A novel procedure for diagnosing multiple faults in rotating machinery
Zhijian Wang1, Zhennan Han1, Fengshou Gu2
1College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, Shanxi 030024, PR China.
ISA Transactions
|December 3, 2014
Summary
A new Combined Mode Function-Ensemble Empirical Mode Decomposition (CMF-EEMD) method enhances wind turbine gearbox fault diagnosis. This technique improves the analysis of complex gearbox signals, offering superior performance over existing methods.
Area of Science:
- Mechanical Engineering
- Signal Processing
- Condition Monitoring
Background:
- Wind turbine gearboxes are critical components prone to multi-fault conditions.
- Effective signal processing is essential for accurate fault detection and diagnosis.
Purpose of the Study:
- To introduce a novel signal processing method, CMF-EEMD, for analyzing wind turbine gearbox signals.
- To enhance the decomposition and analysis of complex gearbox fault signatures.
Main Methods:
- Developed a Combined Mode Function (CMF) comprising low (CL) and high (Ch) frequency components.
- Optimized noise amplitude for EEMD decomposition of CL and Ch.
- Applied Cyclic Autocorrelation Function (CAF) to intrinsic mode functions (IMFs) obtained from CMF-EEMD.
Main Results:
- The CMF-EEMD method demonstrated superior performance in resolving signal components compared to direct EEMD-CAF.
- Successfully applied to analyze multi-fault conditions in a wind turbine gearbox.
Conclusions:
- CMF-EEMD offers improved accuracy and effectiveness for wind turbine gearbox condition monitoring.
- The proposed method provides a more robust approach to diagnosing complex gearbox faults.
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