Name
Title | ||
|---|---|---|
A two-stage feature selection and intelligent fault diagnosis method for rotating machinery using hybrid filter and wrapper method. |
Abstract | ||
|---|---|---|
Selecting the most discriminative features from the original high dimensional feature space and finding out the optimal parameters for recognition model both have vital influences on the accuracy of fault diagnosis for complicated mechanical system. However, as these two important processes are interactional, conducting them separately may result in inferior diagnostic accuracy. This paper presents a feature selection and fault diagnosis framework which can select the optimal feature subset and optimize the parameters of SVM classifier synchronously and dynamically with the ultimate objective of achieving the highest diagnostic rate. The proposed method is based on a hybrid Filter and Wrapper framework. Since the original feature dimensionality is high which may lead to a lower computation efficiency of the process of synchronous feature selection and SVM parameters optimization, ReliefF is applied for preliminarily selecting some optimal feature candidates. Furthermore, in the reselection process, the reselection state of feature candidates and the values of classifier parameters are all encoded into BPSO particles. The optimal feature subset and the SVM model can be synchronously obtained for fault diagnosis with a high performance. Moreover, in the original feature extraction stage, intrinsic time-scale decomposition (ITD) is utilized to preprocess the nonstationary vibration signal into several PRCs. The statistical parameters in time and frequency domain of PRCs are extracted as the multitudinous original features for each signal sample. Two experimental cases including rolling bearing fault and rotor system fault are implemented to evaluate the proposed scheme. The results demonstrate that compared with some existing methods the proposed one can obtain a better comprehensive performance in the number of optimal features, training time and testing accuracy. (C) 2017 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2017.11.016 | NEUROCOMPUTING |
Keywords | Field | DocType |
Fault diagnosis,Feature selection,Intrinsic time-scale decomposition,ReliefF,Binary particle swarm optimization,Support vector machine | Data mining,Feature selection,Artificial intelligence,Classifier (linguistics),Frequency domain,Feature vector,Pattern recognition,Feature (computer vision),Support vector machine,Curse of dimensionality,Feature extraction,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
275 | 0925-2312 | 6 |
PageRank | References | Authors |
0.44 | 15 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolong Zhang | 1 | 96 | 18.80 |
| Zhang Qing | 2 | 35 | 12.71 |
| Miao Chen | 3 | 113 | 11.35 |
| Yuantao Sun | 4 | 6 | 0.44 |
| Xianrong Qin | 5 | 6 | 0.44 |
| Heng Li | 6 | 21 | 7.48 |