Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)
Rubén Morales-Menéndez, Nando de Freitas, David Poole
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.