Paper Info
Title
Rolling-Bearing Fault-Diagnosis Method Based On Multimeasurement Hybrid-Feature Evaluation
Abstract
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.
Year
DOI
Venue
2019
10.3390/info10110359
INFORMATION
Keywords
Field
DocType
rolling bearing, feature evaluation, fault diagnosis, hybrid measurements
Data mining,Multiplicative function,Computer science,Support vector machine,Kernel principal component analysis,Bearing (mechanical),Correlation,Vibration,Cluster analysis,Complete information
Journal
Volume
Issue
Citations 
10
11
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Jianghua Ge100.34
Guibin Yin200.34
Yaping Wang349632.87
Di Xu4101.91
Fen Wei500.34