Bayesian Fault Diagnosis Using Process Knowledge of Response Information
- 10.2991/icadme-15.2015.358How to use a DOI?
- Fault detection and diagnosis, Bayesian inference, response information
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.
- © 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/).
Cite this article
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 -