Autonomous diagnostics and prognostics through competitive learning driven HMM-based clustering

A prerequisite to effective wide-spread deployment of condition-based maintenance (CBM) practices is effective diagnostics and prognostics. This paper presents a novel method for employing HMMs for autonomous diagnostics as well as prognostics. The diagnostics module exploits competitive learning to achieve HMM-based clustering. The prognostics module builds upon the diagnostics module to compute joint distributions for health-state transition times. The proposed methods were validated on a physical test bed; a drilling machine.