Abstract
This study proposes a novel resonance demodulation frequency band selection method named the initial center frequency-guided filter (ICFGF) to diagnose the bearing fault. The proposed technology has a better performance on resisting the interference from the random impulses. More explicitly, the ICFGF can be summarized as two steps. In the first step, a variance statistic index is applied to evaluate the energy spectrum distribution, which can adaptively determine the center frequency of the fault impulse and suppress the interference from random impulse effectively. In the second step, a modified mayfly optimization algorithm (MMA) is applied to search the optimal resonance demodulation frequency band based on the center frequency from the first step, which has faster convergence. Finally, the filtered signal is processed by the squared envelope spectrum technology. Results of the proposed method for signals from an outer fault bearing and a ball fault bearing indicate that the ICFGF works well to extract bearing fault feature. Furthermore, compared with some other methods, including fast kurtogram, ensemble empirical mode decomposition, and conditional variance-based selector technology, the ICFGF can extract the fault characteristic more accurately.
Highlights
The contributions of this paper can be summarized as follows: one is that a novel index is proposed to determine the resonant frequency for the bearing fault features; the other is that a new distribution mode for the initial agents is proposed in this paper
To extract the bearing fault feature, this study proposes a new method to determine the optimal resonance frequency band (ORFB), which includes a novel energy spectrum statistic index and an mayfly optimization algorithm (MMA) optimization algorithm
The novel spectrum index is built based on the differences between the energy spectrum of Gaussian noise and the energy spectrum of the periodic impulses
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The three above indices worked better in resisting Gaussian noise but were still weak when random impulses existed in the measured signal To fill this gap, correlated kurtosis kurtogram [19] and periodicity-based kurtogram [20] were proposed to extract the bearing fault impulses. The CVB was applied to the time-frequency analysis result, which inherited the drawbacks of the short-time Fourier transform and was still weak to random impulses To overcome these two drawbacks, a novel method named the initial center frequency-guided filter (ICFGF) is proposed in the present study. The contributions of this paper can be summarized as follows: one is that a novel index is proposed to determine the resonant frequency for the bearing fault features; the other is that a new distribution mode for the initial agents is proposed in this paper.
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