Adaptive clustering algorithm based on improved marine predation algorithm and its application in bearing fault diagnosis

Zhuanzhe Zhao; Mengxian Wang; Yongming Liu; Zhibo Liu; Yuelin Lu; Yu Chen; Zhijian Tu; Zhuanzhe Zhao; Mengxian Wang; Yongming Liu; Zhibo Liu; Yuelin Lu; Yu Chen; Zhijian Tu

doi:10.3934/era.2023359

Electronic Research Archive

2023, Volume 31, Issue 11: 7078-7103. doi: 10.3934/era.2023359

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Research article

Adaptive clustering algorithm based on improved marine predation algorithm and its application in bearing fault diagnosis

1.
School of Artificial Intelligence, Anhui Polytechnic University, Wuhu 241000, China
2.
Anhui Provincial Key Laboratory of Discipline Co-construction on Intelligent Equipment Quality and Reliability, Wuhu 241000, China
3.
School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China
4.
Center for Robot Performance Testing and Reliability Assessment, Anhui Polytechnic University, Wuhu 241000, China
5.
Anhui Provincial Key Laboratory of Electric Drive and Control, Anhui Polytechnic University, Wuhu 241000, China
6.
Wuhu Ceprei Robotics Industry Technology Research Institute Company Limited, Wuhu 241000, China

Received: 04 August 2023 Revised: 19 September 2023 Accepted: 26 September 2023 Published: 06 November 2023

In cluster analysis, determining the number of clusters is an important issue because there is less information about the most appropriate number of clusters in the real problem. Automatic clustering is a clustering method that automatically finds the most appropriate number of clusters and divides instances into the corresponding clusters. In this paper, a novel automatic clustering algorithm based on the improved marine predator algorithm (IMPA) and K-means algorithm is proposed. The new IMPA utilizes refracted opposition-based learning in population initialization, generates opposite solutions to improve the diversity of the population and produces more accurate solutions. In addition, the sine-cosine algorithm is incorporated to balance global exploration and local development of the algorithm for dynamic updating of the predator and prey population positions. At the same time, the Gaussian-Cauchy mutation is combined to improve the probability of obtaining the globally optimal solution. The proposed IMPA is validated with some benchmark data sets. The calculation results show that IMPA is superior to the original MPA in automatic clustering. In addition, IMPA is also used to solve the problem of fault classification of Xi*an Jiaotong University bearing data. The results show that the IMPA has better and more stable results than other algorithms such as the original MPA, whale optimization algorithm, fuzzy C-means and K-means in automatic clustering.
- fault diagnosis,
- automatic clustering,
- cluster validity index,
- marine predator algorithm,
- K-means clustering
Citation: Zhuanzhe Zhao, Mengxian Wang, Yongming Liu, Zhibo Liu, Yuelin Lu, Yu Chen, Zhijian Tu. Adaptive clustering algorithm based on improved marine predation algorithm and its application in bearing fault diagnosis[J]. Electronic Research Archive, 2023, 31(11): 7078-7103. doi: 10.3934/era.2023359

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Abstract

In cluster analysis, determining the number of clusters is an important issue because there is less information about the most appropriate number of clusters in the real problem. Automatic clustering is a clustering method that automatically finds the most appropriate number of clusters and divides instances into the corresponding clusters. In this paper, a novel automatic clustering algorithm based on the improved marine predator algorithm (IMPA) and K-means algorithm is proposed. The new IMPA utilizes refracted opposition-based learning in population initialization, generates opposite solutions to improve the diversity of the population and produces more accurate solutions. In addition, the sine-cosine algorithm is incorporated to balance global exploration and local development of the algorithm for dynamic updating of the predator and prey population positions. At the same time, the Gaussian-Cauchy mutation is combined to improve the probability of obtaining the globally optimal solution. The proposed IMPA is validated with some benchmark data sets. The calculation results show that IMPA is superior to the original MPA in automatic clustering. In addition, IMPA is also used to solve the problem of fault classification of Xi*an Jiaotong University bearing data. The results show that the IMPA has better and more stable results than other algorithms such as the original MPA, whale optimization algorithm, fuzzy C-means and K-means in automatic clustering.

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