Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

IEEE Access

Yue Ma¹  Xiaohua Wu² 

[1] School of Automation, Northwestern Polytechnical University, Xi&x2019;an, China;

关键词: Rotating electrical machine;    fault classification;    feature extraction;    dimensionality reduction;    sparse representation;    collaborative representation;   

DOI  :  10.1109/ACCESS.2020.3029194

来源: DOAJ

【 摘 要 】

It is always a serious challenge to deal with the influence of noise for fault detection of rotating electrical machines under varying operating environments. To extract the most discriminative features for machine fault classification, a novel dimensionality reduction algorithm called joint sparsity and collaboration preserving projections (JSCPP) is proposed in this article, based on the platform of graph embedding framework. A joint combination of sparse representation (SR) and collaborative representation (CR) is used to construct the intrinsic and penalty graphs. SR emphasizes only on the dictionary atoms highly correlated to the query sample for local optimum while CR takes every atom into consideration for global optimum, thus applying a joint combination would make it benefit from the merits of both for graph construction. After JSCPP seeks the optimal projection directions by minimizing the intra-class compactness and maximizing the inter-class separability, a nearest neighbor classifier is then used. Through two sets of vibration data with white Gaussian noise interfered, the capability of JSCPP is demonstrated by effectively classifying different defect types and severity degrees of faulty bearings. The experimental results have also proved that the classification performance of the proposed method is much superior to the other compared algorithms in both accuracy rates and reliability.

【 授权许可】

Unknown