Fault diagnosis on railway vehicle bearing based on fast extended singular value decomposition packet. (February 2020)

• Record Type: Journal Article
• Authors: Huang, Yan; Huang, Chenguang; Ding, Jianming; Liu, Zechao
• Is Part Of: Measurement. Volume 152(2020)
• Publication Date: 2020-02
• Subjects: Railway vehicle -- Bearing diagnosis -- Singular value decomposition package -- Signal decomposition; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107277
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Recently, a new signal decomposition method called singular value decomposition package (SVDP) has been proposed to extract the resonance band excited by the bearing defect. As an emerging method, some disadvantages limit its applicability on industrial bearing diagnosis. To improve the performance of SVDP, an extended SVDP and its fast computation is proposed in this paper. The main improvements of the proposed method are that extending the subcomponent amount and modified the reconstruction of Hankel matrix to enhance the decomposition precision and flexibility. A set of simulated signal are used to analyze the performance and characteristic of the proposed method. Moreover a set of faulty data collected from running test rig with consideration of practical interference of wheel-rail excitement are studied to testify the effectiveness of the proposed method. The results show that the proposed method is capable of extracting the resonance band excited by bearing defect with distinguished performance.