Nearest neighbor convex hull for health indicator construction

Abstract

Developing a suitable and monotonous health index (HI) that can be used to represent a whole degradation process is a key step for continuous machine health monitoring during its life cycle. It is expected that the potential HI is able to inform incipient fault moment and then track machine degradation trajectories effectively and monotonically. Previously, nearest neighbor convex hull classification (NNCHC) has been widely applied for fault classification. In this paper, a HI construction methodology for machine life cycle health monitoring based on NNCHC is proposed. Firstly, a normal convex hull is modeled based on normal vibration data to fully characterize machine health conditions. Afterward, two HIs are constructed based on ℓ 1 norm and ℓ 2 norm distances between the normal convex hull and test points. The superiority of the developed approach in this study lies in the flexible and efficient development of a HI for fault progress tracking. Moreover, the only usage of a normal dataset in the proposed methodology is closer to real application.