Industrial robot health management system and method combining time domain and frequency domain analysis

Abstract

The present application relates to the field of industrial robots, in particular to a kind of industrial robot health management system and method combined with time domain and frequency domain analysis.The present application uses the following technical scheme: using dynamic time warping algorithm to align benchmark current data and real-time feedback current data, then similarity measurement is carried out by the way of calculating Euclidean distance, judge whether industrial robot is in normal operating state, after judging that industrial robot exists fault, through high-pass filtering to instruction speed data and actual response speed data, and through the way of spectrum diagram, judge the specific fault of industrial robot;The beneficial effects of the present application are that: through data alignment, the accurate judgment of whether the operating state of industrial robot is normal can be realized;Through the way of high-pass filtering and spectrum diagram, the accurate judgment of the fault type of industrial robot is realized, and the health management efficiency of industrial robot is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial robots, in particular to an industrial robot health management system and method combining time domain and frequency domain analysis. BACKGROUND

[0002] In the diagnosis of industrial robot health management, similarity measurement is usually calculated using Euclidean distance. The use of Euclidean distance has a prerequisite that two time series are completely aligned on the time axis. However, in actual applications, the running data of industrial robots may be offset on the time axis due to differences in sensor sampling rate, mechanical delay, external interference and other factors. This inconsistency on the time axis will cause the result calculated by Euclidean distance to be unable to accurately reflect the actual similarity between signals, resulting in a large deviation.

[0003] In addition, the running data of industrial robots is affected by various working condition information, such as load changes, operation modes, etc. These factors will cause the spectral characteristics of the signals to change. If the running data affected by the working condition information is directly subjected to spectral analysis, a large amount of noise and interference may be introduced, resulting in inaccurate extraction of spectral characteristics. SUMMARY

[0004] The purpose of the present application is to provide an industrial robot health management system and method combining time domain and frequency domain analysis to solve the problems of data misalignment and inability to perform frequency domain analysis on running data in the current diagnosis process of industrial robots.

[0005] To achieve the above purpose, the present application adopts the following technical solution: an industrial robot health management method combining time domain and frequency domain analysis, comprising the following steps:

[0006] S01, first collect the feedback current of the industrial robot under multiple normal operating states to obtain baseline feedback current data and construct a baseline data set.

[0007] S02, collect the feedback current of the actual running industrial robot in real time to obtain a real-time feedback current data sequence within a running period.

[0008] S03, call out the baseline feedback current data of the same operating state of the actual running industrial robot in step S02 from the baseline data set in step S01, and perform data alignment with the real-time feedback current data sequence in step S02.

[0009] S04, calculate the Euclidean distance of the aligned reference feedback current data and real-time feedback current data in step S03, and determine whether the calculated Euclidean distance exceeds the set threshold value, if it exceeds the set threshold value, it is considered that the actual running industrial robot has a fault, and step S05 is entered, if it does not exceed the set threshold value, it is considered that the actual running industrial robot is in normal operation state.

[0010] S05, for the case considered to have a fault in step S04, the instruction speed data and the actual response speed data of the industrial robot in the time period of the real-time feedback current data are obtained, and the instruction speed data and the actual response speed data are converted from time domain signal to frequency domain signal respectively to obtain the frequency spectrum of the instruction speed data and the actual response speed data; finally, the frequency spectrum of the actual response speed data is compared with the frequency spectrum of the instruction speed data to determine the fault type of the actual running industrial robot.

[0011] Specifically, in step S05, when comparing the frequency spectrum of the actual response speed data with the frequency spectrum of the instruction speed data to determine the fault type of the actual running industrial robot, the following determination method is used:

[0012] S51, if there is a vibration peak in the high frequency part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a bearing fault.

[0013] S52, if there is a vibration peak in the main frequency part and the first harmonic part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a misalignment problem.

[0014] S53, if there is a vibration peak in the main frequency part and the high harmonic part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a dynamic balance problem.

[0015] S54, if there is a vibration peak in the gear meshing frequency and its harmonic part and sideband frequency part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a bevel gear problem.

[0016] S55, if there is a vibration peak in the low frequency part and the sideband frequency part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a loose installation problem.

[0017] Specifically, in step S05, before converting the instruction speed data and the actual response speed data from the time domain signal to the frequency domain signal, the instruction speed data and the actual response speed data are subjected to high-pass filtering processing to remove the working condition information.

[0018] Specifically, in step S03, when aligning the reference feedback current data and the real-time feedback current data, a dynamic time warping algorithm is specifically used.

[0019] An industrial robot health management system combining time domain and frequency domain analysis includes a reference database, a feedback current acquisition module, a data alignment module, an Euclidean distance calculation module, a speed data acquisition module, a spectrum diagram display module, and a fault type output module. The reference database is used to store the reference data set collected by the above method. The feedback circuit acquisition module is used to execute step S02 of the above method. The data alignment module is used to execute step S03 of the above method. The Euclidean distance calculation module is used to execute step S04 of the above method. The speed data acquisition module is used to execute step S05 of the above method to acquire the instruction speed data and the actual response speed data of the industrial robot in the time period in which the real-time feedback current data is located. The spectrum diagram display module is used to display the spectrum diagram of the instruction speed data and the actual response speed data obtained in step S05 of the above method. The fault type output module is used to output the fault type existing in the actual running industrial robot obtained in step S05 of the above method.

[0020] The beneficial effects of the present application are that: by using the dynamic time warping algorithm to align the reference current data and the real-time feedback current data, and then calculating the Euclidean distance to measure the similarity, the accuracy of judging whether the industrial robot is running normally is realized. After judging that the industrial robot has a fault, the instruction speed data and the actual response speed data are subjected to high-pass filtering, and the specific fault existing in the industrial robot is judged through the spectrum diagram, so that the health management efficiency of the industrial robot and the accuracy of fault judgment are improved. BRIEF DESCRIPTION OF DRAWINGS

[0021] ATTACHMENT Figure 1 The system connection schematic diagram of the industrial robot health management system combining time domain and frequency domain analysis in the embodiment. DETAILED DESCRIPTION

[0022] Embodiment 1, an industrial robot health management method combining time domain and frequency domain analysis, includes the following steps:

[0023] S01, first collect the feedback current of the industrial robot under multiple normal running states to obtain reference feedback current data and construct a reference data set.

[0024] S02, collecting real-time feedback current data of an actual running industrial robot in a running cycle.

[0025] S03, calling out the reference feedback current data of the same running state of the actual running industrial robot from the reference data set in step S01, and aligning the data with the real-time feedback current data sequence in step S02.

[0026] S04, calculating the Euclidean distance of the aligned reference feedback current data and real-time feedback current data in step S03, and determining whether the calculated Euclidean distance exceeds the set threshold value, if it exceeds the set threshold value, it is considered that the actual running industrial robot has a fault, and step S05 is entered, if it does not exceed the set threshold value, it is considered that the actual running industrial robot is in normal running state.

[0027] S05, for the case considered to have a fault in step S04, the instruction speed data and the actual response speed data of the industrial robot in the time period of the real-time feedback current data are obtained, and the instruction speed data and the actual response speed data are converted from time domain signal to frequency domain signal to obtain the frequency spectrum of the instruction speed data and the actual response speed data; finally, the frequency spectrum of the actual response speed data is compared with the frequency spectrum of the instruction speed data to determine the fault type of the actual running industrial robot.

[0028] Specifically, in step S05, when comparing the frequency spectrum of the actual response speed data with the frequency spectrum of the instruction speed data to determine the fault type of the actual running industrial robot, the following determination method is used:

[0029] S51, if there is a vibration peak in the high frequency part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a bearing fault.

[0030] S52, if there are vibration peaks in the main frequency part and the first harmonic part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a misalignment problem.

[0031] S53, if there are vibration peaks in the main frequency part and the high harmonic part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a dynamic balance problem.

[0032] S54, if there are vibration peaks in the gear meshing frequency and its harmonic part and sideband frequency part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a bevel gear problem.

[0033] S55, if vibration peaks exist in both the low frequency part and the sideband frequency part of the spectrum of the actual response speed data compared with the spectrum of the command speed data, it is determined that the industrial robot has a mounting looseness fault.

[0034] Specifically, in step S05, before converting the command speed data and the actual response speed data from time domain signals to frequency domain signals, the command speed data and the actual response speed data are first subjected to high-pass filtering processing to remove the working condition information.

[0035] Specifically, in step S03, when aligning the reference feedback current data and the real-time feedback current data, a dynamic time warping algorithm is specifically used.

[0036] Meanwhile, for the above method, the embodiment also provides an industrial robot health management system combining time domain and frequency domain analysis, referring to Figure 1 which comprises a reference database, a feedback current acquisition module, a data alignment module, an Euclidean distance calculation module, a speed data acquisition module, a spectrum diagram display module and a fault type output module, the reference database is used to store the reference data set collected by the above method, the feedback circuit acquisition module is used to execute step S02 of the above method, the data alignment module is used to execute step S03 of the above method, the Euclidean distance calculation module is used to execute step S04 of the above method, the speed data acquisition module is used to execute step S05 of the above method to acquire the command speed data and the actual response speed data of the industrial robot in the time period in which the real-time feedback current data is located, the spectrum diagram display module is used to display the spectrum diagram of the command speed data and the actual response speed data obtained in step S05 of the above method, and the fault type output module is used to output the fault type existing in the actual running industrial robot obtained in step S05 of the above method.

[0037] Of course, the above is only a preferred embodiment of the present application, and does not limit the use range of the present application, so that any equivalent change in the principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for industrial robot health management combining time and frequency domain analysis, characterized in that, The method comprises the following steps: S01, collecting feedback current of the industrial robot in multiple normal operating states to obtain baseline feedback current data and construct a baseline data set; S02, collecting feedback current of the actual operating industrial robot to obtain real-time feedback current data in a running cycle; S03, calling out the baseline feedback current data of the same operating state of the actual operating industrial robot from the baseline data set in step S01, and aligning the real-time feedback current data in step S02; S04, calculating the Euclidean distance of the aligned baseline feedback current data and real-time feedback current data in step S03, and determining whether the calculated Euclidean distance exceeds the set threshold, if it exceeds the set threshold, it is considered that the actual operating industrial robot has a fault, and step S05 is entered, if it does not exceed the set threshold, it is considered that the actual operating industrial robot is in normal operating state; S05, for the case considered to have a fault in step S04, the instruction speed data and the actual response speed data of the industrial robot in the time period of the real-time feedback current data are obtained, and the instruction speed data and the actual response speed data are converted from time domain signals to frequency domain signals to obtain the frequency spectrum of the instruction speed data and the actual response speed data; finally, the frequency spectrum of the actual response speed data is compared with the frequency spectrum of the instruction speed data to determine the fault type of the actual operating industrial robot, and the following determination method is used: S51, if there is a vibration peak in the high frequency part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a bearing fault; S52, if there is a vibration peak in the main frequency part and the first harmonic part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a misalignment fault; S53, if there is a vibration peak in the main frequency part and the high harmonic part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a dynamic balance fault; S54, if there is a vibration peak in the gear meshing frequency and its harmonic part and the sideband frequency part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a bevel gear fault; S55, if there is a vibration peak in the low frequency part and the sideband frequency part of the frequency spectrum of the actual response speed data compared with the frequency spectrum of the instruction speed data, it is determined that the industrial robot has a loose installation fault.

2. The industrial robot health management method combining time domain and frequency domain analysis according to claim 1, characterized in that: In step S05, before converting the instruction speed data and the actual response speed data from time domain signals to frequency domain signals, the instruction speed data and the actual response speed data are subjected to high-pass filtering to remove interference signals in the working condition information.

3. The industrial robot health management method combining time domain and frequency domain analysis according to claim 1, characterized in that: In step S03, when aligning the baseline feedback current data and the real-time feedback current data, a dynamic time warping algorithm is used.

4. An industrial robot health management system combining time domain and frequency domain analysis, characterized by: The system comprises a reference database, a feedback current acquisition module, a data alignment module, a Euclidean distance calculation module, a rotating speed data acquisition module, a spectrum diagram display module and a fault type output module. The reference database is used to store the reference data set collected in step S01 according to any one of claims 1-3. The feedback current acquisition module is used to execute step S02 according to any one of claims 1-3. The data alignment module is used to execute step S03 according to any one of claims 1-3. The Euclidean distance calculation module is used to execute step S04 according to any one of claims 1-3. The rotating speed data acquisition module is used to acquire the command rotating speed data and the actual response rotating speed data of the industrial robot in the time period in which the real-time feedback current data is acquired in step S05 according to any one of claims 1-3. The spectrum diagram display module is used to display the spectrum diagram of the command rotating speed data and the actual response rotating speed data obtained in step S05 according to any one of claims 1-3. The fault type output module is used to output the fault type existing in the actual running industrial robot obtained in step S05 according to any one of claims 1-3.

Images