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

Author

Chinnam, Ratna Babu ; Baruah, Pundarikaksha

Author_Institution

Dept. of Ind. & Manuf. Eng., Wayne State Univ., Detroit, MI, USA

Volume

4

fYear

2003

fDate

20-24 July 2003

Firstpage

2466

Abstract

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.

Keywords

condition monitoring; drilling machines; fault diagnosis; hidden Markov models; maintenance engineering; pattern clustering; unsupervised learning; autonomous diagnostics; autonomous prognostics; competitive learning driven HMM-based clustering; condition-based maintenance; drilling machine; health-state transition times; Clustering methods; Distributed computing; Drilling machines; Hardware; Hidden Markov models; Labeling; Manufacturing industries; Robustness; Software algorithms; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223951

Filename

1223951