Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA

Abstract

In order to separate the sub-signals and extract the feature frequency in the signal accurately, we proposed a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) feature extraction method based on the improved grasshopper optimization algorithm (IGOA). The method not only improved the local optimal problem of GOA, but could also determine the bandwidth threshold and B-spline order of TVF-EMD adaptively. Firstly, a nonlinear decreasing strategy was introduced in this paper to adjust the decreasing coefficient of GOA dynamically. Then, energy entropy mutual information (EEMI) was introduced to comprehensively consider the energy distribution of the modes and the dependence between the modes and the original signal, and the EEMI was used as the objective function. In addition, TVF-EMD was optimized by IGOA and the optimal parameters matching the input signal were obtained. Finally, the feature frequency of the signal was extracted by analyzing the sensitive mode with larger kurtosis. The optimization experiments of 23 sets of benchmark functions showed that IGOA not only enhanced the balance between exploration and development, but also improved the global and local search ability and stability of the algorithm. The analysis of the simulation signal and bearing signal shows that the parameter-adaptive TVF-EMD method can separate the modes with specific physical meanings accurately. Compared with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), TVF-EMD with fixed parameters and GOA-TVF-EMD, the decomposition performance of the proposed method is better. The proposed method not only improved the under-decomposition, over-decomposition and modal aliasing problems of TVF-EMD, but could also accurately separate the frequency components of the signal and extract the included feature information, so it has practical significance in mechanical fault diagnosis.

Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization

“…In addition to CC and Kur, other indices, including mutual information (MI) [33], energy entropy (EE) [34], and energy entropy mutual information (EEMI) [28], are commonly used to evaluate the degree of optimization in signal processing. As shown in Figure 7 indicates that CCKur can be regarded as the best optimization model.…”

“…In addition, there are swarm intelligence optimization technologies that imitate the behaviors of various organisms, such as ants [23], particle swarm optimization [24], fish schools [25], glow worm swarm optimization [26], and grey wolves [27], to achieve optimal parameter estimation. Zhou et al [28] proposed the parameter-adaptive TVF-EMD feature extraction method, based on improved GOA, to deal with the mechanical fault diagnosis. However, the Genetic algorithm and the swarm intelligence optimization algorithm can be categorized as population optimization algorithms.…”