Sparse components separation-based operational reliability assessment approach

As a metric to quantize the engineering system and plants quality, reliability has developed as a scientific discipline which is mainly rely on statistical analysis and life tests. However, with the improvement of mechanical system quality and service time, access to life tests and historical failure data become more and more difficult and time-consuming. To overcome the dependence of statistical failure data, a novel operational reliability assessment approach is proposed. System vibration response varies from operational states to states. In a bearing-rotor system, the vibration response of failure system is the impulse component. Besides, the vibration caused by abrasion is the harmonic component. For impulse and harmonic components extraction, a morphological component analysis (MCA) method based on basis pursuit denoising (BPDN) is used to decompose the vibration signals and reconstruct the impulse signals. Then classical time domain indexes of impulse signals are used as the observation sequence of a corresponding Hidden Markov Models (HMM) to assessment operational reliability. Finally, BPDN, traditional time-features and the proposed method are respectively applied in the operational reliability assessment of an experiment carried out on an aerospace bearing test rig. Comparison results confirmed the effectiveness of the proposed method for operational reliability assessment in bearing-rotor system.