Optimization of Gearbox Fault Detection Method Based on Deep Residual Neural Network Algorithm
Zhaohua Wang1, Yingxue Tao1, Yanping Du1
1Department of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, No. 1, Xinghua Street, Beijing 102600, China.
Sensors (Basel, Switzerland)
|September 9, 2023
Summary
Gear fault diagnosis is crucial for machinery health. This study introduces an improved ResNeXt50 model with CBAM for accurate and faster gear failure detection, outperforming other methods.
Area of Science:
- Mechanical Engineering
- Artificial Intelligence
Background:
- Gear failures are common due to operational demands, making early detection challenging.
- Traditional neural network models for gear fault diagnosis suffer from complexity and long training times.
Purpose of the Study:
- To develop an efficient and accurate gear fault detection method.
- To enhance feature extraction capabilities for improved diagnostic performance.
Main Methods:
- Integrated the Convolutional Block Attention Module (CBAM) into the ResNeXt50 network.
- Converted 1D vibration signals into 2D images using optimal time-frequency analysis for noise reduction.
- Trained and tested the model on a gearbox fault dataset, comparing it with classical convolutional neural network models.
Main Results:
- The proposed CBAM-ResNeXt50 model demonstrated superior fault identification accuracy.
- The model achieved a significantly reduced average training time compared to other models.
- Effective performance was observed under two distinct working conditions.
Conclusions:
- The CBAM-ResNeXt50 method offers an effective solution for gearbox fault diagnosis.
- This approach enhances both accuracy and efficiency in detecting gear failures.
- The findings provide valuable insights for current gear failure diagnosis research.
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