A Novel Fault Diagnosis Method Based on MADCNN for Rolling Bearings

Abstract

Rolling bearings are treated as important machinery power components, faults of rolling bearings affect machinery operation, so an intelligent fault diagnosis method is very useful of safety operation in rolling bearings. This paper proposes a novel fault diagnosis method based on improved Adaptive Deep Convolution Neural Networks algorithm to realize fault recognition for rolling bearings. First, the Continuous Wavelet Transform (CWT) method is applied to the time-frequency decomposition of vibration signals and extract feature information images for training and testing. Second, to further improve self-learning ability of the Adaptive Deep Convolution Neural Network (ADCNN) in feature images, the Multiple Channels ADCNN method is proposed to classify different fault image types for the rolling bearing. Finally, fault images corresponding to different health states of the rolling bearing are applied to the proposed method, the experiment proves that the proposed method has a better performance for fault recognition in rolling bearings.