Abstract
The objective of this research is to investigate the feasibility of utilizing the multi-scale analysis and support vector machine (SVM) classification scheme to diagnose the bearing faults in rotating machinery. For complicated signals, the characteristics of dynamic systems may not be apparently observed in a scale, particularly for the fault-related features of rotating machinery. In this research, the multi-scale analysis is employed to extract the possible fault-related features in different scales, such as the multi-scale entropy (MSE), multi-scale permutation entropy (MPE), multi-scale root-mean-square (MSRMS) and multi-band spectrum entropy (MBSE). Some of the features are then selected as the inputs of the support vector machine (SVM) classifier through the Fisher score (FS) as well as the Mahalanobis distance (MD) evaluations. The vibration signals of bearing test data at Case Western Reserve University (CWRU) are utilized as the illustrated examples. The analysis results demonstrate that an accurate bearing defect diagnosis can be achieved by using the extracted machine features in different scales. It can be also noted that the diagnostic results of bearing faults can be further enhanced through the feature selection procedures of FS and MD evaluations.
Highlights
Machine health monitoring and fault diagnosis have attracted considerable attention in both the academic fields as well as the industrial applications
The results demonstrate that the diagnostic accuracy can be enhanced by imposing the feature selection process
Since the multi-scale entropy (MSE) and multi-scale permutation entropy (MPE) correspond to the different features of dynamical systems, the two measurements of entropy can be utilized to characterize the signals of rotary system with faulted bearings
Summary
Machine health monitoring and fault diagnosis have attracted considerable attention in both the academic fields as well as the industrial applications. In addition to the conventional spectral analysis of faulty bearing vibration signals [3,4], the envelope analysis and the demodulation technique are widely utilized for diagnosis of different bearing defects [5,6,7,8]. To advance the state-of-art of bearing defect diagnostics, a new approach is proposed to investigate the effectiveness of utilizing the multi-scale analysis and feature selection methods for diagnosis of different bearing faults in this research. Through the FS evaluation, the features of high distinguishabilities are selected for diagnosis of different bearing defects. With the feature selection process, either the FS or MD evaluation, both the accuracy of classification results and the computational efficiency can be enhanced. The analysis results show that the different classes of bearing defects can be diagnosed accurately through the multi-scale analysis and SVM classification. It is noted that the MD evaluation can achieve more accurate diagnostic results than the FS evaluation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
