Morphological Component Analysis-Based Hidden Markov Model for Few-Shot Reliability Assessment of Bearing

Reliability is of great significance in ensuring the safe operation modern industry, which mainly relies on data analysis and life tests. However, as mechanical systems becomes increasingly longer with rapid development manufacturing collection historical failure progressively more time-consuming. In this paper, a few-shot reliability assessment approach proposed order to overcome dependence data. Firstly, vibration response bearing was illustrated. Then, based analysis, morphological component (MCA) method sparse representation theory used decompose signals extract impulse signals. After components’ reconstruction, their statistical indexes were utilized input observation vector Mixture Gaussians Hidden Markov Model (MoG-HMM) for estimation. Finally, experimental dataset an aerospace analyzed via method. The comparison results illustrate effectiveness assessment.