Fault Detection in Continuous and Periodic Industrial Chemical Processes with Hidden Markov Models

Gustavo Matheus de Almeida and Song Won Park


Fault detection, Hidden Markov model, Signal processing, DAMADICS benchmark


The development of automatic and reliable fault detection systems is still a challenge nowadays. Chemical processes are complex by nature by presenting non linear dynamics, multiple modes with constant interchanges, and spatial and serial correlations, to mention a few. To address these issues, this work explores the hidden Markov model (HMM) technique to construct a fault detection system for continuous and periodic processes. The DAMADICS actuator benchmark, with thirty four abrupt fault scenarios, was used for evaluation purposes. Abrupt faults of low magnitude are challenging and of great interesting in practice. The results obtained with the proposed methodology were compared to classical multivariate statistical process control (MSPC) techniques. They show a significant higher performance leading to earlier fault detection given a fixed false alarm rate of 1%.

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