Application of Improved Jellyfish Search algorithm in Rotate Vector reducer fault diagnosis

Xiaoyan Wu; Guowen Ye; Yongming Liu; Zhuanzhe Zhao; Zhibo Liu; Yu Chen; Xiaoyan Wu; Guowen Ye; Yongming Liu; Zhuanzhe Zhao; Zhibo Liu; Yu Chen

doi:10.3934/era.2023250

Electronic Research Archive

2023, Volume 31, Issue 8: 4882-4906. doi: 10.3934/era.2023250

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Research article Special Issues

Application of Improved Jellyfish Search algorithm in Rotate Vector reducer fault diagnosis

1.
School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China
2.
Chery New Energy Automotive Co., Ltd, Wuhu 241002, China
3.
School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China
4.
Anhui Provincial Key Laboratory of Discipline Co-construction on Intelligent Equipment Quality and Reliability, Wuhu 241000, China

Received: 16 May 2023 Revised: 14 June 2023 Accepted: 29 June 2023 Published: 11 July 2023

In order to overcome the low accuracy of traditional Extreme Learning Machine (ELM) network in the performance evaluation of Rotate Vector (RV) reducer, a pattern recognition model of ELM based on Ensemble Empirical Mode Decomposition (EEMD) fusion and Improved artificial Jellyfish Search (IJS) algorithm was proposed for RV reducer fault diagnosis. Firstly, it is theoretically proved that the torque transmission of RV reducer has periodicity during normal operation. The characteristics of data periodicity can be effectively reflected by using the test signal periodicity characteristics of rotating machinery and EEMD. Secondly, the Logistic chaotic mapping of population initialization in JS algorithm is replaced by tent mapping. At the same time, the competition mechanism is introduced to form a new IJS. The simulation results of standard test function show that the new algorithm has the characteristics of faster convergence and higher accuracy. The new algorithm was used to optimize the input layer weight of the ELM, and the pattern recognition model of IJS-ELM was established. The model performance was tested by XJTU-SY bearing experimental data set of Xi'an Jiaotong University. The results show that the new model is superior to JS-ELM and ELM in multi-classification performance. Finally, the new model is applied to the fault diagnosis of RV reducer. The results show that the proposed EEMD-IJS-ELM fault diagnosis model has higher accuracy and stability than other models.
Citation: Xiaoyan Wu, Guowen Ye, Yongming Liu, Zhuanzhe Zhao, Zhibo Liu, Yu Chen. Application of Improved Jellyfish Search algorithm in Rotate Vector reducer fault diagnosis[J]. Electronic Research Archive, 2023, 31(8): 4882-4906. doi: 10.3934/era.2023250

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Abstract

In order to overcome the low accuracy of traditional Extreme Learning Machine (ELM) network in the performance evaluation of Rotate Vector (RV) reducer, a pattern recognition model of ELM based on Ensemble Empirical Mode Decomposition (EEMD) fusion and Improved artificial Jellyfish Search (IJS) algorithm was proposed for RV reducer fault diagnosis. Firstly, it is theoretically proved that the torque transmission of RV reducer has periodicity during normal operation. The characteristics of data periodicity can be effectively reflected by using the test signal periodicity characteristics of rotating machinery and EEMD. Secondly, the Logistic chaotic mapping of population initialization in JS algorithm is replaced by tent mapping. At the same time, the competition mechanism is introduced to form a new IJS. The simulation results of standard test function show that the new algorithm has the characteristics of faster convergence and higher accuracy. The new algorithm was used to optimize the input layer weight of the ELM, and the pattern recognition model of IJS-ELM was established. The model performance was tested by XJTU-SY bearing experimental data set of Xi'an Jiaotong University. The results show that the new model is superior to JS-ELM and ELM in multi-classification performance. Finally, the new model is applied to the fault diagnosis of RV reducer. The results show that the proposed EEMD-IJS-ELM fault diagnosis model has higher accuracy and stability than other models.

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