A Simultaneous Fault Diagnosis Method Based on Cohesion Evaluation and Improved BP-MLL for Rotating Machinery



Zhang, Yixuan, Yang, Rui ORCID: 0000-0002-5634-5476, Huang, Mengjie, Han, Yu, Wang, Yiqi, Di, Yun, Su, Dongke and Lu, Qidong
(2021) A Simultaneous Fault Diagnosis Method Based on Cohesion Evaluation and Improved BP-MLL for Rotating Machinery. SHOCK AND VIBRATION, 2021. pp. 1-12.

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Abstract

<jats:p>In this paper, an improved simultaneous fault diagnostic algorithm with cohesion-based feature selection and improved backpropagation multilabel learning (BP-MLL) classification is proposed to localize and diagnose different simultaneous faults on gearbox and bearings in rotating machinery. Cohesion evaluation algorithm selects high sensitivity feature parameters from time and frequency domain in high-dimensional vectors to construct low-dimensional feature vectors. The BP-MLL neural network is utilized for fault diagnosis by classifying the feature vectors. An effective global error function is proposed in BP-MLL neural network by modifying distance function to improve both generalization ability and fault diagnostic ability of full-labeled and nonlabeled situations. To demonstrate the effectiveness of the proposed method, simultaneous fault diagnosis experiments are conducted via wind turbine drivetrain diagnostics simulator (WTDDS). The experiment results show that the proposed method has better overall performance compared with conventional BP-MLL algorithm and some other learning algorithms.</jats:p>

Item Type: Article
Uncontrolled Keywords: Bioengineering, 7 Affordable and Clean Energy
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 06 Jun 2022 15:50
Last Modified: 15 Mar 2024 15:43
DOI: 10.1155/2021/7469691
Open Access URL: https://www.hindawi.com/journals/sv/2021/7469691/
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3155962