A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features

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.

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

“…The rotational frequency of the motor is 29.95 Hz, and the sampling frequency is 12,000 Hz. According to the bearing parameters, the theoretical fault frequencies of the inner ring, outer ring, and rolling element are 162.18, 107.36, and 141.17 Hz, respectively [19]. The proposed algorithm parameters were initialized according to the characteristics of the vibration signal.…”

“…Currently, there are two common methods for pre-selecting the signal period. The first is to construct a suitable multi-objective optimization function and then use optimization algorithms such as grid search [ 18 ], grasshopper optimization algorithm [ 19 ], or particle swarm optimization [ 20 ] to identify the optimal period and filter length. However, these methods often require dozens of iterations, which is computationally complex and time-consuming.…”

An Optimal Parameter Selection Method for MOMEDA Based on EHNR and Its Spectral Entropy

“…Hence, unreasonable filter length L will weaken or enhance the energy of the original signal, and then cause misdiagnosis. To address the above-mentioned issues, scholars have introduced some optimization algorithms [ 36 , 37 ] to determine the fault period T and filter length L of MOMEDA, such as particle swarm optimization (PSO) algorithm [ 27 , 38 ], grasshopper optimization algorithm (GOA) [ 39 ] and so on. These parameter optimization algorithms mostly take kurtosis, envelope spectrum kurtosis (ESK) and other similar indexes as objective functions to improve MOMEDA.…”

Enhanced Characteristic Vibration Signal Detection of Generator Based on Time-Wavelet Energy Spectrum and Multipoint Optimal Minimum Entropy Deconvolution Adjusted Method

“…For instance, it is not a globally optimal filter but only a locally optimal one which can only obtain part of the impulse signal. To overcome the side-effect of MED, Mcdonald and Zhao [19] proposed a special method named Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) which employs the objective vector together with several D norms, and had obtained wide application [19][20][21][22][23][24].…”