LAUSR

Convex hulls based maximum margin classification has been widely studied for machine fault diagnosis, while its exploration for machine degradation modeling is seldom reported. In this study, a sparse and flexible convex-hull representation for machine degradation modeling is proposed to realize degradation trajectory tracking and fault diagnosis at a same time. First, considering using vibration data as health monitoring signals, globally normal and abnormal spectral lines can be obtained based on the fast Fourier transform and they are, respectively, characterized as individually flexible convex hulls. Subsequently, a sparse and flexible convex-hull representation degradation model is constructed by simultaneously finding the closest pair of samples and its sparse regularization between normal and abnormal convex hulls. Finally, a health indicator can be developed for early fault detection and degradation trajectory tracking during a machine life cycle. Meanwhile, quick fault diagnosis can be realized by finding a difference between the optimal closest samples in a normal convex hull and an abnormal convex hull. Two experimental cases are used to show the effectiveness and superiority of the proposed model to recent existing works.