Process fault prediction and prognosis based on a hybrid technique

Galagedarage Don, Mihiran Pathmika (2019) Process fault prediction and prognosis based on a hybrid technique. Masters thesis, Memorial University of Newfoundland.

[img] [English] PDF - Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

Download (4MB)


The present study introduces a novel hybrid methodology for fault detection and diagnosis (FDD) and fault prediction and prognosis (FPP). The hybrid methodology combines both data-driven and process knowledge driven techniques. The Hidden Markov Model (HMM) and the auxiliary codes detect and predict the abnormalities based on process history while the Bayesian Network (BN) diagnoses the root cause of the fault based on process knowledge. In the first step, the system performance is evaluated for fault detection and diagnosis and in the second step, prediction and prognosis are evaluated. In both cases, an HMM trained with Normal Operating Condition data is used to determine the log-likelihoods (LL) of each process history data string. It is then used to develop the Conditional Probability Tables of BN while the structure of BN is developed based on process knowledge. Abnormal behaviour of the system is identified through HMM. The time of detection of an abnormality, respective LL value, and the probabilities of being in the process condition at the time of detection are used to generate the likelihood evidence to BN. The updated BN is then used to diagnose the root cause by considering the respective changes of the probabilities. Performance of the new technique is validated with published data of Tennessee Eastman Process. Eight of the ten selected faults were successfully detected and diagnosed. The same set of faults were predicted and prognosed accurately at different levels of maximum added noise.

Item Type: Thesis (Masters)
Item ID: 13714
Additional Information: Includes bibliographical references.
Keywords: HMM, Bayesian Network, Fault Prediction, Prognosis, Fault Diagnosis
Department(s): Engineering and Applied Science, Faculty of
Date: May 2019
Date Type: Submission
Library of Congress Subject Heading: System failures (Engineering)--Prevention; Process control.

Actions (login required)

View Item View Item


Downloads per month over the past year

View more statistics