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

doi:10.23940/ijpe.18.05.p18.9951003

Abstract

Abstract:

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.

Submitted on February 8, 2018; Revised on March 12, 2018; Accepted on April 23, 2018
References: 27

Yang Ge Xiaomei Jiang. Mathematical Morphology and Deep Learning-based Approach for Bearing Fault Recognition [J]. Int J Performability Eng, 2018, 14(5): 995-1003.

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