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Open AccessArticle

Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation

1
Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
2
Hunan Provincial Key Laboratory of Mechanical Equipment Maintenance, Hunan University of Science and Technology, Hunan 411201, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(11), 359; https://doi.org/10.3390/info10110359
Received: 2 November 2019 / Revised: 12 November 2019 / Accepted: 15 November 2019 / Published: 19 November 2019
(This article belongs to the Section Information Applications)
To improve the accuracy of rolling-bearing fault diagnosis and solve the problem of incomplete information about the feature-evaluation method of the single-measurement model, this paper combines the advantages of various measurement models and proposes a fault-diagnosis method based on multi-measurement hybrid-feature evaluation. In this study, an original feature set was first obtained through analyzing a collected vibration signal. The feature set included time- and frequency-domain features, and also, based on the empirical-mode decomposition (EMD)-obtained time-frequency domain, energy and Lempel–Ziv complexity features. Second, a feature-evaluation framework of multiplicative hybrid models was constructed based on correlation, distance, information, and other measures. The framework was used to rank features and obtain rank weights. Then the weights were multiplied by the features to obtain a new feature set. Finally, the fault-feature set was used as the input of the category-divergence fault-diagnosis model based on kernel principal component analysis (KPCA), and the fault-diagnosis model was based on a support vector machine (SVM). The clustering effect of different fault categories was more obvious and classification accuracy was improved. View Full-Text
Keywords: rolling bearing; feature evaluation; fault diagnosis; hybrid measurements rolling bearing; feature evaluation; fault diagnosis; hybrid measurements
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MDPI and ACS Style

Ge, J.; Yin, G.; Wang, Y.; Xu, D.; Wei, F. Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation. Information 2019, 10, 359.

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