Motor bearing fault diagnosis using deep convolutional neural networks with 2D analysis of vibration signal

Abstract

Bearings are critical components in rotating machinery, and it is crucial to diagnose their faults at an early stage. Existing fault diagnosis methods are mostly limited to manual features and traditional artificial intelligence learning schemes such as neural network, support vector machine, and k-nearest-neighborhood. Unfortunately, interpretation and engineering of such features require substantial human expertise. This paper proposes an adaptive deep convolutional neural network (ADCNN) that utilizes cyclic spectrum maps (CSM) of raw vibration signal as bearing health states to automate feature extraction and classification process. The CSMs are two-dimensional (2D) maps that show the distribution of cycle energy across different bands of the vibration spectrum. The efficiency of the proposed algorithm (CSM+ADCNN) is validated using benchmark dataset collected from bearing tests. Experimental results indicate that the proposed method outperforms the state-of-the-art algorithms, yielding 8.25% to 13.75% classification performance improvement.