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

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2 Author(s)
Chinnam, R.B. ; Dept. of Ind. & Manuf. Eng., Wayne State Univ., Detroit, MI, USA ; Baruah, P.

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.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003