A Novel Ensemble Learning-Based Multisensor Information Fusion Method for Rolling Bearing Fault Diagnosis

Abstract

It is meaningful to learn fault-related information from multisensor signals automatically and provide accurate diagnostic results. To further improve the multisensor information fusion effect, reduce the discrepancy between the real value and predicted value, and improve the rolling bearing fault diagnosis accuracy, in this article, a novel ensemble learning-based multisensor information fusion method is proposed. First, a multiscale convolutional neural network (MSCNN) is constructed as a base learning model to learn multiscale features of raw vibration signals. Second, based on the ensemble learning framework, a multibranch MSCNN is built to realize the simultaneous extraction of multiple sensor signal features and output the decision score of each sensor. Then, considering the confidence of multiple different sensors, fuzzy rank is utilized to fuse the decision scores of each sensor, minimizing the discrepancy between the real value and predicted value. Finally, the usefulness and robustness of the proposed method are validated on two different types of rolling bearing datasets. The dataset and code are available under request and the contact e-mail is pantc2006@163.com.