Abstract
The nonstationary components and noises contained in the bearing vibration signal make it particularly difficult to extract fault features, and minimum entropy deconvolution (MED), maximum correlated kurtosis deconvolution (MCKD), and fast spectral kurtosis (FSK) cannot achieve satisfactory results. However, the filter size and period range of multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) need to be set in advance, so it is difficult to achieve satisfactory filtering results. Aiming at these problems, a parameter adaptive MOMEDA feature extraction method based on grasshopper optimization algorithm (GOA) is proposed. Firstly, the multipoint kurtosis (MKurt) of MOMEDA filtered signal is used as the optimization objective, and the optimal filter size and periodic initial value which matched with the vibration signal can be determined adaptively through multiple iterations of GOA. Secondly, the periodic impact contained in the vibration signal is extracted by the optimized MOMEDA, and the fault features in the impact signal are extracted by Hilbert envelope demodulation. Finally, the simulation signal and bearing signal are processed by the proposed approach. The results show that the introduction of GOA not only solves the problem of parameter selection in MOMEDA, but also achieves better performance compared with other optimization methods. Meanwhile, the feasibility and superiority of the approach are fully proved by comparing it with the three methods MED, MCKD, and FSK after parameter optimization. Therefore, this approach provides a novel way and solution for fault diagnosis of the rolling bearing, gear, and shaft.
Highlights
The bearing is an important rotating part widely used in manufacturing, transportation, aerospace, and other fields, and one of the most vulnerable rotating components
The results show that the introduction of grasshopper optimization algorithm (GOA) solves the problem of parameter selection in multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), and achieves better performance compared with other optimization methods
A parameter adaptive MOMEDA feature extraction method based on GOA is proposed for solving the problem of the bearing fault diagnosis
Summary
The bearing is an important rotating part widely used in manufacturing, transportation, aerospace, and other fields, and one of the most vulnerable rotating components. Abboud et al [25] enhanced the impact component of the signal through MED, constructed the optimal frequency band by FSK, and extracted fault features by Hilbert envelope demodulation. Wan et al [27] calculated the period of MCKD according to the estimated characteristic frequency and extracted the early fault features contained in the filtered signal by FSK. The impact component contained in the mixed signal is extracted by MOMEDA, and the opposite of the MKurt is served as the objective function to optimize the filter parameters. The optimized MOMEDA is used to extract the impact component from the signals, and the fault feature is extracted through Hilbert envelope demodulation.
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