A Multi-source Information Fusion Bearing Fault Diagnosis Method Based on PCIDCNN

This paper proposes a new bearing fault diagnosis model (PCIDCNN) based on multi-source information fusion with principal component analysis and improved 1DCNN model to achieve the diagnosis of bearing faults under the operating conditions of alternating loads. The proposed model improves the multi-sensor data fusion ability and feature learning ability to solve the problem of information overload and noise interference of multi-source information during bearing operation. The bearings are the key component of rotating machinery. It is crucial to make timely and accurate fault diagnosis on bearings for the reliability and safety. PCA is employed to fuse signals from multiple sensors to obtain the fused data. We propose an improved 1DCNN model combining the attention mechanism and fused pooling layer to capture important fault features adequately. Experimental results based on real datasets show that the proposed method is able to analyze and diagnose the bearing fault signals, accurately identify different fault types, with obvious advantages over traditional machine learning models, achieving the diagnosis precision rate of 96.33%.