Fault Diagnosis of Rotating Machinery Bearings Based on Improved DCNN and WOA-DELM

A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation...

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Bibliographic Details
Published in:Processes 2023-07, Vol.11 (7), p.1928
Main Authors: Wang, Lijun, Ping, Dongzhi, Wang, Chengguang, Jiang, Shitong, Shen, Jie, Zhang, Jianyong
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial neural networks
Bearings
Classification
Critical components
Deep learning
Diagnostic systems
Discriminant analysis
Eigenvectors
Entropy
Fault diagnosis
Fourier transforms
Long short-term memory
Machine learning
Machinery
Mathematical models
Mathematical optimization
Neural networks
Optimization
Optimization algorithms
Roller bearings
Rotating machinery
Sensors
Signal processing
Sliding friction
Support vector machines
Wavelet transforms
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Summary:A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr11071928