A Rolling Bearing Fault Diagnosis Method Based on Switchable Normalization and a Deep Convolutional Neural Network

Abstract

Aiming to address the problems of a low fault detection rate and poor diagnosis performance under different loads and noise environments, a rolling bearing fault diagnosis method based on switchable normalization and a deep convolutional neural network (SNDCNN) is proposed. The method effectively extracted the fault features from the raw vibration signal and suppressed high-frequency noise by increasing the convolution kernel width of the first layer and stacking multiple layers’ convolution kernels. To avoid losing the intensity information of the features, the K-max pooling operation was adopted at the pooling layer. To solve the overfitting problem and improve the generalization ability, a switchable normalization approach was used after each convolutional layer. The proposed SNDCNN was evaluated with two sets of rolling bearing datasets and obtained a higher fault detection rate than SVM and BP, reaching a fault detection rate of over 90% under different loads and demonstrating a better anti-noise performance.