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.
Highlights
As one of the most widely used components in rotating machinery, the health status of rolling bearings has an important impact on the working conditions of the entire mechanical equipment.Once a failure occurs, the performance of the equipment is greatly reduced and even has catastrophic consequences [1]
In view of the above analysis, this paper proposes a fault-diagnosis method based on multi-measurement hybrid-feature evaluation
In order to verify the effectiveness of the new fault-feature set based on the multi-measurement hybrid-feature evaluation model in this paper, a category-divergence fault-diagnosis model based on kernel principal component analysis (KPCA) [29] and a fault-diagnosis model based on a support vector machine (SVM) [30], are proposed
Summary
As one of the most widely used components in rotating machinery, the health status of rolling bearings has an important impact on the working conditions of the entire mechanical equipment. Many scholars have invested in the research of rolling bearing fault diagnosis based on feature evaluation and feature extraction [11,12,13]. In view of the above analysis, this paper proposes a fault-diagnosis method based on multi-measurement hybrid-feature evaluation. By using a four-feature evaluation model of comprehensive distance, correlation, and information, the original feature set composed of the time domain, frequency domain, and time-frequency domain features parameters that are used to obtain the feature score. The new weighted feature set is formed for each feature weight by each feature score, and applied to the fault diagnosis of the rolling bearing. The proposed diagnostic method is applied to two different sets of experimental data of rolling-bearing failure.
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