Paper Info
Title
Fault Diagnosis of Train Plug Door Based on a Hybrid Criterion for IMFs Selection and Fractional Wavelet Package Energy Entropy
Abstract
The train plug door is the only way for passengers getting on and off. Its failures will make train operation ineffective. Taking the developed digital signal processing technologies into consideration, a data-driven diagnosis method for train plug doors is proposed based on sound recognition. First, a novel preprocessing method based on empirical mode decomposition and hybrid intrinsic mode functions (IMFs) selection criterion is proposed. The selected significant IMFs are used to reconstruct the signals. Inspired by the idea of fractional calculus, novel entropy named fractional wavelet package decomposition energy entropy (FWPDE) is proposed. Finally, multi-class support vector machine is used for classification and validation. Experimental results indicate that the proposed preprocessing method is of great significance to extract effective FWPDE features. In addition, FWPDE is more powerful in comparison with the classical wavelet package decomposition energy entropy. The identification accuracy using the proposed method reaches 96.28%, which demonstrates its effectiveness and superiority.
Year
DOI
Venue
2019
10.1109/TVT.2019.2925903
IEEE Transactions on Vehicular Technology
Keywords
Field
DocType
Train plug doors,fault diagnosis,wavelet package decomposition entropy (WPDE),fractional wavelet package decomposition entropy (FWPDE),multi-class support vector machine (MSVM)
Signal processing,Digital signal processing,Railway engineering,Computer science,Support vector machine,Algorithm,Electronic engineering,Preprocessor,Fractional calculus,Wavelet,Hilbert–Huang transform
Journal
Volume
Issue
ISSN
68
8
0018-9545
Citations 
PageRank 
References 
1
0.35
0
Authors
4
Name
Order
Citations
PageRank
Yuan Cao17310.39
Yongkui Sun231.40
Guo Xie3157.15
Tao Wen42712.07