Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

Abstract

The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator termed integrated fault-impact measure index (IFIMI) guiding the improved dung beetle optimizer (IDBO). An adaptive selection method, using a weight factor, chooses the intrinsic mode function (IMF) containing principal fault data. IMOMEDA whose key parameters are determined by a novel combinatorial strategy is then employed to deconvolute selected fault components, enhancing periodic fault impulses by removing complex interferences and ambient noise. The deconvoluted signal undergoes enhanced envelope spectrum processing to extract fault frequencies and identify fault types. Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for compound faults diagnosis of rolling bearings, showcasing its superiority over existing techniques.

An improved envelope spectrum via Hoyer index-gram for bearing fault extraction

“…The simulated signal is used as an example to illustrate the band segmentation method of IESHoyergram in detail. As shown in figure 5, by adjusting the window width N w in the FSC, various spectrograms with the spectral frequency f as the horizontal coordinate and the spectral correlation matrix as the vertical coordinate are obtained and used as the trend spectrum, where the values of N w are 2 4 , 2 5 , 2 6 , 2 7 and 2 8 . Figures 6(a To provide a more intuitive comparison of the optimal frequency bands selected by the five algorithms, their results are displayed in figure 9.…”

“…bearing fault diagnosis, and the acquisition of vibration signals relies heavily on precise measurements by mechanical equipment such as sensors [4,5].…”