Fault Detection and Diagnosis in a Food Pasteurization Process With Hidden Markov Models

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.