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Study on a Novel Fault Damage Degree Identification Method Using High-Order Differential Mathematical Morphology
Huimin Zhao1,2,3,4, Rui Yao1, Ling Xu2
1Software Institute, Dalian Jiaotong University, Dalian 116028, China.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study introduces a new method, high-order difference mathematical morphology gradient spectrum entropy (HMGSEDI), for accurately identifying bearing damage. This technique enhances the quantitative measurement of weak fault signals in rotating machinery.
Area of Science:
- Mechanical Engineering
- Signal Processing
- Condition Monitoring
Background:
- Rolling bearing faults often present weak signals, making quantitative damage assessment challenging.
- Existing methods struggle with the accurate measurement of subtle fault indicators in machinery.
Purpose of the Study:
- To propose a novel quantitative method for identifying the degree of damage in rolling bearings.
- To address the limitations of current techniques in detecting and measuring weak fault signals.
Main Methods:
- Developed the high-order difference mathematical morphology gradient spectrum entropy (HMGSEDI) method.
- Analyzed the influence of structural element scales to determine optimal parameters.
- Defined a discrimination concept to quantify differences between entropy measures.
Main Results:
- The HMGSEDI method demonstrated superior effectiveness in identifying bearing fault damage degrees.
- Experimental validation showed a significant improvement in the accuracy of fault damage identification.
- The proposed method successfully processed vibration signals under both no-load and load conditions.
Conclusions:
- HMGSEDI is an effective quantitative method for identifying bearing fault damage.
- This approach offers a new pathway for fault prediction in rotating machinery.
- The method significantly enhances the accuracy and reliability of condition monitoring.
