@inproceedings{5330e7ff8e1d4ee19cfe06739ecbbf30,
title = "Batch process monitoring and fault diagnosis based on multi-time-scale dynamic PCA models",
abstract = "Dynamics are inherent characteristics of batch processes, which can be divided into short time-scale dynamics within a batch duration and long time-scale dynamics across several batches. The interactions between process variables make different types of dynamics confounded. Under such situations, it is difficult to perform efficient fault diagnosis. In this paper, a batch process monitoring scheme is proposed to separate different types of process variations for modeling and perform monitoring and fault diagnosis with multi-time-scale dynamic principal component analysis (PCA) models. Simulation results show that the fault diagnosis efficiency is enhanced.",
keywords = "Batch process, Dynamics, Fault diagnosis, Monitoring, Principal component analysis",
author = "Yuan Yao and Furong Gao",
year = "2009",
doi = "10.3182/20090712-4-tr-2008.00154",
language = "English",
isbn = "9783902661548",
series = "IFAC Proceedings Volumes (IFAC-PapersOnline)",
publisher = "IFAC Secretariat",
number = "PART 1",
pages = "940--945",
booktitle = "International Symposium on Advanced Control of Chemical Processes, ADCHEM'09 - Proceedings",
edition = "PART 1",
}