@inproceedings{5eebfad333fa4fd883fccacab8e19070,
title = "Reconstruction based fault prognosis for continuous processes",
abstract = "In this paper, a fault prognosis approach for continuous processes with hidden faults is proposed based on the principal component analysis structure and multivariate time series prediction. It is assumed that the actual fault is a slowly time-varying autocorrelated process and the fault can be completely reconstructed. Fault magnitude is estimated via reconstruction first, then predicted by a vector ARMA model. A new fault detection policy is proposed and the denoising effect on prediction modeling is studied. The case study of CSTR demonstrates the efficiency of the approach and the validity of the analysis. {\textcopyright} 2009 IFAC.",
keywords = "Fault prognosis, Fault reconstruction, Principal component analysis, Vector ARMA",
author = "Gang Li and {Joe Qin}, S. and Yindong Ji and Donghua Zhou",
year = "2009",
month = jun,
doi = "10.3182/20090630-4-ES-2003.00168",
language = "English",
isbn = "9783902661463",
series = "IFAC Proceedings Volumes",
publisher = "Elsevier Ltd.",
number = "8",
pages = "1019--1024",
booktitle = "Proceedings of the 7th IFAC Symposium onFault Detection, Supervision and Safety of Technical Processes",
address = "United Kingdom",
note = "7th IFAC Symposium onFault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS'09) ; Conference date: 30-06-2009 Through 03-07-2009",
}