Name
Abstract | ||
|---|---|---|
Train plug doors play a vital role to keep train operation safe and reliable. Taking the advantages of sound signals based fault diagnosis method into consideration, a novel fault diagnosis method for train plug doors based on multiscale normalized permutation entropy (MNPE) and an improved particle swarm optimization based multi-class SVM (IPSO-MSVM) is proposed. First, empirical mode decomposition (EMD) is used to decompose each sound signal into a series of intrinsic mode functions (IMFs) and a residue for stationary processing. Then, MNPE features are extracted from the IMFs. And Fisher discrimination criterion is utilized to obtain the most significant features as feature vectors. Further, an improved PSO (IPSO) is used to search for the optimal parameters of multi-class SVM. Finally, the IPSO-MSVM model is trained and verified using training set and test set, respectively. The identification accuracy of the proposed method reaches 90.54%, which is higher than backpropagation (BP) neural network classifier and 1 Nearest Neighbor (1NN) classifier, indicating the proposed method for fault diagnosis on train plug doors is feasible. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICCAIS.2018.8570324 | 2018 International Conference on Control, Automation and Information Sciences (ICCAIS) |
Keywords | Field | DocType |
fault diagnosis,train plug doors,multi-scale permutation entropy,PSO,SVM | Particle swarm optimization,k-nearest neighbors algorithm,Feature vector,Pattern recognition,Control theory,Support vector machine,Artificial intelligence,Engineering,Backpropagation,Classifier (linguistics),Hilbert–Huang transform,Test set | Conference |
ISSN | ISBN | Citations |
2475-790X | 978-1-5386-6021-8 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongkui Sun | 1 | 3 | 1.40 |
| Guo Xie | 2 | 15 | 7.15 |
| Yuan Cao | 3 | 73 | 10.39 |
| Tao Wen | 4 | 27 | 12.07 |