Variable-scale evolutionary adaptive mode denoising in the application of gearbox early fault diagnosis. (15th February 2023) - Liu, Rui; Ding, Xiaoxi; Zhang, Yudong; Zhang, Mingkai; Shao, Yimin
Abstract: Due to the influence of gearbox structure complexity, vibration signals are always corrupted by heavy multiscale noise interference distributed in modulation frequency bands, which brings great challenge for early fault identification. However, conventional adaptive noise cancellation methods mainly focus on a comprehensive denoising effect while ignore improper reference induced by multiscale characteristics of noise interference. This will lead to two major problems. On one aspect, reference filtering at sensitive scales is not mutually independent, and it has a negative influence on each other. On the other aspect, improper reference may cause losses of fault-related information. These limitations greatly hinder the fault features identification, especially in an early fault stage of a fault. Aiming at these problems, this study proposes a novel variable-scale evolutionary adaptive mode denoising method (VEAMD) for weak feature enhancement of gearbox early fault diagnosis. First, to sense the multiscale distribution characteristics of a desired signal, an ergodic VMD process with a dual domain energy factor ( DDEF ) is presented, where a series of Wiener filters are adaptively designed. Secondly, the desired signal and a reference signal are input into these Wiener filters to generate multiple mode subsignals with wideband noise suppressed. Meanwhile, multiscale noise interferences are separated into different scales as reference bases for mode denoising. Third, via evolutionary digital filters (EDFs), a refined reference filtering is performed within mode scale to obtain multiple intrinsic mode subsignals (IMSs), namely mode denoising. Finally, the Pearson correlation coefficients between two parts of each subsignal pair is employed as a weight to synthesize a denoised signal. In this manner, VEAMD effectively tackles an improper reference at local scales by a series of adaptive mode denoising, achieving more significant noise cancellation capability and an effect of weak feature enhancement, which is conducive for a timely and accurate early gearbox fault diagnosis. Simulation and experiment prove the superiority of VEAMD over other four methods.