A rolling bearing fault diagnosis technique based on recurrence quantification analysis and Bayesian optimization SVM

• Published in: Applied soft computing Vol. 156; p. 111506
• Main Authors: Wang, Bing, Qiu, Wentao, Hu, Xiong, Wang, Wei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2024
• Subjects: Bayesian optimization algorithm; Fault diagnosis; Rolling bearing; RQA; SVM; Fault diagnosis; RQA; SVM; Bayesian optimization algorithm; Rolling bearing
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2024.111506

A rolling bearing fault diagnosis technique is proposed based on Recurrence Quantification Analysis (abbreviated as RQA) and Bayesian optimized Support Vector Machine (abbreviated as RQA-Bayes-SVM). Firstly, analyzing the vibration signal with recurrence plot and the nonlinear feature parameters are extracted with RQA, constructing a feature matrix describing the fault mode and fault degree comprehensively. Finally, Bayesian optimization algorithm is introduced for searching the best penalty factor C and kernel function parameter g of SVM and establishing an optimal Bayes-SVM model. Bearing datasets from CWRU is imported for diagnosis on fault mode and fault degree. The results show that the technique presents a good performance on fault mode diagnosis as well as fault degree distinction. Compared with common k-Nearest Neighbor (abbreviated as KNN) and Random Forest (abbreviated as RF) diagnosis models, Bayes-SVM has the best accuracy and stability, which indicates a potential value for engineering applications. •Feature extraction based on RQA is proposed.•BO is employed in SVM optimization.•RQA-Bayes-MSVM diagnosis technique is proposed.•CWRU bearing datasets is introduced for instance analysis.