Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway. (10th September 2017)

• Record Type: Journal Article
• Authors: Chen, Hongtian; Jiang, Bin; Lu, Ningyun
• Other Names: Liu Wanquan Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2017(2017)
• Publication Date: 2017-09-10
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2017/8937356
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Incipient faults in high-speed railway have been rarely considered before developing into faults or failures. In this paper, a new data-driven incipient fault estimate (FE) methodology is proposed under multivariate statistics frame, which incorporates with Kullback-Leibler divergence (KLD) in information domain and neural network approximation in machine learning. By defining one sensitive fault indicator (SFI), the incipient fault amplitude can be precisely estimated. According to the experimental platform of China Railway High-speed 2 (CRH2), the proposed incipient FE algorithm is examined, and the more sensitivity and accuracy to tiny abnormality are demonstrated. Followed by the incipient FE results, several factors on FE performance are further analyzed.