Real-Time Monitoring of Complex Industrial Processes with Particle Filters
Abstract
This paper discusses the application of particle filtering algorithms to fault diagnosis in complex industrial processes. We consider two ubiq- uitous processes: an industrial dryer and a level tank. For these appli- cations, we compared three particle filtering variants: standard parti- cle filtering, Rao-Blackwellised particle filtering and a version of Rao- Blackwellised particle filtering that does one-step look-ahead to select good sampling regions. We show that the overhead of the extra process- ing per particle of the more sophisticated methods is more than compen- sated by the decrease in error and variance.
Cite
Text
Morales-Menéndez et al. "Real-Time Monitoring of Complex Industrial Processes with Particle Filters." Neural Information Processing Systems, 2002.Markdown
[Morales-Menéndez et al. "Real-Time Monitoring of Complex Industrial Processes with Particle Filters." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/moralesmenendez2002neurips-realtime/)BibTeX
@inproceedings{moralesmenendez2002neurips-realtime,
title = {{Real-Time Monitoring of Complex Industrial Processes with Particle Filters}},
author = {Morales-Menéndez, Rubén and de Freitas, Nando and Poole, David},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {1457-1464},
url = {https://mlanthology.org/neurips/2002/moralesmenendez2002neurips-realtime/}
}