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Intelligent Fault Diagnosis of Bearings Based on Energy Levels in Frequency Bands Using Wavelet and Support Vector Machines (SVM)

1
Department of Mechanical Engineering, Abbaspour College of Technology, Shahid Beheshti University, Tehran 16589-53571, Iran
2
Department of Mechanical Engineering, Georgia Southern University, Statesboro, GA 30460, USA
3
Department of Manufacturing Engineering, Georgia Southern University, Statesboro, GA 30460, USA
*
Author to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2019, 3(1), 11; https://doi.org/10.3390/jmmp3010011
Received: 24 December 2018 / Revised: 14 January 2019 / Accepted: 15 January 2019 / Published: 19 January 2019
In this paper, a new method was introduced for feature extraction and fault diagnosis in bearings based on wavelet packet decomposition and analysis of the energy in different frequency bands. This method decomposes a signal into different frequency bands using different types of wavelets and performs multi-resolution analysis to extract different features of the signals by choosing energy levels in different frequency bands. The support vector machines (SVM) technique was used for faults classifications. Daubechies, biorthogonal, coiflet, symlet, Meyer, and reverse Meyer wavelets were used for feature extraction. The most appropriate decomposition level and frequency band were selected by analyzing the variation in the signal’s energy level. The proposed approach was applied to the fault diagnosis of rolling bearings, and testing results showed that the proposed approach can reliably identify different fault categories and their severities. Moreover, the effectiveness of the proposed feature selection and fault diagnosis method was significant based on the similarity between the wavelet packet and the signal, and effectively reduced the influence of the signal noise on the classification results. View Full-Text
Keywords: feature extraction; rolling bearing; multi-resolution; wavelet; support vector machine feature extraction; rolling bearing; multi-resolution; wavelet; support vector machine
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Yadavar Nikravesh, S.M.; Rezaie, H.; Kilpatrik, M.; Taheri, H. Intelligent Fault Diagnosis of Bearings Based on Energy Levels in Frequency Bands Using Wavelet and Support Vector Machines (SVM). J. Manuf. Mater. Process. 2019, 3, 11.

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