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Independent vector analysis based on binary grey wolf feature selection and extreme learning machine for bearing fault diagnosis

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Abstract

This paper develops a new architecture for bearing fault diagnosis based on independent vector analysis, feature selection, and extreme learning machines classifiers. The suggested method applied to vibration signals includes the following steps: First, the independent vector analysis is introduced to separate vibration signal components from each other. Second, statistical parameters are extracted from all the obtained sources. Then three binary optimisation algorithms such as binary bat algorithm, binary particle swarm optimisation and binary grey wolf optimisation are employed for feature selection one by one. Finally, three classifiers based on extreme learning, artificial neural networks and random forest are used to perform the classification step. The obtained results show that independent vector analysis followed by feature selection based on binary grey wolf optimisation and classification using an extreme learning machine provides an optimal input vector which contains only five features for each sample and a very small misdiagnosis rate equal to 0.76%. The obtained results also prove that the suggested methodology gives the best classification results and high visibility compared to the other studied approaches.

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Data availability

The used dataset in this paper is downloaded in November 2020 from the Dynamic and Identification Research Group (DIRG) at ftp://ftp.polito.it/people/DIRG_BearingData/.

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Acknowledgements

The authors would like to express sincere thanks to the Dynamic and Identification Research Group (DIRG) in the Department of Mechanical and Aerospace Engineering at Politecnico di Torino, Italy for providing the bearing vibration data which is used in this paper.

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  1. LABGET Laboratory, Electrical Engineering Department, Larbi Tebessi University, Tebessa, Algeria

    Chouaib Souaidia, Tawfik Thelaidjia & Salah Chenikher

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Correspondence to Chouaib Souaidia.

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Souaidia, C., Thelaidjia, T. & Chenikher, S. Independent vector analysis based on binary grey wolf feature selection and extreme learning machine for bearing fault diagnosis. J Supercomput 79, 7014–7036 (2023). https://doi.org/10.1007/s11227-022-04931-4

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