A Novel 1D-CNN-Based Diagnosis Method for a Rolling Bearing with Dual-Sensor Vibration Data Fusion

Abstract

Due to variation of working conditions and influence of noise in vibration data, rolling bearing intelligent diagnosis based on deep learning faces challenges in efficient utilization of monitoring data and scientific extraction of fault features. This study proposes a one-dimensional convolution neural network (1D-CNN)-based intelligent diagnosis method for a rolling bearing, which fuses the horizontal and the vertical vibration signals, makes full use of spectral order features by full-spectrum analysis, and achieves accurate classification of fault pattern by 1D-CNN model. The experimental datasets of constant and variable working conditions of rolling bearing are constructed. The test results of the proposed method show that spectral order features are extracted effectively by full-spectrum analysis and high diagnostic accuracy is obtained by the constructed 1D-CNN model on both datasets. The comparison with the other four similar methods indicates that the diagnostic accuracy of the proposed method outperforms the comparative methods significantly in the case of variable operating conditions.