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Hidden Markov model based approach for diagnosing cause of alarm signals
Author(s) -
Ariamuthu Venkidasalapathy Joshiba,
Kravaris Costas
Publication year - 2021
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.17297
Subject(s) - alarm , hidden markov model , path (computing) , sequence (biology) , fault (geology) , process (computing) , false alarm , manual fire alarm activation , real time computing , markov chain , fault detection and isolation , markov process , computer science , markov model , identification (biology) , operator (biology) , engineering , hidden semi markov model , artificial intelligence , machine learning , markov property , mathematics , computer network , statistics , repressor , actuator , aerospace engineering , chemistry , biology , genetics , operating system , biochemistry , transcription factor , botany , seismology , gene , geology
When a fault occurs in a process, it slowly propagates within the system and affects the measurements triggering a sequence of alarms in the control room. The operators are required to diagnose the cause of alarms and take necessary corrective measures. The idea of representing the alarm sequence as the fault propagation path and using the propagation path to diagnose the fault is explored. A diagnoser based on hidden Markov model is built to identify the cause of the alarm signals. The proposed approach is applied to an industrial case study: Tennessee Eastman process. The results show that the proposed approach is successful in determining the probable cause of alarms generated with high accuracy. The model was able to identify the cause accurately, even when tested with short alarm sub‐sequences. This allows for early identification of faults, providing more time to the operator to restore the system to normal operation.