Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network

Abstract

This paper presents a data-driven intelligent fault diagnosis approach for rotating machinery (RM) based on a novel continuous wavelet transform-local binary convolutional neural network (CWT-LBCNN) model. The proposed approach builds an end-to-end diagnosis mechanism, and does not need manual feature extraction. By feeding the inputting vibration signal, features are captured adaptively and fault condition of RM is diagnosed automatically. Different from traditional CNNs, the proposed CWT-LBCNN utilizes a local binary convolution layer to replace a traditional convolution layer, which enables CWT-LBCNN to have faster training speed and less proneness to overfitting. Two experimental studies including bearing fault diagnosis and gearbox compound fault diagnosis show that the proposed CWT-LBCNN has more stable and reliable prediction accuracy than other existing methods.