Hidden markov model-based approach for process monitoring

Abstract

Hidden Markov Models (HMMs) are a popular and ubiquitous tool for modelling a large range of time series data. It has been applied successfully to various complex problems, being especially effective with those requiring a huge amount of measured data, such as pattern recognition in speech, handwriting and even facial recognition. Since Fault detection and diagnosis is an important problem in process engineering recent studies are focusing on developing new techniques which are more accurate, sensitive to small faults, with no time delay and can monitor multi-mode process ef- fectively. In order to satisfy these requirements, a huge data are needed and a suitable model to process these data is the HMM. The main objective of this work is to develop novel HMM-based approach to diagnose various operating modes of a process includ- ing Bayesian methods for mode selection. The mode in this work refers to process operational statuses such as normal or abnormal operating conditions.