Strategy for Fault Diagnosis on Train Plug Doors Using Audio 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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/1/3 |
| _version_ | 1828146853380620288 |
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| author | Yongkui Sun Guo Xie Yuan Cao Tao Wen |
| author_facet | Yongkui Sun Guo Xie Yuan Cao Tao Wen |
| author_sort | Yongkui Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:52:06Z |
| format | Article |
| id | doaj.art-93b76be695f14c369e7d500adb17f02b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:52:06Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-93b76be695f14c369e7d500adb17f02b2022-12-22T04:03:49ZengMDPI AGSensors1424-82202018-12-01191310.3390/s19010003s19010003Strategy for Fault Diagnosis on Train Plug Doors Using Audio SensorsYongkui Sun0Guo Xie1Yuan Cao2Tao Wen3School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaShaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, ChinaNational Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, ChinaAs 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.https://www.mdpi.com/1424-8220/19/1/3fault diagnosistrain plug doorsmulti-scale permutation entropy (MNPE)improved particle swarm optimization (IPSO)multi-class SVM |
| spellingShingle | Yongkui Sun Guo Xie Yuan Cao Tao Wen Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors Sensors fault diagnosis train plug doors multi-scale permutation entropy (MNPE) improved particle swarm optimization (IPSO) multi-class SVM |
| title | Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors |
| title_full | Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors |
| title_fullStr | Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors |
| title_full_unstemmed | Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors |
| title_short | Strategy for Fault Diagnosis on Train Plug Doors Using Audio Sensors |
| title_sort | strategy for fault diagnosis on train plug doors using audio sensors |
| topic | fault diagnosis train plug doors multi-scale permutation entropy (MNPE) improved particle swarm optimization (IPSO) multi-class SVM |
| url | https://www.mdpi.com/1424-8220/19/1/3 |
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