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| Novel early fault detection and diagnosis for rolling element bearings in graph spectrum domain |
| CHEN Zixu1,2,3，ZHU Zhenjie1,2,3，LU Guoliang1,2,3 |
1.School of MechanicalEngineering, Shandong University, Jinan 250061, China;
2.Key Laboratory of High-efficiency and Clean Mechanical Manufacture of MOE, Shandong University, Jinan 250061, China;
3.National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China |
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Abstract Health status online diagnosis is an important way to ensure the reliable operation of rolling element bearings(REB). As a self-adaptive decomposition method, local mean decomposition(LMD) can describe the non-stationary signal into multi-scale. However, the decomposed component signal always has a large scale and is hard to extract weak fault features. To solve these problems, a novel early fault detection and diagnosis method for rolling element bearings in graph spectrum domain is proposed. We first decompose the vibration signal into multi-scale by LMD. Based on the generated component signal, the graph theory is used for dynamically modeling of rolling element bearings. Then a quantitative index of dynamic characteristics can be established by calculating the similarity between adjacent models, pauta criterion is employed to make early fault detection. Finally, the pattern recognition method is used to make fault diagnosis. The experiment on XJTU-SY and Case Western Reserve University(CWRU) data sets demonstrate the effectiveness of our method.
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Received: 14 October 2020
Published: 28 March 2022
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