Dimensionality reduction method based on similarity balance discriminant projection for bearing fault diagnosis. (1st October 2022)

• Record Type: Journal Article
• Authors: Shi, Mingkuan; Ding, Chuancang; Shi, Juanjuan; Jiang, Xingxing; Huang, Weiguo; Zhu, Zhongkui
• Is Part Of: Measurement science & technology. Volume 33:Number 10(2022)
• Publication Date: 2022-10-01
• Subjects: fault diagnosis -- dimensionality reduction -- feature extraction -- data mining; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac7437
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: For data-driven intelligent diagnosis, comprehensive mining of the information in the data is a key issue and a great challenge. Knowledge of how to discover potentially useful information in the data is particularly critical to increase the accuracy of fault pattern recognition. The structural and discriminative information in the data present a potential direction from which to solve the issue and overcome the challenge. In this study, a similarity balance discriminant projection (SBDP) algorithm is proposed, by incorporating an optimized support vector machine (SVM) and then developing a fault diagnosis model for rolling bearing fault diagnosis. To address the defects of unbalanced adjacency relations and non-strictly monotonic between-class weight functions, SBDP reconstructs new within-class and between-class adjacency graphs by fusing structural and category information, thus effectively preserving the intrinsic manifold structure information of high-dimensional feature data. The effectiveness and suitability of the proposed model is demonstrated for two kinds of bearing diagnosis applications. The application results indicate that SBDP is better able to extract features representing the intrinsic information of faults and the optimized SVM is effective in identifying the fault types with high accuracy.