Chun Lv, Peilin Zhang, Dinghai Wu, Bing Li, Yunqiang Zhang
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Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter
Bearing fault signal analysis is an important means of bearing fault diagnosis. To effectively eliminate noise in a fault signal, an adaptive multiscale combined morphological filter is proposed based on the theory of mathematical morphology. Both simulation and experimental results show that the adaptive multiscale combined morphological filter can remove noise more thoroughly and retain details of the fault signal better than the dual-tree complex wavelet filter, traditional morphological filter, adaptive singular value decomposition method (ASVD), and improved switching Kalman filter (ISKF). The adaptive multiscale combined morphological filter considers both positive and negative impulses in the signal; therefore, it has strong adaptability to complex noise in the environment, making it an effective new method for bearing fault diagnosis.
期刊介绍:
This comprehensive journal provides the latest information on rotating machines and machine elements. This technology has become essential to many industrial processes, including gas-, steam-, water-, or wind-driven turbines at power generation systems, and in food processing, automobile and airplane engines, heating, refrigeration, air conditioning, and chemical or petroleum refining. In spite of the importance of rotating machinery and the huge financial resources involved in the industry, only a few publications distribute research and development information on the prime movers. This journal is the first source to combine the technology, as it applies to all of these specialties, previously scattered throughout literature.