Hob vibration signal denoising and effective features enhancing using improved complete ensemble empirical mode decomposition with adaptive noise and fuzzy rough sets

Abstract

The acquired hob vibration signals are inevitably contaminated by noise in the industrial environment, which changes the vibration signal frequency distribution and reduces the accuracy of feature extraction and hob wear identification. To solve this problem, a novel hob vibration signal denoising and effective feature enhancement method, CEEMDAN-FRS, is proposed based on the improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and fuzzy rough sets (FRS). First, the effective frequency distributions of gear hobbing, particularly the modulation sidebands, were obtained as prior knowledge by analyzing the hob vibration response mechanism. Then, the two key parameters of CEEMDAN (i.e., noise standard deviation and ensemble size) were adaptively determined based on signal characteristics to achieve improved decomposition compared with the use of fixed values. The evaluation and selection of intrinsic mode functions (IMFs) based on a single feature such as kurtosis or root mean square, only focus on the partial characteristics, leading to an identification bias. Thus, 11 features with different sensitivities of IMFs are weighted based on FRS, and fused as a unified feature to conduct a comprehensive IMF evaluation. Finally, a reweighted IMF reconstruction strategy is proposed. The comparisons of the proposed method and related approaches to hob vibration signals show that the proposed method achieves better performance in terms of effective feature enhancement, noise removal, and signal-to-noise ratio improvement