Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors †
1
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2
Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China
3
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China
*
Authors to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in The Seventh International Conference on Control Automation & Information Sciences (ICCAIS), Hangzhou, China, 24–27 October 2018.
Sensors 2019, 19(1), 3; https://doi.org/10.3390/s19010003
Received: 4 December 2018
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Revised: 17 December 2018
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Accepted: 18 December 2018
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Published: 20 December 2018
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
As the only entry/exit for passengers getting on and off a train, the train plug door is of great importance to keep train operation safe and reliable. As signal processing technologies develop rapidly, taking the easy acquisition advantages of sound signals, a novel fault diagnosis method for train plug doors using multi-scale normalized permutation entropy (MNPE) and an improved particle swarm optimization based multi-class support vector machine (IPSO-MSVM) is proposed. Firstly, sound samples are collected using high-precision audio sensor. In the features extraction process, a hybrid method blending empirical mode decomposition (EMD), multi-scale permutation entropy (MNPE) with Fisher discrimination criterion is utilized. First, EMD is used to decompose each sound signal into several intrinsic mode functions (IMFs) and a residue for stationary processing. Then, MNPE features are extracted from the IMFs. To obtain the most significant features, the Fisher discrimination criterion is further applied. To address the time-consuming defects of traditional grid based method for selecting the optimal parameters of multi-class SVM, an improved PSO (IPSO) is proposed. The superiority of the IPSO-MSVM model and the hybrid feature extraction method was tested on the collected sound samples by comparing to commonly applied methods. Results indicate the identification accuracy of the proposed method is highest, which reaches 90.54%, demonstrating its feasibility.
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Keywords:
fault diagnosis; train plug doors; multi-scale permutation entropy (MNPE); improved particle swarm optimization (IPSO); multi-class SVM
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MDPI and ACS Style
Sun, Y.; Xie, G.; Cao, Y.; Wen, T. Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors. Sensors 2019, 19, 3. https://doi.org/10.3390/s19010003
AMA Style
Sun Y, Xie G, Cao Y, Wen T. Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors. Sensors. 2019; 19(1):3. https://doi.org/10.3390/s19010003
Chicago/Turabian StyleSun, Yongkui, Guo Xie, Yuan Cao, and Tao Wen. 2019. "Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors" Sensors 19, no. 1: 3. https://doi.org/10.3390/s19010003
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