Bayesian Fault Diagnosis Using Process Knowledge of Response Information

DOI

10.2991/icadme-15.2015.358How to use a DOI?

Keywords

Fault detection and diagnosis, Bayesian inference, response information

Abstract

Process fault diagnosis is a topic of significant practical interest. Bayesian fault diagnosis methods have been developed to identify the problem source from all monitors of the process. However in a large scale industrial process, taking all the monitors into account not only increases computation burdens but also leads to spurious diagnosis. This paper proposes a new approach to obtain a more reliable diagnosis under Bayesian frame. It explicitly takes the process knowledge expressed as response matrix into consideration to estimate the likelihood in Bayesian inference. The simulation demonstrates that the proposed approach is able to improve the diagnosis even when some abnormal mode data is sparse or not available in the historical dataset.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wenbing Zhu
AU  - Ruohan Chen
AU  - Sun Zhou
PY  - 2015/10
DA  - 2015/10
TI  - Bayesian Fault Diagnosis Using Process Knowledge of Response Information
BT  - Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering
PB  - Atlantis Press
SP  - 1937
EP  - 1940
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-15.2015.358
DO  - 10.2991/icadme-15.2015.358
ID  - Zhu2015/10
ER  -