Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network

Abstract

<abstract> <p>As an essential component of mechanical equipment, the fault diagnosis of rolling bearings may not only guarantee the systematic operation of the equipment, but also minimize any financial losses caused by equipment shutdowns. Fault diagnosis algorithms based on convolutional neural networks (CNN) have been widely used. However, traditional CNNs have limited feature representation capabilities, thereby making it challenging to determine their hyperparameters. This paper proposes a fault diagnosis method that combines a 1D-CNN with an attention mechanism and hyperparameter optimization to overcome the aforementioned limitations; this method improves the search speed for optimal hyperparameters of CNN models, improves the diagnostic accuracy, and enhances the representation of fault feature information in CNNs. First, the 1D-CNN is improved by combining it with an attention mechanism to enhance the fault feature information. Second, a swarm intelligence algorithm based on Differential Evolution (DE) and Grey Wolf Optimization (GWO) is proposed, which not only improves the convergence accuracy, but also increases the search efficiency. Finally, the improved 1D-CNN alongside hyperparameters optimization are used to diagnose the faults of rolling bearings. By using the Case Western Reserve University (CWRU) and Jiangnan University (JNU) datasets, when compared to other common diagnosis models, the results demonstrate the usefulness and dependability of the DE-GWO-CNN algorithm in fault diagnosis applications by demonstrating the increased diagnostic accuracy and superior anti-noise capabilities of the proposed method. The fault diagnosis methodology presented in this paper can accurately identify faults and provide dependable fault classification, thereby assisting technicians in promptly resolving faults and minimizing equipment failures and operational instabilities.</p> </abstract>

Multi-scale residual neural network with enhanced gated recurrent unit for fault diagnosis of rolling bearing

“…Accuracy (%) DE-GWO-CNN [22] 99.25 LF-iCapsNet [24] 99.60 MSCNN [32] 99.88 Multi-signal CNN-GRU [34] 99.69 Proposed method 99.98…”

“…For example, Zhao et al [21] proposed a class-aware adversarial multiwavelet CNN for cross-domain fault diagnosis of rotating machinery, addressing the issues of incomplete feature extraction and limited utilization of unlabeled target data in this context. Wang et al [22] introduced a fault diagnosis method for rolling bearings that leverages the attention mechanism and hyperparameter optimization in conjunction with a one-dimensional CNN. Furthermore, RNN has also achieved fruitful praisable achievements in fault diagnosis [23] by virtue of its excellent temporal data processing ability.…”

A review on convolutional neural network in rolling bearing fault diagnosis

“…This algorithm had performed hyperparameter optimization on the CNN algorithm, but the parameter adaptation of the network still need further improvement. Wang et al [16] proposed the fault diagnosis method that combines 1D-CNN with attention mechanism and hyperparameter optimization. The method improved the optimal hyperparameter search speed of CNN and improves the accuracy of fault diagnosis.…”