Adaptive Multiclass Mahalanobis Taguchi System for Bearing Fault Diagnosis under Variable Conditions
| Autor: | Xinan Chen, Zhipeng Wang, Ning Wang, Limin Jia, Yong Qin, Yakun Zuo |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
Feature selection 02 engineering and technology lcsh:Chemical technology Fault (power engineering) computer.software_genre Biochemistry Article Analytical Chemistry law.invention Taguchi methods law Robustness (computer science) Singular value decomposition 0202 electrical engineering electronic engineering information engineering Feature (machine learning) lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation bearing VMD Mahalanobis distance Bearing (mechanical) adaptive Multiclass Mahalanobis Taguchi System 020208 electrical & electronic engineering fault diagnosis Atomic and Molecular Physics and Optics Binary classification 020201 artificial intelligence & image processing Data mining SVD computer |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 19 Issue 1 Sensors, Vol 19, Iss 1, p 26 (2018) |
| ISSN: | 1424-8220 |
| Popis: | Bearings are vital components in industrial machines. Diagnosing the fault of rolling element bearings and ensuring normal operation is essential. However, the faults of rolling element bearings under variable conditions and the adaptive feature selection has rarely been discussed until now. Thus, it is essential to develop a practicable method to put forward the disposal of the fault under variable conditions. Considering these issues, this paper uses the method based on the Mahalanobis Taguchi System (MTS), and overcomes two shortcomings of MTS: (1) MTS is an effective tool to classify faults and has strong robustness to operating conditions, but it can only handle binary classification problems, and this paper constructs the multiclass measurement scale to deal with multi-classification problems. (2) MTS can determine important features, but uses the hard threshold to select the features, and this paper selects the proper feature sequence instead of the threshold to overcome the lesser adaptivity of the threshold configuration for signal-to-noise gain. Hence, this method proposes a novel method named adaptive Multiclass Mahalanobis Taguchi system (aMMTS), in conjunction with variational mode decomposition (VMD) and singular value decomposition (SVD), and is employed to diagnose the faults under the variable conditions. Finally, this method is verified by using the signal data collected from Case Western Reserve University Bearing Data Center. The result shows that it is accurate for bearings fault diagnosis under variable conditions. |
| Databáze: | OpenAIRE |
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