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Abstract
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.
Highlights
Bearings are important components in rotating mechanical equipment, and their operating condition affects the overall working state of mechanical equipment
A bearing is prone to defects after prolonged use, and these defects can be useful during bearing fault diagnosis [1,2,3,4]
The periodic pulse signal is caused by the bearing fault, and the bearing fault type can be determined by analyzing the characteristic frequency of the periodic pulse signal
Summary
Bearings are important components in rotating mechanical equipment, and their operating condition affects the overall working state of mechanical equipment. Due to the complex working environment of the bearing, the vibration signals of the bearing collected by relevant equipment show obvious nonstationary and nonlinear characteristics [5,6,7,8,9]. The mathematical morphology filter, developed on the basis of mathematical morphology transformation, is an effective method of analyzing nonlinear signals. A traditional morphological filter uses a single-scale operation, so its noise-filtering effects are general; in addition, it may filter out useful fault signals, which may prevent a true reflection of the bearing fault signals [15,16,17,18]. Both simulation and experimental results show that the adaptive multiscale combined morphological filter has a better denoising effect and can retain useful signals better than the traditional filter
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