Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization

Abstract

Accurate fault information is critical for optimal scheduling of production activities, improving system reliability, and reducing operation and maintenance costs. In recent years, many fault diagnosis methods for rolling element bearings have been developed based on deep learning. Most of them are totally data-driven and do not consider the domain knowledge that has been used in fault diagnosis for years. Meanwhile, operating conditions such as rotating speed and load that have great influence on vibration signals are also ignored. It may cause a decrease in accuracy when the bearing type or operating condition changes. To address these problems, this article proposes a rolling element bearing fault diagnosis and localization approach based on multitask convolutional neural network (CNN) with information fusion. In the proposed approach, domain knowledge, operating conditions, and vibration signals are fused into a three-dimensional input that can be processed well by CNN. Then, a multitask CNN with dynamic training rates is constructed to simultaneously accomplish two tasks, fault diagnosis and localization. Experimental results on two rolling element bearing test beds with different bearing types and operating conditions are presented and compared with existing state-of-the-art approaches to demonstrate the effectiveness and accuracy of the proposed approach.