the reference waveform to determine an abnormality in the power consumption device, and a result of determination by the abnormality determination unit. It is characterized by including a determination result display unit for displaying.

According to the present invention, it is possible to detect an abnormality of any and a plurality of power consuming devices from the total current waveform while reducing the sensing cost required during operation.

Issues, configurations and effects other than those described above will be clarified by the explanation of the following embodiments.

It is a figure which shows the example of the abnormality equipment determination apparatus and the target apparatus. It is a figure which shows the example of the power-source system circuit of the target device of abnormality device determination. An example of the current waveform for one manufacturing process for each motor is shown. It is a figure which shows the example of the total current waveform. It is a flowchart which shows the example of the flow of abnormality determination processing. It is a block diagram which shows the structural example of the abnormality equipment determination apparatus. FIG. 6A is a block diagram showing an example of a configuration related to learning, and FIG. 6B is a block diagram showing an example of a configuration related to operation. It is a figure which illustrates the current waveform at the time of operation. FIG. 7 (a) shows the acquired total current waveform, and FIG. 7 (b) shows an example of a graph in which the current waveforms of individual devices are stacked. It is a figure which shows the example of the waveform of the current for one manufacturing process for each motor at the time of operation. It is a figure which shows the example of the processing flow of the abnormality determination processing (first embodiment). It is a figure which estimated the individual device current waveform using the calculated rate of change. It is a figure which shows the example of the display screen of the change rate displayed by the determination result display part. It is a figure which shows the example of the processing flow of the abnormality determination processing (second embodiment). It is the figure which estimated the individual device current waveform at the time of abnormality. It is a figure which shows the example of the display screen of the change rate displayed by the determination result display part. It is a figure which shows the example of the processing flow of the abnormality determination processing (third embodiment). It is a figure which shows the example of the display screen of the change rate displayed by the determination result display part. It is a figure which shows the example of the processing flow of the abnormality determination processing (fourth embodiment). It is a figure which shows the example of the relationship between the total current waveform and the current waveform of each individual device. FIG. 18A shows the acquired total current waveform, and FIG. 18B shows an example of a graph in which the current waveforms of individual devices are stacked. It is a figure which estimated the current waveform of an individual device using the calculated rate of change. It is a figure which shows the example of the relationship between the total current waveform and the current waveform of each individual device. FIG. 20A shows the acquired total current waveform, and FIG. 20B shows an example of stacking graphs of current × voltage ratio of individual devices. It is a figure which shows the example of the power-source system circuit of the target apparatus which concerns on 5th Embodiment. It is a figure which shows the example of the processing flow of the abnormality determination processing (fifth embodiment). It is a figure which shows the example of the relationship between the total current waveform and the current waveform of each individual device. FIG. 23 (a) shows the acquired total current waveform, and FIG. 23 (b) is a diagram showing an example of stacking graphs of current × voltage ratio of individual devices converted by voltage ratio. It is a figure which shows the structure of the outline of the target apparatus which concerns on 6th Embodiment. It is a figure which shows the structural example of the outline of the power supply system circuit of a numerical control metal processing machine. It is a figure which shows the example of the processing flow of the abnormality determination processing (the sixth embodiment). It is a figure which shows the hardware configuration example of the abnormality equipment determination apparatus.

In the following embodiments, when necessary for convenience, the description will be divided into a plurality of sections or embodiments, but unless otherwise specified, they are not unrelated to each other, and one is the other. It is related to some or all modifications, details, supplementary explanations, etc.

Further, in the following embodiments, when the number of elements (including the number, numerical value, quantity, range, etc.) is referred to, except when explicitly stated or when the number is clearly limited to a specific number in principle. , The number is not limited to the specific number, and may be more than or less than the specific number.

Furthermore, it goes without saying that, in the following embodiments, the components (including element steps and the like) are not necessarily essential unless otherwise specified or clearly considered to be essential in principle. stomach.

Similarly, in the following embodiments, when the shape, positional relationship, etc. of the components, etc. are referred to, the shape, etc. It shall include those that are close to or similar to. This also applies to the above numerical values and ranges.

Further, in all the drawings for explaining the embodiment, the same members are in principle given the same reference numerals, and the repeated description thereof will be omitted. Hereinafter, each embodiment of the present invention will be described with reference to the drawings.

Conventionally, in industrial equipment, there are many devices controlled by motors, and the technology to acquire the actual rotation state of the motor with a detector called an encoder and change the control input value to the motor to control it with high accuracy. There is. Using such a mechanism that the control input value changes according to the actual state, an effort is being made to determine the abnormal state of the motor by acquiring the current value among the control input values from the outside using a current sensor. It is done.

With such technology, when the rotation is transmitted from the motor using a gear such as a speed reducer, it is possible to determine the abnormal state of the gear. In recent industrial equipment such as robots and machine tools, it is not uncommon for multiple drive units to exist in one equipment and each to be controlled by a motor. Therefore, it may be possible to determine an abnormality for each drive unit by a method of acquiring a current value for each drive unit.

However, if a sensor such as a current sensor is attached to each drive device when determining an abnormality in a drive device such as a plurality of mounted motors, in addition to the cost of the sensor itself, analog sensor data is converted into a digital signal. A digital converter and a logging device for reading a digital signal are required for each drive device, and the cost of those devices is also required, resulting in high sensing cost.

In the system described in Patent Document 1 described above, a learning phase is provided before operation to measure and store the current of each drive device, and during operation, the total current consumption of each drive device is large. Only the current consumption of the original control board (hereinafter referred to as the total current) is measured by the sensor to detect the abnormality.

In such a technology, when calculating the current waveform of the target drive device, the target drive device is obtained by subtracting the currents of other drive devices other than the target drive device from the total current measured during operation. Obtain the current waveform during operation. At the time of abnormality determination, the determination is made by comparing the obtained operating current waveform of the target drive device with the stored current waveform of the target drive device.

However, this method has two drawbacks. The first is that when the current waveforms of a plurality of drive devices change at the same time, it is difficult to obtain the operating current waveform of each drive device from the total current waveform during operation by using subtraction. The second is that even if only one drive device changes the current waveform, only the drive device in which the abnormality first occurs can be targeted.

In the following examples, since all of them correspond to various configurations that can be taken by an actual device, it is also an excellent point that it has high versatility as an abnormal device determination system.

FIG. 1 is a diagram showing an example of an abnormal device determination device and a target device. The target device includes a control Box 200 and a robot unit 300. The power supply of the target device is obtained from the switchboard 100. The robot unit 300 has a plurality of operating axes, and the first operating axis 301, the second operating axis 302, the third operating axis 303, the fourth operating axis 304, the fifth operating axis 305, and the sixth operating axis 306 are , Each can rotate in the direction of the arrow. It can be said that each operating axis is a power consuming device that consumes electric power during normal operation. The control Box 200 controls the operation of the robot unit 300.

The abnormal device determination device 1 is connected to the control Box 200 of the target device. The abnormality device determination device 1 includes a storage unit 10 and a processing unit 20. The storage unit 10 stores a reference waveform storage unit 11 that stores a reference waveform. More specifically, the storage unit 10 records a reference waveform in which each of the current supplied to the power consuming device and the current supplied to the control board that supplies power to the plurality of power consuming devices is recorded in time series. Remember.

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 board 220 in time series via a current sensor described later. The abnormality determination unit 22 determines whether or not the acquired current value is abnormal. The determination result display unit 23 displays the result determined by the abnormality determination unit 22.

FIG. 27 is a diagram showing a hardware configuration example of the abnormal device determination device. The abnormality device determination device 1 includes a central processing device (Central Processing Unit: CPU) 2, a memory 3, an external storage device 4 such as a hard disk device (Hard Disk Drive: HDD), and a CD (Computer Disk) or DVD (Digital). A reading device 6 that reads information from a portable storage medium 5 such as a Versail Disk), an input device 7 such as a keyboard, mouse, and bar code reader, an output device 8 such as a display, and a communication network such as the Internet. This can be realized by a general computer provided with a communication device 9 that communicates with another computer via the computer, or a network system provided with a plurality of the computers. Needless to say, the reading device 6 may be capable of not only reading but also writing to the portable storage medium 5.

For example, the current acquisition unit 21, the abnormality determination unit 22, and the determination result display unit 23 included in the processing unit 20 load a predetermined program stored in the external storage device 4 into the memory 3 and execute it in the CPU 2. This is feasible, and the storage unit 10 can be realized by the CPU 2 using the memory 3 or the external storage device 4.

This predetermined program is downloaded from the portable storage medium 5 via the reading device 6 or from the network via the communication device 9 to the external storage device 4, and then loaded onto the memory 3 to be loaded into the CPU 2. May be executed by. Further, it may be directly loaded onto the memory 3 from the portable storage medium 5 via the reading device 6 or from the network via the communication device 9 and executed by the CPU 2.

FIG. 2 is a diagram showing an example of a power supply system circuit of a target device for determining an abnormal device. Electric power is supplied from the switchboard 100 to each motor, which is a drive motor, in the robot unit 300 via the control box 200. The robot unit 300 includes a plurality of drive motors, the rotation of each drive motor is transmitted to the speed reducer, and the operation shaft provided ahead thereof is operated. The reducer is a mechanism that reduces the input rotation speed and instead increases the torque.

In the example of FIG. 2, the control Box 200 includes a power supply circuit 210 and a control board 220, and the electric power supplied from the switchboard 100 is passed to the power supply circuit 210, passes through the control board 220, and is passed to each motor. Will be supplied. The control board 220 can supply power to a plurality of power consuming devices. As the drive motor, the first motor 321 and the second motor 322, the third motor 323, the fourth motor 324, the fifth motor 325, and the sixth motor 326 are mounted on the robot unit 300. ..

Each drive motor includes a first reduction gear 311, a second reduction gear 312, a third reduction gear 313, a fourth reduction gear 314, a fifth reduction gear 315, and a sixth reduction gear 316. Are connected respectively. Further, the speed reducers include a first working shaft 301, a second working shaft 302, a third working shaft 303, a fourth working shaft 304, a fifth working shaft 305, and a sixth working shaft 306. , Are connected respectively.

FIG. 3 shows an example of the current waveform for one manufacturing process for each motor. The current waveform shown in the present invention describes a waveform obtained by extracting an envelope from an AC waveform, but an effective value may be used in addition to the envelope, or a torque current value converted from an AC component may be used. can. The robot unit 300 operates by driving a motor connected to each operating axis according to the magnitude and frequency of the current output from the control board 220 according to a predetermined operation program. Therefore, it is known that these current waveforms are important physical quantities that reflect the deterioration state of the motor of each operating shaft of the robot, the speed reducer which is a mechanical component connected to the motor, and the operating shaft.

The current waveform 501 is a waveform of the current of the first motor 321 connected to the first operating 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 the current waveforms of the second motor 322 connected to the second operating shaft 302 and the third operating shaft 303, respectively. The current waveform of the three motors 323, the current waveform of the fourth motor 324 connected to the fourth operating shaft 304, the current waveform of the fifth motor 325 connected to the fifth operating shaft 305, and the sixth motor connected to the sixth operating shaft 306. It is the waveform of the current of 326.

FIG. 4 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 to the control board 220 via the power supply circuit 210, and the total current waveform details 602 show the breakdown of each axis in the total current waveform 601.

The total current waveform 601 and the total current waveform details 602 are equal current waveforms, indicating that the total current of each operating shaft is equal to the total current, and is expressed by the following equation (1). However, I _{w (t)} is the total current at a certain time t, and I _{i (t)} is the current of each operating shaft at a certain time t (hereinafter referred to as the individual device current as a physical quantity including the state of the operating shaft, the reducer, and the motor). , And i means the number of the operating axis.

... (1)
So far, we have focused on the current, but the condition that we can focus only on the current is that the voltage is constant at each location and the current and electric power are in a proportional relationship. In the conventional example and the first to third embodiments according to the present invention described later, the condition that the voltage is constant is used as a premise.

<Example of the conventional technique> Next, a configuration example of the abnormal device determination device by the conventional technique will be described with reference to FIGS. 5 to 8 for comparison. FIG. 5 shows an example of the processing flow of the abnormal device determination device of the conventional technique in a flowchart format, and FIG. 6 shows an example of the conventional technique and a block diagram of the abnormal device determination device according to the present invention.

FIG. 6A is a block diagram showing an example of a configuration related to learning of the abnormal device determination device 1, and FIG. 6B is a block showing an example of a configuration related to the operation of the abnormal device determination device 1. It is a figure. At the time of learning, the current acquisition unit 21 included in the processing unit 20 of the abnormality device determination device 1 acquires the total current value from the total current sensor 701. The total current sensor 701 is installed in the wiring between the switchboard 100 and the power supply circuit 210, or in the wiring between the power supply circuit 210 and the control board 220.

At the time of 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 shaft 1 current sensor 702 to the shaft 6 current sensor 703 are installed in the wiring between the control board 220 and each motor, respectively.

When the current value is acquired by the current acquisition unit 21, the current acquisition unit 21 stores all the waveforms as the reference waveform in the reference waveform storage unit 11. The stored reference waveform is a waveform such as the current waveform 501 to the current waveform 506, and it is necessary to satisfy the relationship that the total current of each operating shaft is equal to the total current.

The current acquisition unit 21 analyzes the correlation between the past failure record and the current data, calculates and sets an index (abnormality) and its threshold value for determining a failure (abnormality) using a predetermined algorithm.

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 as in the case of learning. The total current sensor 701 is installed in the wiring between the switchboard 100 and the power supply circuit 210, or in the wiring between the power supply circuit 210 and the control board 220, as in the case of learning.

Then, the current acquisition unit 21 passes the acquired total current to the abnormality determination unit 22, and the abnormality determination unit 22 acquires the reference waveform from the reference waveform storage unit 11 and determines whether or not the total current corresponds to the abnormality. When the determination result display unit 23 determines that the abnormality is abnormal, the determination result display unit 23 identifies that fact and the abnormal operating axis, creates display information, and displays the information on a display or the like of the device determination device 1 (not shown).

Here, in the processing during operation by the conventional technique, the abnormality determination unit 22 estimates the current waveform of the target device for abnormality determination from the acquired total current waveform during operation. In this process, it is calculated based on the mathematical formula shown in the following formula (2). However, the subscript 0 in parentheses indicates that it is the current waveform at the time of learning, and the subscript t in parentheses indicates a certain time during operation. Further, the following equation (2) is an example when the abnormality detection target is the first operating axis.

... (2)
That is, at the time of abnormality determination by the prior art, the current waveform of the current target device, that is, the individual device is calculated by subtracting the current waveform of the individual device other than the target at the time of learning from the total current of the current.

FIG. 7 is a diagram illustrating an operation current waveform acquired in an abnormality determination by the prior art. FIG. 7A shows the acquired total current waveform 611, and FIG. 7B shows an example of the current waveform 612 of the individual device 1 obtained by the above equation (2). ..

FIG. 8 shows an example of the current waveform for one manufacturing process for each motor during operation. The example shown in FIG. 8 is basically the same as the example at the time of learning shown in FIG. 3, but the current waveform 511 of the first operating shaft 301 is different from the current waveform 501. In this case, in order to detect an abnormality by the conventional technique, the current waveforms 512 to 516 during operation of the second operating shaft 302 to 306 are the same waveforms as the current waveforms 502 to 506 during learning, respectively. There is a need. If the waveforms are different, the current waveform 511 of the first operating shaft 301 cannot be accurately acquired, and the abnormality determination cannot be said to be an accurate determination. That is, it can be said that the abnormality of a plurality of devices cannot be detected.

FIG. 5 is a flowchart showing an example of the flow of abnormality determination processing by the prior art. This flow is configured by steps S001 to S009, the processing at the time of learning is the processing of steps S001 to S003, and the processing at the time of operation is the processing of steps S004 to S009.

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 shaft 1 current sensor 702 to the shaft 6 current sensor 703. ..

Then, the current acquisition unit 21 stores the current waveform (individual current waveform at the time of learning) of each axis (step S002). Specifically, the current acquisition unit 21 stores the entire waveform as a reference waveform in the reference waveform storage unit 11.

Then, the current acquisition unit 21 sets a threshold value of the degree of abnormality (correlation coefficient) for determining abnormality (step S003). Specifically, the current acquisition unit 21 analyzes the correlation between the past failure record and the current data, and calculates an index (abnormality) for determining a failure (abnormality) and its threshold value using a predetermined algorithm. Set. The above is the processing flow at the time of learning.

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

Then, the abnormality determination unit 22 removes from the acquired total operating current using the reference waveform of each axis that is not subject to abnormality detection (step S005). Specifically, the abnormality determination unit 22 acquires a reference waveform from the reference waveform storage unit 11 and subtracts it from the total current.

Then, the abnormality determination unit 22 calculates the degree of abnormality of the extracted waveform as 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 at the time of learning, and calculates the degree of abnormality.

Then, the abnormality determination unit 22 determines whether or not the degree of abnormality is equal to or higher than the threshold value (step S007). Specifically, the abnormality determination unit 22 determines whether or not the correlation coefficient calculated in step S006 is equal to or greater than the threshold value of the degree of abnormality set in step S003.

When the degree of abnormality is not equal to or higher than the threshold value (when "No" in step S007), the abnormality determination unit 22 determines that the abnormality is normal, and controls to step S004 to proceed to the abnormality detection of the next process. Return (step S008).

When the degree of abnormality is equal to or higher than the threshold value (when "Yes" in step S007), the abnormality determination unit 22 determines that it is abnormal (step S009).

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

<First Example> Next, the first embodiment according to the present invention will be described with reference to FIGS. 9 to 11. In the first embodiment, the configuration is basically the same as that of the above-mentioned prior art, but there are differences. Hereinafter, the difference will be mainly described.

FIG. 9 is a diagram showing an example of the processing flow of the abnormality determination processing (first embodiment). In the processing at the time of learning, after storing the current waveform of each axis, the current acquisition unit 21 sets a threshold value of the rate of change as the degree of abnormality to be determined as abnormal (step S103), which is different from the conventional technique. .. Specifically, the current acquisition unit 21 analyzes the correlation between the past failure record and the current data, sets the index for determining the failure (abnormality) as the rate of change of the current waveform, and uses a predetermined algorithm as the threshold value. Calculate and set.

Then, in the processing during operation, after acquiring the total current waveform for one process, the abnormality determination unit 22 performs multivariate analysis, for example, multiple regression analysis on the acquired total current during operation, and obtains the current waveform of each axis. The rate of change is calculated (step S105). Specifically, first, it is assumed that all the operating axes (individual devices) have changed from the time of learning, and the abnormality determination unit 22 constructs the equation shown in the following equation (3). However, α ₁ to α ₆ are rate of change indicating the rate of change of the current waveform of each individual device, and are unknown at the time of this processing.

... formula (3)
In the above equation (3), there are six unknowns (α ₁ to α ₆ ). The anomaly targeted in this embodiment is conditioned on the condition that this unknown is a value that does not change within one process. That is, it is a condition that the entire waveform becomes α _{i times in the event of an abnormality.} Under this condition, the above equation (3) holds at each time in the process. Therefore, for example, if the current value is acquired every second in the process for 15 seconds as shown in FIG. 3, the above equation (3) can create 15 pieces with different values of t. It is possible.

Since there are 15 equations for 6 unknown rate of change, it can be said that the _{rate of change α 1} to α _{6 can be solved from the viewpoint of simultaneous equations.} It is a feature of this embodiment that processing is performed using multiple regression analysis, which is a method for solving a plurality of unknowns, assuming that all individual devices are changed in this way. Further, in the present embodiment, the abnormality determination unit 22 is subjected to multiple regression analysis utilizing the fact that the sum of the products of the current supplied to the plurality of power consuming devices and the current change rate is equal to the current supplied to the control board. The current change rate is calculated.

When the degree of abnormality (rate of change) is equal to or higher than the threshold value (when “Yes” in step S007), the abnormality determination unit 22 determines that the abnormality is present, and the determination result display unit 23 determines the change rates α ₁ to α _6. Is displayed on the display unit (step S109).

FIG. 10 is a diagram in which the individual device current waveform is estimated using the calculated rate of change. The current waveform of each individual device is represented by the following equation (4).

... Equation (4)
Specifically, the current waveform 523 of shaft 3 shown in FIG. 10 is different from the current waveform 513 during operation as shown in FIG. 8, and has a alpha ₃ times the rate of change.

FIG. 11 is a diagram showing an example of a change rate display screen displayed by the determination result display unit. The rate of change α _i takes “1” as an initial value, and the current waveform at this time is equal to the learning current waveform shown in FIG. 3 and equal to the learning individual device waveform.

The display screen 800 includes a graph 801 showing the transition of the rate of change for each operating axis in process units. _{Here, the rate of change α ib} described in each graph of FIG. 11 indicates the threshold value of the degree of abnormality set in step S103 in the flowchart of the first embodiment shown in FIG. One plot of graph 801 in FIG. 11 shows one process, and one plot is generated for each process from step S004 to step S007 of the flowchart.

In summary, the abnormality determination unit 22 calculates the current change rate for each power consuming device by multivariate analysis using the current supplied to the control board acquired by the current acquisition unit 21 and the reference waveform, and the current change. When the rate exceeds the threshold value, it is determined that the power consumption device is abnormal. Further, the determination result display unit 23 displays the result of the determination by the abnormality determination unit 22.

In this embodiment, as shown in FIG. 11, it is possible to capture how the axis 1 and the axis 3 are changing at the same time by multivariate analysis, and it is possible to determine an abnormality of a plurality of devices, which could not be targeted by the conventional technology. The effect that is possible is obtained. In addition, the determination result display unit 23 can also obtain the effect that it is possible to observe how the transition is abnormal even if it is not determined to be abnormal. The above is the first embodiment of the present invention.

<Second Example> Next, a second embodiment according to the present invention will be described with reference to FIGS. 12 to 14. The second embodiment has basically the same configuration as the first embodiment described above, but there are differences. Hereinafter, the difference will be mainly described.

FIG. 12 is a diagram showing an example of the processing flow of the abnormality determination processing (second embodiment). The processing at the time of learning is the same as that of the first embodiment. In the processing during operation, after acquiring the total current waveform for one process, the rate of change is calculated by multivariate analysis. In that processing, a time window is set and the total current waveform during operation and the reference waveform are used for a predetermined period. There is a difference in that a part of the data is cut out and multivariate analysis is performed multiple times. That is, the current change rate is calculated for each section in which the reference waveform is divided into a plurality of sections by a predetermined time window, and the abnormality is determined.

As explained in the first embodiment, the number of equations (3) to be constructed in order to calculate the _{rate of change α 1} to α _{6 in multivariate analysis (multiple regression analysis) is an unknown number (α 1} to α). _In the case of 6, the number may be 6) or more. For example, if the current value is recorded every 1 second in the process for 15 seconds, the rate of change α _i can be calculated only with the data for 6 seconds. That is, multiple regression analysis is possible if the number of consecutive samples is equal to or greater than the number of operating axes.

In the second embodiment, it is assumed that the data at the time of learning is the current waveform shown in FIG. 3, and the current waveform at the time of abnormality is the state shown in FIG. However, in the actual processing, the current waveform group shown in FIG. 13 is not acquired from the sensor, and is described here only for the sake of explanation.

Specifically, the abnormality determination unit 22 first divides and extracts the total current waveform during operation in the minimum time width (time window) (step S205). In the above example, the minimum time width is 6 seconds, which can secure an unknown number of samples, but the minimum time width is not limited to this, and may be longer. Then, the abnormality determination unit 22 divides and extracts the total current waveform during learning by the minimum time width (time window) used in step S205 (step S206).

Then, the abnormality determination unit 22 calculates the rate of change of each axis and each time current by multiple regression analysis (step S207). Specifically, the abnormality determination unit 22 calculates the rate of change specified from the sample in the set time window.

According to the processes of steps S205 to S207 above, the rate of change is calculated by multiple regression analysis every 6 seconds, so that the in-process transition of the rate of change shown in FIG. 14 can be obtained. The rate of change is calculated by shifting every second and acquiring data for 6 seconds, so a discrete but gentle curve like a sine wave is drawn. The smaller the number of samples, the more dynamic the reaction of the rate of change can be obtained.

FIG. 14 is a diagram showing an example of a change rate display screen displayed by the determination result display unit. The display screen 810 includes a graph 811 showing the transition of the rate of change for each operating axis in time units. As shown in Graph 811, the rate of change increases only in the first half of the process on the first operating axis 301 and only in the second half of the process on the third operating axis 303, and it is possible to capture local abnormalities. ..

According to the invention according to the second embodiment, the abnormal state does not uniformly appear in the process, and even the abnormal state partially expressed in the process can be accurately captured. In particular, when the time required for one process is long, the accuracy of abnormality detection can be dramatically improved.

<Third Example> Next, a third embodiment according to the present invention will be described with reference to FIGS. 15 and 16. The third embodiment has basically the same configuration as the first embodiment described above, but there are differences. Hereinafter, the difference will be mainly described.

FIG. 15 is a diagram showing an example of the processing flow of the abnormality determination processing (third embodiment). The processing at the time of learning is basically the same as that of the first embodiment. The second threshold value is calculated to achieve an improvement in determination accuracy. That is, a threshold value (second threshold value) that serves as a predetermined detectability index is specified according to the reference waveform of the current supplied to the control board, and if the current change rate does not exceed the second threshold value, it is determined to be abnormal. By not doing so, erroneous judgment is prevented.

Specifically, after setting the threshold value of the degree of abnormality in step S103, a second degree of abnormality threshold considering variation is set from the current waveform during learning as a process during learning (step S304). In the present invention, since the current of each individual device is estimated from the total current, if the total current varies, there is a concern that the estimation accuracy of the current of each individual device also varies. The condition for avoiding this is that the amount of change in the current of each individual device becomes larger than the variation in the total current, which can be expressed by the following equation (5). However, SI _{w (0)} indicates the variation of the total current waveform during learning.

... Equation (5)

Deforming the above equation (5), when the abnormality degree threshold in consideration of the variation and alpha _ia, alpha _ia can be expressed by Equation (6).

... Equation (6)
_{The α ia} obtained by the equation (6) is determined to be abnormal when both the abnormality degree and the second abnormality degree threshold value are detected in the abnormality degree determination process during operation (step S307). Used in. That is, the quotient obtained by dividing the sum of the fluctuation amount of the reference waveform of the current supplied to the control board 220 and the current supplied to the power consuming device by the current supplied to the power consuming device is specified as the second abnormality degree threshold. can do.

FIG. 16 is a diagram showing an example of a change rate display screen displayed by the determination result display unit. The display screen 820 includes a graph 821 showing the transition of the rate of change for each operating axis in process units. _{Here, the rate of change α ib} described in each graph of FIG. 16 indicates the threshold value of the degree of abnormality set in step S103 in the flowchart of the third embodiment shown in FIG. The rate of change α _ia indicates the second abnormality degree threshold set in step S304 in the flowchart of the third embodiment shown in FIG. One plot of graph 821 in FIG. 16 shows one process, and one plot is generated for each process of steps S004 to S307 of the flowchart.

For example, with respect to the first operating axis, since the rate of change exceeds both the threshold value of the degree of abnormality and the threshold value of the second degree of abnormality, it is possible to determine that it is abnormal. However, as for the axis 3, α _ib is exceeded, but α _ia is not yet exceeded. If α _ia is not set, the third operating axis will be determined to be abnormal even at this point, but in reality it may be a false alarm due to variations in the total current. The invention according to the present embodiment has the effect of reducing false alarms due to sudden events such as variations in total current.

Further, as a further effect of the invention according to the third embodiment, if there is a concern that the individual device current is small with respect to the total current variation and that even if an abnormality occurs, it is buried in the variation, the individual device is individually specified in advance. It is also possible to take measures to install a current sensor only for the device. That is, it is an index that can be used for optimizing the number of sensors, and it can be said that it also has the effect of appropriately reducing the number of sensors while maintaining accuracy.

<Fourth Example> Next, a fourth embodiment according to the present invention will be described with reference to FIGS. 17 to 20. The fourth embodiment has basically the same configuration as the first embodiment described above, but there are differences. Hereinafter, the difference will be mainly described.

FIG. 17 is a diagram showing an example of the processing flow of the abnormality determination processing (fourth embodiment). In the fourth embodiment, measures are taken when the voltage supplied to each individual device is different. Specifically, at the time of learning, the current acquisition unit 21 calculates the voltage ratio, which is the ratio between each individual device and the main power supply voltage, by multiple regression analysis (step S403), and stores the individual voltage ratio of each axis. By setting (step S404), when the voltages supplied to the individual devices are different, the voltage ratio can be calculated without acquiring the voltage value.

FIG. 18 is a diagram showing the relationship between the total current waveform targeted for determining the abnormality in the fourth embodiment and the current waveform of each individual device. FIG. 18A is a graph of the total current, and FIG. 18B is an example of a graph in which the current waveforms of each individual device are stacked. Comparing FIGS. 18 (a) and 18 (b), the total current and the total current of each individual device do not match. This means that the total current cannot be expressed by simple addition of current waveforms because the voltage is different for each individual device.

In the first embodiment, the relational expression with current was established from the assumption that the total power is equal to the total power and the voltage is constant. In this embodiment, the relational expression that the total sum of the original electric powers is equal is used. Here, the method for obtaining the voltage ratio described above will be described. The voltage ratio k _i is a physical quantity represented by the following formula (7). However, V _w indicates the voltage at the total current acquisition location, and V _i indicates the voltage at each individual device current acquisition location.

... formula (7)

Using the relationship of the above equation (7), the relationship between the voltage ratio of each individual device and the current is shown by the equation (8).

... Equation (8)
Build equation (8) at each time of learning during the current waveform, by performing a multiple regression analysis, it is possible to calculate each individual device voltage ratio k _i.

FIG. 19 is a diagram in which the current waveform of an individual device is estimated using the calculated rate of change. When using the individual devices voltage k _i calculated calculating each individual device current waveform, and the waveform 541 of the first operating shaft in FIG. 19, a waveform 542 of the second operating shaft, and a waveform 543 of the third working axis, A waveform 544 of the fourth operating axis, a waveform 545 of the fifth operating axis, and a waveform 546 of the sixth operating axis are obtained. The vertical axis of each graph is the current x voltage ratio.

FIG. 20 is a diagram showing an example of the relationship between the total current waveform and the current waveform of each individual device. FIG. 20A shows the acquired total current waveform, and FIG. 20B shows an example of stacking graphs of current × voltage ratio of individual devices. Comparing FIG. 20 (a) and FIG. 20 (b), the sizes of the graphs are the same. Here, the mathematical formula used in the multiple regression analysis performed in S405 in the flowchart of FIG. 17 is shown as the following equation (9).

... formula (9)
The timing using the above equation (9), already the individual devices voltage ratio k _i for already calculated, it is possible to perform a multiple regression analysis with no problem.

As described above, according to the fourth embodiment, even when it is unclear whether the voltages of the individual devices are equal or not, the voltage ratio can be calculated by using the equation (8) without adding a voltage sensor. It can also be applied to devices with different voltages for individual devices while maintaining accuracy. That is, it can be said that the range of applicable devices can be expanded.

<Fifth Example> Next, a fifth embodiment according to the present invention will be described with reference to FIGS. 21 to 23. The fifth embodiment has basically the same configuration as the first embodiment described above, but there are differences. Hereinafter, the difference will be mainly described.

FIG. 21 is a diagram showing an example of a power supply system circuit of the target device according to the fifth embodiment. Other devices (components) such as cooling fans 231 and 232 and the non-excited electromagnetic brake 330 are further added to the control board 220 with respect to the configurations of the first to fourth embodiments.

FIG. 22 is a diagram showing an example of the processing flow of the abnormality determination processing (fifth embodiment). In the fifth embodiment, individual devices other than the main components and other devices that are difficult to measure even temporarily, such as during learning, are collectively handled to measure the other devices alone. It is possible to judge abnormal equipment without doing it.

Basically, the processing flow of the abnormality determination process according to the fifth embodiment is the same as the processing flow of the abnormality determination process according to the fourth embodiment. However, at the time of learning, the current acquisition unit 21 calculates an individual voltage ratio and a constant for each axis by multiple regression analysis (step S503), and stores the current waveform using the constant (residual) as the current of other devices (step S503). Step S504).

That is, in the abnormality determination process according to the fifth embodiment, the current of the individual device that is not measured when the _{individual voltage ratio k i} is obtained is expressed by the _{constant I else (0) and calculated.} The following equation (10) shows the relational expression _{of the individual voltage ratio k i} and the other individual equipment current I _{else (0).}

... formula (10)
Here, I _{else (0)} is a value including the current consumption of the cooling fans 231 and 232 of FIG. 21 and the non-excited electromagnetic brake 330.

The non-excited electromagnetic brake 330 is a brake generally applied to robots. This brake has a feature that the brake is applied when the electric power is not supplied, and the brake is not applied when the electric power is supplied. Therefore, it has the characteristic of consuming a certain amount of electric power when energized, and the dotted line arrow in FIG. 21 indicates only the direction of action of the brake. Constructs simultaneous equations necessary for multiple regression analysis with the above equation (10) can be calculated and the voltage ratio k _i, other individual device current I _{the else} a _(0).

FIG. 23 is a diagram showing an example of the relationship between the total current waveform and the graph in which the graphs of the current × voltage ratio of each individual device converted by the voltage ratio are stacked. FIG. 23 (a) shows the total current at the time of learning, FIG. 23 (b), each individual device current × voltage ratio which is translated using the voltage ratio k _i determined by the above equation (10) and the other individual equipment The graph which piled up the current I _{else (0) is shown.} Comparing FIG. 23 (a) and FIG. 23 (b), the sizes of both graphs are equal.

Here, the mathematical formula used in the multiple regression analysis in the flowchart of FIG. 22 is shown as equation (11).

... Equation (11)
Because of formula (11) already each individual device voltage ratio k _i is already calculated at the timing of using, it is possible to perform a multiple regression analysis with no problem, but resolution is poor, using the other discrete equipment current change rate alpha _{the else} , It is also possible to detect abnormalities in other individual devices. That is, the current acquisition unit 21 collectively considers the power consuming device whose reference waveform is not stored in the storage unit 10 and receives power from the control board 220 as the power consuming device, and calculates the voltage ratio and the rate of change. can do.

According to this embodiment, there is an effect that it is possible to accurately determine an abnormality even when a large number of individual devices are included in addition to the main devices. In addition, even when other individual devices change within one process, the above equations (10) and (11) can be used by using the average value using multiple waveforms of the same process. , Can produce the same effect.

<Sixth Example> Next, a sixth embodiment according to the present invention will be described with reference to FIGS. 24 to 26. The sixth embodiment has basically the same configuration as the first embodiment described above, but there are differences. Hereinafter, the difference will be mainly described.

FIG. 24 is a diagram showing a configuration example of an outline of the target device according to the sixth embodiment. The target device according to the sixth embodiment is a numerically controlled metal processing machine 400 having 4-axis operation (X-axis 431, Y-axis 432, Z-axis 433, spindle shaft 434), and a tool replacement for replacing the tool 461 as an accessory. A device (tool change shaft 475) is also attached.

FIG. 25 is a diagram showing a configuration example of an outline 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 board 411, a second control board 412, and a third control board 413. The power supply circuit 410 receives power from the switchboard 100. Each control board is supplied with power from the power supply circuit 410. An individual device is attached to each control board.

The first control board 411 includes a first motor 451, a first speed reducer 441 that decelerates its rotation, and a table X-axis 431 that slides in the X-axis direction by a force transmitted by the first speed reducer 441. Is attached. Further, on the first control board 411, a second motor 452, a second speed reducer 442 for decelerating the rotation thereof, and a table Y-axis 432 that slides in the Y-axis direction by the force transmitted by the second speed reducer 442. And is accompanied. Further, on the first control board 411, a third motor 453, a third speed reducer 443 that reduces the rotation thereof, and a tool Z-axis 433 that slides in the Z-axis direction by the force transmitted by the third speed reducer 443. And is accompanied.

The second control board 412 is accompanied by a fourth motor 454, a fourth speed reducer 444 that decelerates its rotation, and a spindle shaft 434 that rotates by a force transmitted from the fourth speed reducer 444.

The third control board 413 is accompanied by a fifth motor 495, a fifth speed reducer 485 that decelerates its rotation, and a tool exchange shaft 475 that rotates by the force transmitted from the fifth speed reducer 485.

FIG. 26 is a diagram showing an example of the processing flow of the abnormality determination processing (sixth embodiment). In the sixth embodiment, even when the power supply is branched and supplied to the control board, it is possible to estimate the current of the individual device and determine the abnormality at each location.

Basically, the processing flow of the abnormality determination processing according to the sixth embodiment is the same as the processing flow of the abnormality determination processing according to the first embodiment. However, at the time of learning, the current acquisition unit 21 acquires the total current and the current of each control board and the accompanying individual device (step S601), and stores the current waveform of each control board and each individual device in the reference waveform storage unit 11. (Step S602). Then, during operation, the abnormality determination unit 22 calculates the rate of change of each control board current by multiple regression analysis after acquiring the total current waveform for one process (step S004) (step S605), and the calculated rate of change. The current of each control board is calculated and acquired (step S606), and the rate of change of the current of each individual device is calculated by multiple regression analysis (step S607). That is, if there is a control board, recursive processing is performed as a nested structure.

That is, in the sixth embodiment, the relationship between the control board and the individual device shown in the first to fifth embodiments is expanded, and the same relationship is further established between the plurality of control boards and the power supply circuit. Utilizing this, the rate of change of individual devices via the control board is estimated from the original current.

Here, the flow of current waveform estimation of the table X-axis 431, the table Y-axis 432, and the tool Z-axis 433 from the power supply circuit 410 via the first control board 411 will be specifically described.

In FIG. 25, the current flowing through the wiring between the power distribution board 100 and the power supply circuit 410 is the total current I _{w (t)} , and the current flowing through the wiring between the power supply circuit 410 and each control board is the control board current _{ICi (t)} . (1) The current flowing through the wiring between the control board 411 and the first to third motors is defined as the individual device current _{Ii (t)} . Regarding these, the current acquisition unit 21 acquires the waveform at the time of learning by the current sensor and stores it in the reference waveform storage unit 11 as _{I w (0)} , _{ICi (0)} , and I _{i (0), respectively.}

During operation, the current acquisition unit 21 first acquires the total current I _{w (t)} , performs multiple regression analysis using the equation shown in the following equation (12), and calculates _{each control board current change rate α Ci.} ..

... formula (12)

Subsequently, the current waveform of the individual device attached to the first control board 411 is estimated using the value of _{α C1} × _{IC1 (0).} For the estimation, the following equation (13) in which the _{total current I w (t)} is replaced with α _C1 × _{IC1 (0) is used.}

... formula (13)
_{Since the rate of change α 1} to α ₃ of the current waveforms of the table X-axis 431, the table Y-axis 432, and the tool Z-axis 433 can be calculated from the above equation (13), the degree of abnormality of the individual device attached to the first control board 411 can be calculated. Can be calculated.

As described above, according to the numerical control metal processing machine 400 according to the sixth embodiment, even a device of a power supply system circuit branched in multiple layers is in operation as long as the reference waveform of the target line can be acquired. It has the effect that abnormality determination can be performed while achieving sensor cost reduction.

The present invention is not limited to the above embodiment, and includes various modifications. For example, the numerically controlled metal processing machine 400 may be another multi-axis controlled machine tool. Further, in the sixth embodiment described above, a nested structure having one layer of control boards is taken as an example, but the present invention is not limited to this, and a nested structure having a plurality of layers of control boards may be used.

Although multiple regression analysis is performed as an example of multivariate analysis, it is not limited to this, and other analysis methods such as principal component analysis may be performed.

Further, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment.

In addition, it is possible to add / delete / replace a part of the configuration of the embodiment with another configuration.

Further, each configuration, function, processing unit, processing means, etc. of the above-mentioned abnormal device determination device 1 may be realized by hardware, for example, by designing a part or all of them by an integrated circuit or the like. Further, each of the above configurations, functions, and the like may be realized by software by the processor interpreting and executing a program that realizes each function. Information such as programs, tables, and files that realize each function can be placed in a memory, a recording device such as a hard disk or SSD, or a recording medium such as an IC card, SD card, or DVD.

In addition, the control lines and information lines indicate those that are considered necessary for explanation, and do not necessarily indicate all control lines and information lines in the product. In reality, it may be considered that almost all configurations are connected to each other by a communication network, a bus, or the like.

The technique according to the present invention is not limited to the abnormal device determination device, and 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).

1 ... Abnormal device determination device, 10 ... Storage unit, 11 ... Reference waveform storage unit, 20 ... Processing unit, 21 ... Current acquisition unit, 22 ... Abnormality determination unit, 23. Judgment result display unit, 100 ... switchboard, 200 ... control Box, 300 ... robot unit, 301 ... first operating axis, 302 ... second operating axis, 303 ... Three operating axes, 304 ... 4th operating axis, 305 ... 5th operating axis, 306 ... 6th operating axis.

Claims (16)

1. Stores a reference waveform in which the current supplied to the power consuming device that consumes power during normal operation and the current supplied to the control board that supplies power to the plurality of power consuming devices are recorded in chronological order. With the memory
   A current acquisition unit that acquires the current supplied to the control board in chronological order during operation,
   An abnormality determination unit that calculates the current change rate for each power consuming device using the current supplied to the control board acquired by the current acquisition unit and the reference waveform, and determines an abnormality in the power consuming device. ,
   A determination result display unit that displays the result of the determination by the abnormality determination unit, and
   An abnormal device determination system characterized by being equipped with.
2. The abnormality device determination system according to claim 1.
   The abnormality determination unit determines an abnormality of a plurality of the power consuming devices by using the current change rate.
   An abnormal device judgment system characterized by this.
3. The abnormality device determination system according to claim 1.
   The abnormality determination unit calculates the current change rate by multivariate analysis.
   An abnormal device judgment system characterized by this.
4. The abnormality device determination system according to claim 1.
   The abnormality determination unit performs the current change rate by multiple regression analysis utilizing the fact that the sum of the products of the current supplied to the plurality of power consuming devices and the current change rate is equal to the current supplied to the control board. To calculate,
   An abnormal device judgment system characterized by this.
5. The abnormality device determination system according to claim 1.
   The abnormality determination unit performs the current change rate by multiple regression analysis utilizing the fact that the sum of the products of the current supplied to the plurality of power consuming devices and the current change rate is equal to the current supplied to the control board. Is calculated,
   In the multiple regression analysis, the current change rate is calculated for each section in which the reference waveform is divided into a plurality of sections by a predetermined time window.
   An abnormal device judgment system characterized by this.
6. The abnormality device determination system according to claim 1.
   The abnormality determination unit determines that the power consuming device is abnormal when the current change rate exceeds a predetermined threshold value.
   An abnormal device judgment system characterized by this.
7. The abnormality device determination system according to claim 1.
   The abnormality determination unit specifies a threshold value as a predetermined detectability index according to the reference waveform of the current supplied to the control board, and does not determine that the abnormality is abnormal if the current change rate does not exceed the threshold value. ,
   An abnormal device judgment system characterized by this.
8. The abnormality device determination system according to claim 1.
   The abnormality determination unit uses a quotient obtained by dividing the sum of the fluctuation amount of the reference waveform of the current supplied to the control board and the current supplied to the power consuming device by the current supplied to the power consuming device as a threshold value. If the current change rate does not exceed the threshold value, it is not determined to be abnormal.
   An abnormal device judgment system characterized by this.
9. The abnormality device determination system according to claim 1.
   The current acquisition unit calculates a voltage ratio for each power consuming device using the reference waveform of the current supplied to the control board and the reference waveform of the current supplied to the power consuming device.
   An abnormal device judgment system characterized by this.
10. The abnormality device determination system according to claim 1.
    The current acquisition unit utilizes the fact that the sum of the products of the reference waveform of the current supplied to the power consuming device and the voltage ratio of each power consuming device is equal to the current supplied to the control board. The voltage ratio for each power consuming device is calculated by the multiple regression analysis.
    An abnormal device judgment system characterized by this.
11. The abnormality device determination system according to claim 1.
    The current acquisition unit utilizes the fact that the sum of the products of the reference waveform of the current supplied to the power consuming device and the voltage ratio of each power consuming device is equal to the current supplied to the control board. The voltage ratio for each power consuming device was calculated by the multiple regression analysis.
    In the abnormality determination unit, the sum of the products of the current supplied to the plurality of power consuming devices, the current change rate, and the voltage ratio of each power consuming device becomes equal to the current supplied to the control board. The current rate of change is calculated by multiple regression analysis utilizing the fact that the current change rate is calculated.
    An abnormal device judgment system characterized by this.
12. The abnormality device determination system according to claim 1.
    The current acquisition unit collectively regards a power consuming device whose reference waveform is not stored in the storage unit and receives power supplied from the control board as a power consuming device, and supplies the power consuming device to the power consuming device. The voltage ratio for each power consuming device is obtained by multiple regression analysis utilizing the fact that the sum of the products of the reference waveform of the current and the voltage ratio for each power consuming device is equal to the current supplied to the control board. calculate,
    An abnormal device judgment system characterized by this.
13. The abnormality device determination system according to claim 1.
    The current acquisition unit collectively regards a power consuming device whose reference waveform is not stored in the storage unit and receives power supplied from the control board as a power consuming device, and supplies the power consuming device to the power consuming device. The voltage ratio for each power consuming device by multiple regression analysis utilizing the fact that the sum of the products of the reference waveform of the current and the voltage ratio for each power consuming device is equal to the current supplied to the control board. Is calculated,
    In the abnormality determination unit, the sum of the products of the current supplied to the plurality of power consuming devices, the current change rate, and the voltage ratio of each power consuming device becomes equal to the current supplied to the control board. The current change rate is calculated by multiple regression analysis utilizing the fact that the current change rate is calculated.
    An abnormal device judgment system characterized by this.
14. The abnormality device determination system according to claim 1.
    The power consuming device is a drive motor that operates the operating shaft of the robot.
    An abnormal device judgment system characterized by this.
15. The abnormality device determination system according to claim 1.
    The power consuming device is a drive motor that operates the operating shaft of the robot.
    The abnormality includes an abnormality of the drive motor and an abnormality of another device that affects the current waveform of the drive motor.
    An abnormal device judgment system characterized by this.
16. The abnormality device determination system according to claim 1.
    The one or more power consuming devices include the control board that supplies power to one or more other power consuming devices.
    The storage unit stores a reference waveform in which the current supplied to the other power consuming device is recorded in time series.
    The abnormality determination unit calculates the current change rate for the other power consuming device and determines the abnormality of the power consuming device.
    An abnormal device judgment system characterized by this.

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