  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.
