The Msegram: A useful multichannel feature synchronous extraction tool for detecting rolling bearing faults. (15th March 2023) - Yuan, Jing; Song, Zhitian; Jiang, Huiming; Zhao, Qian; Zeng, Qingyu; Wei, Ying
Highlights: A multichannel feature synchronous extraction tool i.e. Msegram is proposed for detecting rolling bearing faults. Tensor synchronous denoising by HOSVD is studied for multichannel signal preprocessing. Multi-layer K -value MVMD is designed for multichannel synchronous adaptive decomposition. A tower-shaped EC map is proposed to visualize the feature output of multichannel bearing faults. Simulations and experimental cases of compound faults are used to testify Msegram with comparisons. Abstract: Multichannel signals collected by multiple sensors contain richer condition information of equipment than single-channel signals. However, such issues as simultaneous denoising, adaptive decomposition and synchronous extraction are still challenging for multichannel signals, which are beneficial to accurate fault diagnosis. Thus, a useful multichannel feature synchronous extraction tool is proposed for detecting rolling bearing faults, named as Msegram. First, a tensor synchronization denoising method based on high order singular value decomposition (HOSVD) is proposed for multichannel signal preprocessing. Original multichannel signals of testing bearings are constructed to be a third-order tensor by phase space reconstruction. Hereinto, a singular entropy increment is adopted to determine a reasonable singular order for each unfolding, and an optimal core tensor is obtained for local reconstruction analysis. Second, multi-layer K -value multivariate variational mode decomposition (MVMD) is designed after the multichannel noise reduction to realize synchronous adaptive filtering and decomposition for the multichannel signals. Third, inspired by the idea of the spectral kurtosis, a tower-shaped crest factor of envelope spectrum (EC) diagram similar to Fast Kurtogram (FK) is proposed to visualize the output of multichannel bearing fault feature results. According to the tower-shaped EC diagram with the maximum fault crest factor, the optimal analytic results of multichannel signals are selected and output to synchronously extract bearing fault features. Finally, repeatable simulations and two experimental fault cases of rolling bearings are implemented to demonstrate the practicability and effectiveness of the proposed method. The results show that the proposed method can successfully reveal the compound faults from experimental bearing and effectively identify the compound faults from locomotive wheelset bearing.