CN114609453A - Robot joint current anomaly detection method and device based on statistical process control - Google Patents

Robot joint current anomaly detection method and device based on statistical process control Download PDF

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CN114609453A
CN114609453A CN202210091853.5A CN202210091853A CN114609453A CN 114609453 A CN114609453 A CN 114609453A CN 202210091853 A CN202210091853 A CN 202210091853A CN 114609453 A CN114609453 A CN 114609453A
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industrial robot
joint
current
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joint current
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肖雷
马百腾
张坤
李正平
刘凯强
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Donghua University
Shanghai Robot Industrial Technology Research Institute Co Ltd
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Donghua University
Shanghai Robot Industrial Technology Research Institute Co Ltd
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Abstract

The invention relates to a robot joint current abnormity detection method and device based on statistical process control, which comprises the following steps: acquiring a current signal of a joint of the industrial robot; acquiring the period of a joint current signal of the industrial robot by using an autocorrelation analysis method; calculating and eliminating the phase difference between the current monitoring signal of the joint of the industrial robot and the reference signal by using a cross-correlation analysis method; and according to the period of acquiring the current signal, calculating the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot in the period, and performing anomaly detection by using a statistical process control method. Compared with the prior art, the method has the advantages of easy control, convenient formation of time sequence data, long-term tracking, accurate detection and the like.

Description

Robot joint current anomaly detection method and device based on statistical process control
Technical Field
The invention relates to the field of industrial robot state monitoring, in particular to a robot joint current abnormity detection method and device based on statistical process control.
Background
With the continuous promotion of the automation process, the industrial robot plays more and more important roles, and the production efficiency is greatly improved. However, the functional requirements of the industrial robot are continuously increased, the structure tends to be complex, and the industrial robot can break down after long-time operation. The state of the industrial robot is monitored, whether the equipment is abnormal or not is detected, and economic loss and casualties are avoided. The traditional industrial robot abnormity detection method collects signals such as vibration signals, torque signals and acoustic emission signals, Chinese patent application (application publication No. CN 111975784A) discloses a joint robot fault diagnosis method based on current and vibration signals, an equiangular sampling time sequence is obtained by utilizing the collected joint motor signals, the filtered vibration signals are subjected to equiangular sampling, finally, Fourier transform is carried out on an equiangular sampling sequence of the vibration signals to obtain a robot joint vibration signal order spectrum, the robot joint vibration signal order spectrum is analyzed, and robot joint fault diagnosis is realized. But the signal of above-mentioned collection is because influenced by factors such as the position of gathering, and the signal quality of gathering is difficult to guarantee, and its sensor is with high costs relatively in addition, and current signal can directly acquire from industrial robot switch board, gathers conveniently, and is with low costs.
The constant-speed motor is researched more in fault diagnosis and state monitoring through the current signals, and after the stator current is obtained, the fault diagnosis and state monitoring of the motor can be completed through a current spectrum analysis method. However, the joints of the industrial robot mainly comprise a servo motor and a reducer, and the research on the condition monitoring and fault diagnosis of the reducer and the servo motor based on current signals is relatively less. Chinese patent application (application publication No. CN 108638128A) discloses a real-time anomaly monitoring method and system for an industrial robot, which uses collected joint current signals to calculate the positioning deviation, current boundary, range and variance thereof, and then compares the signals with the normal interval to complete anomaly detection. But this method can only handle smoother signals. Compared with the current signals of the servo motor and the constant-speed motor, the current signals of the servo motor have periodicity along with the periodic change of the action, the frequency components are complex, the amplitude changes violently, and if a conventional method is used for carrying out abnormal detection according to the current information, the detection result is inaccurate.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a robot joint current abnormity detection method and device based on statistical process control.
The purpose of the invention can be realized by the following technical scheme:
a robot joint current abnormity detection method based on statistical process control is characterized by comprising the following steps:
s1, acquiring a current signal of the joint of the industrial robot;
s2, obtaining the period of the current signal of the joint of the industrial robot by using an autocorrelation analysis method according to the current signal of the joint of the industrial robot;
s3, calculating and eliminating the phase difference between the current monitoring signal and the reference signal of the joint of the industrial robot by using a cross-correlation analysis method according to the current signal of the joint of the industrial robot;
s4, calculating the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot in the period according to the period obtained in the step S2 and the current signal with the phase difference eliminated in the step S3;
and S5, calculating a control threshold range by using a statistical process control method according to the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot, and performing abnormity judgment according to the control threshold range.
Further, the statistical process control method used in step S5 includes the following specific steps:
a1, calculating a control upper limit and a control lower limit of a control chart based on the total energy of the industrial robot joint current reference signal, wherein the calculation formula is as follows:
Figure BDA0003489502650000021
Figure BDA0003489502650000022
Figure BDA0003489502650000023
Figure BDA0003489502650000024
wherein, tiRepresents the i-th energy of the reference signal,
Figure BDA0003489502650000025
the method comprises the steps of representing the average value of total energy in a period of a joint current reference signal of the industrial robot in a normal state, sigma representing the standard deviation of the total energy in the period of the joint current reference signal of the industrial robot in the normal state, UCL representing an upper control limit, and LCL representing a lower control limit;
a2, judging whether the total energy of the joint current monitoring signal of the industrial robot is between the upper control limit and the lower control limit, if so, judging that the industrial robot is not abnormal; and if not, judging that the industrial robot is abnormal.
Further, in the step S1, the method for acquiring the current signal is to pass U, V lines of cables in the industrial robot control cabinet through a current transformer, and collect U, V phase currents of each joint.
Further, the autocorrelation analysis method of step S2 specifically includes steps of obtaining an autocorrelation function of the joint current signal of the industrial robot, normalizing the sequence length to eliminate the influence of time lag, and finally obtaining an index distance between maximum peak values of the autocorrelation function of the joint current signal of the industrial robot, so as to obtain the number of data points of one period of the joint current signal of the industrial robot and the time of one period of the joint current signal of the industrial robot;
the computational expression of the autocorrelation function is as follows:
Figure BDA0003489502650000031
where h is the order, μ is the mean of the sequence, and x is the time series of the input signal.
Further, the cross-correlation function of the cross-correlation analysis method in step S3
Figure BDA0003489502650000032
The calculation expression of (a) is as follows:
Figure BDA0003489502650000033
wherein, (x) represents joint current monitoring signals of the industrial robot, g (x) represents joint current reference signals of the industrial robot in a normal state, and m represents phase difference between the signals.
A robot joint current abnormity detection device based on statistical process control comprises a memory and a processor; the memory for storing a computer program; the processor, when executing the computer program, is configured to implement the following method:
s1, acquiring a current signal of the joint of the industrial robot;
s2, obtaining the period of the current signal of the joint of the industrial robot by using an autocorrelation analysis method according to the current signal of the joint of the industrial robot;
s3, calculating and eliminating the phase difference between the current monitoring signal and the reference signal of the joint of the industrial robot by using a cross-correlation analysis method according to the current signal of the joint of the industrial robot;
s4, calculating the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot in the period according to the period obtained in the step S2 and the current signal with the phase difference eliminated in the step S3;
and S5, calculating a control threshold range by using a statistical process control method according to the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot, and performing abnormity judgment according to the control threshold range.
Further, the statistical process control method used in step S5 includes the following specific steps:
a1, calculating a control upper limit and a control lower limit of a control chart based on the total energy of the industrial robot joint current reference signal, wherein the calculation formula is as follows:
Figure BDA0003489502650000041
Figure BDA0003489502650000042
Figure BDA0003489502650000043
Figure BDA0003489502650000044
wherein, tiRepresents the i-th energy of the reference signal,
Figure BDA0003489502650000045
the method comprises the steps of representing the average value of total energy in a period of a joint current reference signal of the industrial robot in a normal state, sigma representing the standard deviation of the total energy in the period of the joint current reference signal of the industrial robot in the normal state, UCL representing an upper control limit, and LCL representing a lower control limit;
a2, judging whether the total energy of the joint current monitoring signal of the industrial robot is between the upper control limit and the lower control limit, if so, judging that the industrial robot is not abnormal; and if not, judging that the industrial robot is abnormal.
Further, in the step S1, the method for acquiring the current signal is to pass U, V lines of cables in the industrial robot control cabinet through a current transformer, and collect U, V phase currents of each joint.
Further, the autocorrelation analysis method of step S2 specifically includes steps of obtaining an autocorrelation function of the joint current signal of the industrial robot, normalizing the sequence length to eliminate the influence of time lag, and finally obtaining an index distance between maximum peak values of the autocorrelation function of the joint current signal of the industrial robot, so as to obtain the number of data points of one period of the joint current signal of the industrial robot and the time of one period of the joint current signal of the industrial robot;
the computational expression of the autocorrelation function is as follows:
Figure BDA0003489502650000046
where h is the order, μ is the mean of the sequence, and x is the time series of the input signal.
Further, the cross-correlation function of the cross-correlation analysis method in step S3
Figure BDA0003489502650000047
The calculation expression of (a) is as follows:
Figure BDA0003489502650000048
wherein, (x) represents joint current monitoring signals of the industrial robot, g (x) represents joint current reference signals of the industrial robot in a normal state, and m represents phase difference between the signals.
Compared with the prior art, the invention has the following advantages:
the invention obtains the period of the current signal by an autocorrelation analysis method and a cross-correlation analysis method, eliminates the phase difference between a reference signal and a monitoring signal, determines the threshold value of the abnormal judgment by a statistical process control method, processes the complex frequency components in the current signal of the complex servo motor, avoids the interference caused by the violent change of the amplitude by the statistical process control method, improves the accuracy of the abnormal detection, completes the abnormal detection only by collecting the current signal, has low cost and small calculated amount, and has good economic benefit.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a time domain diagram of a joint current reference signal 1_ U in a normal state of the industrial robot.
Fig. 3 is a time domain diagram of a joint current reference signal 2_ U in a normal state of the industrial robot.
Fig. 4 is a time domain diagram of a joint current reference signal 3_ U in a normal state of the industrial robot.
Fig. 5 is a time domain diagram of a joint current reference signal 4_ U in a normal state of the industrial robot.
Fig. 6 is a time domain diagram of the joint current reference signal 5_ U in a normal state of the industrial robot.
Fig. 7 is a time domain diagram of the joint current reference signal 6_ U in a normal state of the industrial robot.
Fig. 8 is a time domain diagram of a joint current reference signal 1_ V of an industrial robot in a normal state.
Fig. 9 is a time domain diagram of the joint current reference signal 2_ V in the normal state of the industrial robot.
Fig. 10 is a time domain diagram of the joint current reference signal 3_ V in a normal state of the industrial robot.
Fig. 11 is a time domain diagram of a joint current reference signal 4_ V of an industrial robot in a normal state.
Fig. 12 is a time domain diagram of a joint current reference signal 5_ V of an industrial robot in a normal state.
Fig. 13 is a time domain diagram of the joint current reference signal 6_ V in a normal state of the industrial robot.
Fig. 14 is an image of an autocorrelation function of a joint current reference signal 1_ U in a normal state of an industrial robot.
Fig. 15 is a signal segmentation image of the joint current reference signal 1_ U in a normal state of the industrial robot.
Fig. 16 is a time domain image comparison diagram of the normal state current reference signal 1_ U and the fault state joint current monitoring signal 1_ U of the industrial robot.
Fig. 17 is a time domain image comparison diagram of the normal state current reference signal 1_ U and the fault state joint current monitoring signal 1_ U after the phase difference is eliminated.
Fig. 18 is a control diagram of the industrial robot current signal 1_ U.
Fig. 19 is a control diagram of the industrial robot current signal 1_ V.
Fig. 20 is a control diagram of the industrial robot current signal 2_ U.
Fig. 21 is a control diagram of the industrial robot current signal 2_ V.
Fig. 22 is a control diagram of the industrial robot current signal 3_ U.
Fig. 23 is a control diagram of the industrial robot current signal 3_ V.
Fig. 24 is a control diagram of the industrial robot current signal 4_ U.
Fig. 25 is a control diagram of the industrial robot current signal 4_ V.
Fig. 26 is a control diagram of the industrial robot current signal 5_ U.
Fig. 27 is a control diagram of the industrial robot current signal 5_ V.
Fig. 28 is a control diagram of the industrial robot current signal 6_ U.
Fig. 29 is a control diagram of the industrial robot current signal 6_ V.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1:
the embodiment provides a robot joint current abnormality detection method based on statistical process control, as shown in fig. 1, including the following steps:
step S1, acquiring current signals of the joints of the industrial robot,
step S2, obtaining the period of the joint current signal of the industrial robot by using an autocorrelation analysis method;
step S3, calculating and eliminating the phase difference between the industrial robot joint current monitoring signal and the reference signal by using a cross-correlation analysis method;
step S4, calculating the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot in the period according to the period obtained in the step S2;
and step S5, performing anomaly detection by using a statistical process control method according to the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot.
In this embodiment, step S1 is specifically as follows:
u, V phase cables in the industrial robot control cabinet penetrate through a current transformer, and U, V phase current of each joint is collected. The industrial robot is a six-axis series robot, simultaneously collects current signals of six joints, collects current signals of a normal state and a fault state, has a sampling frequency of 10kHz, and has 200000 sampling points. At the moment, a 12-dimensional current signal of the industrial robot in a normal state and a 12-dimensional current signal of the industrial robot in a fault state can be obtained. The specific data of the current signal are shown in table 1 and table 2, and the time domain diagram of the current signal is shown in fig. 2-fig. 13. In this embodiment, the current reference signal in the normal state and the current monitoring signal in the fault state are selected to verify the validity thereof.
TABLE 1 Joint Current reference Signal data of Industrial robot under Normal State
Figure BDA0003489502650000071
TABLE 2 Joint Current monitoring Signal data under Industrial robot Fault conditions
Figure BDA0003489502650000072
In this embodiment, step S2 is specifically as follows:
and solving an autocorrelation function of the joint current signal of the industrial robot, normalizing the sequence length to eliminate the influence of time lag, and finally solving an index distance between the maximum peak values of the autocorrelation function of the joint current signal of the industrial robot to obtain the number of data points of one period of the joint current signal of the industrial robot and the time of one period of the joint current signal of the industrial robot. The calculated autocorrelation image is shown in fig. 14. The distance between the two peaks was calculated to be 28627 data points (i.e. 2.8627 seconds period of the industrial robot joint current signal). Namely, the cycle point number of the joint current signal 1_ U in the normal state of the industrial robot is 28627 data points. The data division of the normal state joint current signal 1_ U of the industrial robot is performed by the calculated number of cycle points, as shown in fig. 15. The collected joint current signal 1_ U of the industrial robot in the normal state obtained by analysis comprises 6 complete periodic current signals, and the signal of the 7 th period is incomplete. The follow-up study was based on a 6 full cycle analysis of the industrial robot normal state joint current signal 1_ U. And calculating joint current signals of the industrial robot in normal state and fault state of other dimensions in the same way, obtaining the same result, namely the cycle point number of the joint current signals is 28627 data points, and performing development analysis on the joint current signals of the industrial robot in normal state based on 6 complete cycles in follow-up research.
In this embodiment, step S3 is specifically as follows:
when the fault state joint current monitoring signal of the industrial robot and the normal state joint current reference signal of the industrial robot are analyzed and compared, a phase difference exists between the two signals, and analysis is inconvenient. In order to eliminate the phase difference between the industrial robot joint current monitoring signal and the industrial robot normal state joint current reference signal, the industrial robot joint current monitoring signal and the industrial robot normal state joint current reference signal are subjected to cross-correlation calculation to find out the lag difference between the signals. Wherein the cross-correlation function
Figure BDA0003489502650000081
The calculation expression of (c) is as follows:
Figure BDA0003489502650000082
wherein f (x) and g (x) represent joint current monitoring signals of the industrial robot and joint current reference signals of the industrial robot in a normal state, and m represents a phase difference between the signals. After the phase difference between the joint current monitoring signal of the industrial robot and the joint current reference signal of the industrial robot in the normal state is obtained through cross-correlation function calculation, the joint current reference signal of the industrial robot in the normal state is used as a reference, the phase movement of the joint current monitoring signal of the industrial robot is completed according to the calculation result of the phase difference, and the phase difference between the joint current monitoring signal of the industrial robot and the joint current reference signal of the industrial robot in the normal state is eliminated.
As shown in fig. 16. Through the comparison of time domain diagrams, the phase difference exists between the normal state joint current reference signal of the industrial robot and the fault state joint current monitoring signal of the industrial robot, so that the analysis is inconvenient. The phase difference between the normal state joint current reference signal 1_ U of the industrial robot and the fault state joint current monitoring signal 1_ U of the industrial robot is calculated to be 5294 (namely, the phase time difference between the normal state joint current signal 1_ U of the industrial robot and the fault state joint current monitoring signal 1_ U of the industrial robot is 0.5294 seconds) through a cross-correlation analysis method. Namely, the phase of the joint current monitoring signal 1_ U in the fault state of the industrial robot is shifted to the left by 5294 data points by taking the joint current reference signal 1_ U in the normal state of the industrial robot as a reference, as shown in fig. 17. The normal state joint current reference signal 1_ U of the industrial robot and the fault state joint current monitoring signal 1_ U of the industrial robot have no phase difference in time domain, and follow-up analysis is facilitated. After the phase difference is eliminated, time domain images can be compared through the normal state joint current reference signals of the industrial robot and the fault state joint current monitoring signals of the industrial robot, whether abnormity exists or not is observed, and qualitative analysis is conducted. The phase differences between the normal signal and the fault signal of the industrial robot joint current signal 1_ V "" 2_ U "" 2_ V "" 3_ U "" 3_ V "" 4_ U "" 4_ V "" 5_ U "" 5_ V "" 6_ U "" 6_ V "" are 5291, 5329, 5328, 5056, 5057, -944, -944, -10566, -10566, 1863 and 1863 respectively, the phase differences are moved according to the phase point number differences, the time domain phase differences are eliminated, the time domain image comparison is carried out, and the qualitative analysis is carried out.
In the present embodiment, the calculation results of step S4 are shown in table 3, and S1 to S6 represent the first to sixth cycles.
TABLE 3 Total energy of normal state reference current signal and fault state joint monitoring current signal of industrial robot in period
S1 S2 S3 S4 S5 S6
Reference signal 60922.1 57793.6 64101.6 70648.9 61572.4 58183.5
Monitoring signals 55817 67459.1 61972.2 56103.3 65147.4 64068.4
In this embodiment, step S5 specifically includes the following steps:
step A1, calculating the control upper limit and the control lower limit of the control chart based on the total energy of the joint current reference signal of the industrial robot in the normal state, wherein the calculation formula is as follows:
Figure BDA0003489502650000091
Figure BDA0003489502650000092
Figure BDA0003489502650000093
Figure BDA0003489502650000094
wherein, tiRepresents the i-th energy of the reference signal,
Figure BDA0003489502650000095
the average value of the total energy in the period of the joint current reference signal of the industrial robot in the normal state is represented, sigma represents the standard deviation of the total energy in the period of the joint current reference signal of the industrial robot in the normal state, UCL represents the upper control limit, and LCL represents the lower control limit. Finally, the upper control limit, UCL, and the lower control limit, LCL, were calculated to be 75198.45 and 49208.93.
A2, judging whether the total energy of the joint current monitoring signal of the industrial robot in the fault state is between the upper control limit and the lower control limit, if so, judging that the industrial robot is not abnormal; and if not, judging that the industrial robot is abnormal.
Control diagrams of the joint monitoring current signals "1 _ V", "2 _ U", "2 _ V", "3 _ U", "3 _ V", "4 _ U", "4 _ V", "5 _ U", "5 _ V", "6 _ U", "6 _ V" in the fault state of the industrial robot are shown in fig. 18 to 29. The joint monitoring current signals '2 _ V', '4 _ U', '4 _ V', '5 _ U', '5 _ V', '6 _ U' and '6 _ V' of the industrial robot in the fault state are obtained through analysis and exceed the upper control limit, and the industrial robot is abnormal. The situation that the second joint of the industrial robot is abnormal and the current signal monitored due to the problem of sensor arrangement has a clipping phenomenon is found through field inspection, and due to the transmission relation of joint motion, the fault signal is amplified and transmitted to the 4, 5 and 6 axes due to fluctuation caused by faults. Therefore, the method for detecting the abnormality of the joint current signal of the industrial robot based on the statistical process control successfully completes the abnormality detection of the industrial robot.
Example 2:
the embodiment provides a robot joint current abnormity detection device based on statistical process control, which comprises a memory and a processor; a memory for storing a computer program; a processor for, when executing a computer program, implementing the method of:
step S1, acquiring a current signal of the joint of the industrial robot;
s2, acquiring the period of the current signal of the joint of the industrial robot by using an autocorrelation analysis method according to the current signal of the joint of the industrial robot;
step S3, calculating and eliminating the phase difference between the current monitoring signal and the reference signal of the joint of the industrial robot by using a cross-correlation analysis method according to the current signal of the joint of the industrial robot;
step S4, calculating the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot in the period according to the period obtained in the step S2 and the current signal with the phase difference eliminated in the step S3;
and step S5, calculating a control threshold range by using a statistical process control method according to the total energy of the industrial robot joint current monitoring signal and the total energy of the industrial robot joint current reference signal, and performing abnormity judgment according to the control threshold range.
Example 3:
this embodiment provides a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the statistical process control-based robot joint current abnormality as mentioned in embodiment 1 of the present invention, and any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. A robot joint current abnormity detection method based on statistical process control is characterized by comprising the following steps:
s1, acquiring a current signal of the joint of the industrial robot;
s2, obtaining the period of the current signal of the joint of the industrial robot by using an autocorrelation analysis method according to the current signal of the joint of the industrial robot;
s3, calculating and eliminating the phase difference between the current monitoring signal and the reference signal of the joint of the industrial robot by using a cross-correlation analysis method according to the current signal of the joint of the industrial robot;
s4, calculating the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot in the period according to the period obtained in the step S2 and the current signal with the phase difference eliminated in the step S3;
and S5, calculating a control threshold range by using a statistical process control method according to the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot, and performing abnormity judgment according to the control threshold range.
2. The method for detecting robot joint current abnormality based on statistical process control according to claim 1, wherein the statistical process control method used in step S5 includes the following steps:
a1, calculating a control upper limit and a control lower limit of a control chart based on the total energy of the industrial robot joint current reference signal, wherein the calculation formula is as follows:
Figure FDA0003489502640000011
Figure FDA0003489502640000012
Figure FDA0003489502640000013
Figure FDA0003489502640000014
wherein, tiRepresents the i-th energy of the reference signal,
Figure FDA0003489502640000015
the method comprises the steps of representing the average value of total energy in a period of a joint current reference signal of the industrial robot in a normal state, sigma representing the standard deviation of the total energy in the period of the joint current reference signal of the industrial robot in the normal state, UCL representing an upper control limit, and LCL representing a lower control limit;
a2, judging whether the total energy of the joint current monitoring signal of the industrial robot is between the upper control limit and the lower control limit, if so, judging that the industrial robot is not abnormal; if not, judging that the industrial robot is abnormal.
3. The method for detecting abnormal robot joint current based on statistical process control as claimed in claim 1, wherein in step S1, the method for obtaining the current signal is to pass U, V lines of cables in the industrial robot control cabinet through a current transformer, and to collect U, V phase current of each joint.
4. The method for detecting robot joint current abnormality based on statistical process control according to claim 1, wherein the autocorrelation analysis of step S2 includes the specific steps of calculating an autocorrelation function of an industrial robot joint current signal, normalizing the sequence length to eliminate the effect of time lag, and finally calculating an index distance between the maximum peak values of the autocorrelation function of the industrial robot joint current signal to obtain the number of data points of one period of the industrial robot joint current signal and the time of one period of the industrial robot joint current signal;
the computational expression of the autocorrelation function is as follows:
Figure FDA0003489502640000021
where h is the order, μ is the mean of the sequence, and x is the time series of the input signal.
5. The method for detecting robot joint current abnormality based on statistical process control according to claim 1, wherein the cross-correlation function of the cross-correlation analysis method in step S3
Figure FDA0003489502640000022
The calculation expression of (a) is as follows:
Figure FDA0003489502640000023
wherein, (x) represents joint current monitoring signals of the industrial robot, g (x) represents joint current reference signals of the industrial robot in a normal state, and m represents phase difference between the signals.
6. A robot joint current abnormity detection device based on statistical process control is characterized by comprising a memory and a processor; the memory for storing a computer program; the processor, when executing the computer program, is configured to implement the following method:
s1, acquiring a current signal of the joint of the industrial robot;
s2, obtaining the period of the current signal of the joint of the industrial robot by using an autocorrelation analysis method according to the current signal of the joint of the industrial robot;
s3, calculating and eliminating the phase difference between the current monitoring signal and the reference signal of the joint of the industrial robot by using a cross-correlation analysis method according to the current signal of the joint of the industrial robot;
s4, calculating the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot in the period according to the period obtained in the step S2 and the current signal with the phase difference eliminated in the step S3;
and S5, calculating a control threshold range by using a statistical process control method according to the total energy of the joint current monitoring signal of the industrial robot and the total energy of the joint current reference signal of the industrial robot, and performing abnormity judgment according to the control threshold range.
7. The device for detecting abnormal robot joint current based on statistical process control according to claim 6, wherein the statistical process control method used in step S5 comprises the following steps:
a1, calculating a control upper limit and a control lower limit of a control chart based on the total energy of the industrial robot joint current reference signal, wherein the calculation formula is as follows:
Figure FDA0003489502640000031
Figure FDA0003489502640000032
Figure FDA0003489502640000033
Figure FDA0003489502640000034
wherein, tiRepresents the i-th energy of the reference signal,
Figure FDA0003489502640000035
the method comprises the steps of representing the average value of total energy in a period of a joint current reference signal of the industrial robot in a normal state, sigma representing the standard deviation of the total energy in the period of the joint current reference signal of the industrial robot in the normal state, UCL representing an upper control limit, and LCL representing a lower control limit;
a2, judging whether the total energy of the joint current monitoring signal of the industrial robot is between the upper control limit and the lower control limit, if so, judging that the industrial robot is not abnormal; and if not, judging that the industrial robot is abnormal.
8. The device for detecting abnormal current of robot joints based on statistical process control as claimed in claim 6, wherein in step S1, the method for obtaining the current signal is to pass U, V lines of cables in the control cabinet of the industrial robot through a current transformer, and to collect U, V phase current of each joint.
9. The robot joint current abnormality detection device based on statistical process control according to claim 6, wherein the autocorrelation analysis of step S2 specifically comprises the steps of obtaining an autocorrelation function of the industrial robot joint current signal, normalizing the sequence length to eliminate the effect of time lag, and finally obtaining an index distance between the maximum peaks of the autocorrelation function of the industrial robot joint current signal to obtain the number of data points of one period of the industrial robot joint current signal and the time of one period of the industrial robot joint current signal;
the computational expression of the autocorrelation function is as follows:
Figure FDA0003489502640000041
where h is the order, μ is the mean of the sequence, and x is the time series of the input signal.
10. The device for detecting robot joint current abnormality based on statistical process control according to claim 6, wherein the cross-correlation function of the cross-correlation analysis method in step S3
Figure FDA0003489502640000042
The calculation expression of (a) is as follows:
Figure FDA0003489502640000043
wherein, (x) represents joint current monitoring signals of the industrial robot, g (x) represents joint current reference signals of the industrial robot in a normal state, and m represents phase difference between the signals.
CN202210091853.5A 2022-01-26 2022-01-26 Robot joint current anomaly detection method and device based on statistical process control Pending CN114609453A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117428760A (en) * 2023-10-10 2024-01-23 无锡蔚动智能科技有限公司 Joint module control system and method based on artificial intelligence

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117428760A (en) * 2023-10-10 2024-01-23 无锡蔚动智能科技有限公司 Joint module control system and method based on artificial intelligence
CN117428760B (en) * 2023-10-10 2024-03-29 无锡蔚动智能科技有限公司 Joint module control system and method based on artificial intelligence

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