Aero-engine bearing fault detection: A clustering low-rank approach

Different from classic exponential-decaying feature waveform, the fault information of high-speed aero-engine bearing presents an overlapping distortion or even approximately-harmonic morphology, which brings a new challenge for popular bearing diagnosis techniques. From the nonlocal similarity perspective of vibration waveforms, this study first reveals the singular value coupled pattern among strong similarity structure, weak similarity structure and noises, which consequently yields the overlapping coherent pathology in the SVD-based low-rank domain. Leveraging an adaptive clustering into the low-rank regularization theory, a tailored diagnostic framework (dubbed AMS-CIuLR) is then proposed to address the challenging problem. The main highlight of AMS-CIuLR is to adaptively cluster local feature waveforms into multiple isolated groups to guarantee that different similarity structures are reliably concentrated into their matched low-rank domain, which effectively eliminates the singular value overlapping coherent pathology while keeping the structural completeness of relatively weak similarity features. Moreover, an alternative minimization solver is developed for the AMS-CIuLR model to rapidly achieve a satisfying stationary solution. The embedded clustering operation's necessity and AMS-CIuLR's superiority are profoundly investigated through a set of comprehensive numerical studies. An aero-engine bearing experiment under high-speed conditions with rotational speed up to 25,000 rev/min is further conducted to corroborate AMS-CIuLR's superiority. Experimental results show that the proposed framework substantially outperforms state-of-the-art bearing fault detection methods in terms of both information volumes and visual quality. (C) 2019 Elsevier Ltd. All rights reserved.