In-Wheel Motor Fault Diagnosis Method Based on Two-Stream 2DCNNs with DCBA Module
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new fault diagnosis method for in-wheel motors using two-stream 2DCNNs and a DCBA module. The approach enhances feature extraction for improved accuracy in electric vehicle systems.
Area Of Science
- Engineering
- Artificial Intelligence
- Mechanical Systems
Background
- Fault diagnosis in four-wheel independent driving systems is critical for safety and reliability.
- Variable driving conditions and harsh environments pose significant challenges to existing diagnostic methods.
Purpose Of The Study
- To propose a novel fault diagnosis method for in-wheel motors in electric vehicles.
- To enhance diagnostic accuracy and robustness under complex operating conditions.
Main Methods
- A two-stream 2DCNN architecture was developed, integrating a novel DCBA module.
- The DCBA module extracts multi-scale features from vibration signals across different domains.
- Dual-domain learning utilizes both time-domain and time-frequency-domain signals for feature extraction.
Main Results
- The proposed method achieved high recognition accuracy across various speeds, loads, and road surfaces.
- Comparative analysis showed superior performance and robustness against existing models like SENet, ECANet, and CBAM.
- The integration of DCBA and dual-domain learning effectively improved fault feature extraction.
Conclusions
- The novel two-stream 2DCNN with DCBA module offers a robust and accurate solution for in-wheel motor fault diagnosis.
- This method demonstrates significant potential for application in real-world electric vehicle systems.
- Effective feature extraction under complex conditions is key to improving diagnostic performance.
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