Subspace-based MVE for performance degradation assessment of aero-engine bearings with multimodal features

Performance degradation assessment is an important concept in prognostics and health management (PHM) of complex engineering systems. In this study, a novel method utilizing subspace-based minimum volume ellipsoid (SMVE) for bearing performance degradation assessment is proposed. The statistical features extracted from vibration signals are seen as multimodal homologous features in time, frequency and wavelet modalities, thus it needs to consider the differences in variance for the homologous features as well as covariance among them. The subspaces are used to model the homologous features of different frequency bands since they can capture the dynamic information. Based on subspaces, the proposed SMVE model covering most or all of the normal data by a unique ellipsoid with minimum volume considers variance of each dimension adaptively. A performance degradation index is designed based on the SMVE. The proposed method is applied on the vibration signals acquired in run-to-failure tests of aero-engine bearings and common bearings. By comparison with the commonly used time-domain features and traditional minimum volume ellipsoid method, the results demonstrate the effectiveness and superiority of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.