| Citation: | YU Jianbo, LYU Jingxiang, CHENG Hui, et al. Fault diagnosis for rolling bearing based on ITD and improved morphological filter[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(2): 241-249. doi: 10.13700/j.bh.1001-5965.2017.0114(in Chinese)
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For extracting fault impulse components embedded in the vibration signal with much noise and harmonics, an improved morphological filter algorithm called average combination difference morphological filter (ACDIF) is proposed on the basis of the four basic morphological operators. Then ACDIF is combined with the intrinsic time scale decomposition (ITD) and the ITD-ACDIF method is employed in the fault diagnosis for rolling bearing. In the ITD-ACDIF fault diagnosis method, ITD is applied to the original vibration signal and a series of proper rotation components (PRC) are obtained, and then the kurtosis is regarded as criterion to select effective PR components which contain much fault-related information. After that, ACDIF filtering is performed on each effective PR in order to pick up bidirectional impulses, and filtered PRs are combined into a signal. Finally, the fault feature is extracted from reconstructed signal by amplitude spectrum. The experimental results on simulated signal and actual bearing vibration signal demonstrate that the proposed method can effectively suppress noise interference and extracting the characteristics of impact fault under the strong background noise to realize fault diagnosis of equipment.
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