Name
Abstract | ||
|---|---|---|
The train plug door is the only way for passengers to get on and off. The reliability of the doors has a direct impact on passengers' safety and operational efficiency. In order to address the shortcomings of the post-analysis and poor real-time of current fault diagnosis methods for train plug doors, a fault diagnosis method based on sound recognition is proposed. To process the non-stationary sound signals, the empirical mode decomposition (EMD) method is applied to sound signal samples of train plug doors, and a series of intrinsic mode functions (IMFs) are obtained. Then, wavelet packet decomposition is utilized on each IMF to acquire more detailed information. And wavelet packet energy entropy features are obtained. The Fisher discrimination criterion is used to carry out a mathematical analysis to select the most significant features as discrimination features. Finally, multi-class support vector machine (multi-class SVM) is utilized to carry out classification and validation. And the prediction accuracy of the 67 test samples reaches 95.52%, which indicates the proposed fault diagnosis method for train plug doors is feasible. The proposed method also provides the possibility of automatic faults recognition. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MITS.2019.2926366 | IEEE Intelligent Transportation Systems Magazine |
Keywords | DocType | Volume |
empirical mode decomposition,train plug door,fault diagnosis,nonstationary sound signals,intrinsic mode functions,wavelet packet energy entropy features,multiclass support vector machine,multi-class SVM,Fisher discrimination criterion,mathematical analysis,sound recognition | Journal | 13 |
Issue | ISSN | Citations |
3 | 1939-1390 | 1 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Cao | 1 | 73 | 10.39 |
| Yuan Cao | 2 | 73 | 10.39 |
| Lian-chuan Ma | 3 | 11 | 1.98 |