Fault diagnosis of a selective compliance assembly robot arm manipulator based on the end joint motion analysis: Threshold algorithm and experiments

Fei, Xu; Yang, Zhong; Hu, Jian; Jiang, Guoping; Dai, Guohong

doi:10.1177/0142331217690222

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…Komal researched on robot fault diagnosis by establishing a fuzzy fault tree for the robot, the fault tree is an inverted tree-like logic causality diagram that connects events with logic gate symbols, fault tree analysis (FTA) is a top-down deductive failure analysis method to analyze undesired states in the system [8]. Xu et al determined the motion state of the robot by analyzing the motion signal of the robot arm and comparing the velocity curve and threshold of the robot end joint, and then carried out the fault diagnosis of the robot arm joint [9]. Ma et al proposed a linear adaptive observer to estimate the fault parameters with predefined error bounds for fault detection and isolation of robots [10].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of robot joint based on BP neural network

Yang

et al. 2022

Robotica

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Aiming at the problem of low accuracy of robot joint fault diagnosis, a fault diagnosis method of robot joint based on BP neural network is designed. In this paper, the UR10 robot is taken as the research object, and the end pose data of the robot are collected in real time. By injecting different joint errors and changing the sampling frequency, the joint fault database is collected and established, and the BP neural network is used for training to obtain the robot neural network fault diagnosis model. The fault diagnosis model can output the joint fault of the input end pose data. And we analyzed the influence of different joint angle errors and different training sets on the accuracy of joint fault diagnosis of the robot. The results show that when the sampling frequency is 250 Hz, the simulation result of joint fault diagnosis accuracy with the fault degree of 0.5° is 99.17%, and the experimental result is 97.87%. Compared with traditional data-driven methods, it has higher accuracy and diagnostic efficiency, and compared with existing machine learning methods, it also achieves a high accuracy while reducing the network complexity. The effectiveness of the BP neural network robot joint fault diagnosis method is verified by experiments.

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Section: Introductionmentioning

confidence: 99%

Fault diagnosis of robot joint based on BP neural network

Yang

et al. 2022

Robotica

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“…The Selective Compliance Assembly Robot Arm (SCARA) is a four-degree-of-freedom intelligent mechanical arm that is widely used in the production of plastics, automobiles, electronics, pharmaceuticals and foodstuffs [1], saving a lot of manpower and material resources in the transfer and assembly of components on the production line. However, its operational conditions also directly determine the safe operation * Author to whom any correspondence should be addressed.…”

Section: Introducementioning

confidence: 99%

SCARA mechanical fault identification based on WPM-SE+BPNN method

Wang¹,

Sun²,

Bai³

et al. 2022

Meas. Sci. Technol.

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The Selective Compliance Assembly Robot Arm (SCARA) is becoming increasingly important in industrial manufacturing and its operating condition directly determines the safe operation of production lines. However, the complex structure, the variety of mechanical faults and unstable movements of SCARA make fault identification extremely difficult. Therefore, this study proposed identification of SCARA mechanical faults based on wavelet packet multi-segment entropy (WPM-SE) +Back Propagation Neural Network (BPNN). First, the original vibration signal was decomposed into several sub-node signals by wavelet packet transform (WPT) and its envelope spectrum was obtained by Hilbert transform. Then, the envelope spectrum was divided equally into multiple intervals along the time axis, and the energy of each interval was calculated. Afterwards, the feature information of the envelope spectrum was obtained from the energy of each interval, which is defined as multi-segment entropy (M-SE). Where an envelope spectrum obtains a M-SE, and the number of segmentation intervals determines the value of the M-SE. Finally, a feature vector composed of the values of the M-SE was used as the feature input data of the BPNN for mechanical fault identification in SCARA. The BPNN has been tested to achieve an average recognition accuracy of 99.67% for both single mechanical faults and multiple mechanical faults. The results show that the WPM-SE method can effectively extract the feature information of the vibration signal and achieve fast and accurate identification of mechanical faults in SCARA.

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Low-Frequency Compensation of Piezoelectric Accelerometers for Motion Control Systems

2021

J. Electr. Eng. Technol.

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Fault diagnosis of a selective compliance assembly robot arm manipulator based on the end joint motion analysis: Threshold algorithm and experiments

Cited by 9 publications

References 19 publications

Fault diagnosis of robot joint based on BP neural network

Fault diagnosis of robot joint based on BP neural network

SCARA mechanical fault identification based on WPM-SE+BPNN method

Low-Frequency Compensation of Piezoelectric Accelerometers for Motion Control Systems

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