Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4704
Title: Fault detection and diagnosis in a food pasteurization process with Hidden Markov Models
Authors: Tokatlı, Figen
Cinar, Ali
Tokatlı, Figen
Keywords: Fault diagnosis
Food processing
Hidden Markov models
Pasteurization plants
Issue Date: Dec-2004
Publisher: John Wiley and Sons Inc.
Source: Tokatlı, F., and Cinar, A. (2004). Fault detection and diagnosis in a food pasteurization process with Hidden Markov Models. Canadian Journal of Chemical Engineering, 82(6), 1252-1262. doi:10.1002/cjce.5450820612
Abstract: Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a high-temperature-short-time pasteurization system showed that HMM can diagnose the faults with certain characteristics such as fault duration and magnitude.
URI: http://doi.org/10.1002/cjce.5450820612
http://hdl.handle.net/11147/4704
ISSN: 0008-4034
0008-4034
Appears in Collections:Food Engineering / Gıda Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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