Multiperspective Temporal Pooling Convolutional Neural Networks for Fault Diagnosis of Mechanical Transmission Systems

Abstract

The rapid development of convolutional neural networks (CNNs) has significantly contributed to the progress of intelligent fault diagnosis of mechanical transmission systems. Nevertheless, a significant number of prevailing CNN-based diagnostic models may suffer from two notable constraints. First, the existing models often employ fixed temporal pooling for feature extraction, which restricts their ability to effectively capture and analyze a comprehensive range of temporal information. Second, these models may struggle to precisely forecast the operational state of the monitored machinery amidst nonstationary circumstances, such as time-varying or disturbed environments. These challenges limit their feature extraction capabilities and hinder their practical implementation and utilization. To tackle the aforementioned issues, this study develops a multiperspective temporal pooling CNN (MTPCNN). The main contributions encompass: 1) a multikernel feature perception module (MFPM) and a balanced attention module (BAM) are established for multilevel information exploration and optimal feature selection and 2) an innovative multiperspective temporal pooling learning (MTPL) strategy is introduced to aid the model in dynamically selecting the optimal temporal pooling method for the input data. A laboratory dataset collected from a gearbox fault simulator and an industrial dataset collected from a high-speed rail are used for the validation of the proposed approach. The extensive experimental results validate the superiority of the developed MTPCNN model over seven competitive approaches.