Hidden Markov Model Based Approach for Diagnosing Cause of Alarm Signals

TL;DR

This paper presents a hidden Markov model-based method for diagnosing fault causes from alarm sequences in industrial processes, enabling early fault detection and improved operator response.

Contribution

It introduces a novel approach using hidden Markov models to accurately identify fault causes from alarm sequences in industrial settings.

Findings

High accuracy in fault cause identification

Effective with short alarm subsequences

Applicable to real industrial case study

Abstract

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.