Title :
Fault diagnosis of the TE process based on discrete hidden Markov model
Author :
Zhang Hui ; Fang Hua-jing ; Lisha Xia
Author_Institution :
Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
It turns out that a prerequisite to widespread deployment of condition-based maintenance (CBM) technology and practice in industry is effective fault diagnosis and prognosis. Diagnosis is an assessment about the current health of a system based on observed symptoms. Prognosis is to predict the progression of a fault condition to system failure and estimate the remaining useful life (RUL) of the system. Consequently diagnosis is essential to security maintenance of the industry process. This paper presents a method based on discrete hidden Markov model (DHMM) for carrying out diagnosis. The proposed method was validated on a chemical process-the Tennessee Eastman process. The result indicates the effectiveness of this method.
Keywords :
chemical engineering; condition monitoring; discrete systems; failure (mechanical); failure analysis; fault diagnosis; hidden Markov models; maintenance engineering; remaining life assessment; CBM technology; DHMM; TE process; Tennessee Eastman process; chemical process; condition-based maintenance technology; discrete hidden Markov model; fault condition progression; fault diagnosis; fault prognosis; industry process; remaining useful life estimation; security maintenance; system failure; Electronic mail; Fault diagnosis; Hidden Markov models; Industries; Maintenance engineering; Markov processes; Process control; Fault Diagnosis; Hidden Markov Model; Tennessee Eastman Process;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561715
