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| Composite fault diagnosis method of rolling bearing based on consistent optimization index |
| ZHANG Long1, CAI Binghuan1, XIONG Guoliang1, HU Junfeng2 |
1.School of Mechatronics ＆ Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China;
2.Institute of Science and Technology, Nanchang Railway Bureau, Nanchang 330002, China |
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Abstract Cycle impact caused by rolling bearing local fault transmitted from bearing to sensor is affected by transmission path, environmental noise and accidental impact interference, which makes fault feature extraction difficult and diagnosis effect poor.Here, a composite fault diagnosis method of rolling bearing based on the maximum correlation kurtosis deconvolution and adjustable quality factor wavelet transform was proposed.The former was used to weaken the influence of transfer path, while the latter was used to suppress noise components with filtering, and their parameter optimizations consistently took the correlated kurtosis, which could consider characteristics of rolling bearing fault impact cycle, as the optimization index to ensure the overall effect of feature extraction.Meanwhile, this index could not be affected by accidental impact interference.Simulated and test signals were analyzed using the proposed method, and the results were compared to those using the common methods, such as, fast spectral kurtosis to verify the effectiveness and superiority of the proposed method in rolling bearing fault diagnosis.
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Received: 09 December 2019
Published: 15 May 2021
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