Domain Adaptation for Bearing Fault Diagnosis Based on SimAM and Adaptive Weighting Strategy
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces CWT-SimAM-DAMS, a novel domain adaptation method for bearing fault diagnosis. It effectively addresses data distribution discrepancies, significantly improving diagnostic accuracy under complex conditions.
Area Of Science
- Mechanical Engineering
- Artificial Intelligence
- Signal Processing
Background
- Domain adaptation is vital for bearing fault diagnosis due to varying operational conditions causing data distribution discrepancies.
- Transfer fault diagnosis struggles with complex conditions, dispersed data, and significant distribution differences.
Purpose Of The Study
- To propose CWT-SimAM-DAMS, a domain adaptation method enhancing bearing fault diagnosis accuracy.
- To effectively minimize distribution differences between source and target domains in fault diagnosis.
Main Methods
- Utilizes Continuous Wavelet Transform (CWT) and Unsharp Masking (USM) for data preprocessing.
- Employs a Residual Network (ResNet) with the SimAM module for feature extraction.
- Integrates an adaptive weighting strategy with Joint Maximum Mean Discrepancy (JMMD) and Conditional Adversarial Domain Adaption Network (CDAN) algorithms.
Main Results
- The proposed CWT-SimAM-DAMS method demonstrates improved accuracy in bearing fault diagnosis.
- Experimental validation on two datasets confirms the method's effectiveness in enhancing domain adaptability.
Conclusions
- CWT-SimAM-DAMS offers a robust solution for bearing fault diagnosis under challenging domain shifts.
- The adaptive weighting strategy and SimAM integration effectively bridge distribution gaps, boosting diagnostic performance.
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