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Mathematical Morphology and Deep Learning-based Approach for Bearing Fault Recognition

Volume 14, Number 5, May 2018, pp. 995-1003
DOI: 10.23940/ijpe.18.05.p18.9951003

Yang Ge and Xiaomei Jiang

Changshu Institute of Technology, Changshu, 215500, China

(Submitted on February 8, 2018; Revised on March 12, 2018; Accepted on April 23, 2018)


A fault feature extraction method for rolling element bearings based on mathematical morphology is proposed in this paper. In order to obtain more useful features, this paper attempts to mix mathematical fractal features into time-frequency domain features and wavelet packet energy features. Using the mixed features, support vector machine and deep learning are performed to recognize operation conditions of bearings. It is found that mixed features can improve the conditions recognition accuracy. The comparison results show that deep learning performs better than support the vector machine and is able to predict bearing conditions with a mean accuracy of 99.19%. Therefore, it is concluded that the mixed features and deep learning method are effective for bearing operation conditions recognition.


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