Development of Data-Driven Methods for Alarm Flood Monitoring and Analysis
Creators: Parvez, Md Rezwan
Publisher: name: University of Alberta Library
Year: 2024

<p>Alarm floods present substantial challenges to industrial process safety,<br>
given their diverse causes and potential for severe consequences. Modern<br>
process industries involve sophisticated networks of devices that are interconnected<br>
in both upstream and downstream directions. The interconnected<br>
nature of these units and devices is a result of complex industrial production<br>
processes and the necessity to effectively manage numerous variables. Therefore,<br>
when a fault or abnormal condition occurs in an upstream or downstream<br>
component, it can lead to fault propagation due to the interconnected nature<br>
of the units and the feedback mechanisms that exist to control and regulate the<br>
overall system. Thus, this phenomenon leads to an increased number of alarms<br>
being generated across the system, causing an alarm flood. As industrial processes<br>
become more complex, plant operators often find it increasingly difficult<br>
to respond effectively, particularly during an alarm flood. The increased rate<br>
of alarms during an alarm flood overwhelms plant operators, resulting in delayed<br>
response times and further deterioration of the situation. Consequently,<br>
decision supports in alarm flood situations are in great demand to assist plant<br>
operators in assessing the root causes and taking corrective actions in time.<br>
Therefore, this thesis focuses on developing data-driven methods to efficiently<br>
manage alarm flood situations and minimize their impacts.<br>
Three research topics are considered. Firstly, early prediction of an incoming<br>
alarm flood sequence can provide valuable information to industrial operators, facilitating them to take corrective actions in time. A real-time<br>
pattern matching and ranking approach is proposed in this work to conduct<br>
similarity analysis under an online alarm flood situation and to export the<br>
results as a ranking list of historical alarm flood sequences. Unit and setbased<br>
pre-matching mechanisms are proposed to remove irrelevant sequences,<br>
and a set-based indexing and extension strategy is applied to further avoid<br>
unnecessary computation. Real-time decision supports in the form of ranking<br>
of similar historical alarm flood sequences are presented to the industrial<br>
operators.<br>
Secondly, a novel association rule mining approach is proposed for real-time<br>
prediction of alarm events during an alarm flood situation. This approach integrates<br>
a modified compact prediction tree model with new features, namely,<br>
the timetable and co-occurrence matrix, and is constructed based on historical<br>
alarm sequences. An alarm relevancy detection strategy is designed to<br>
identify and eliminate irrelevant alarms from the ongoing alarm flood. Furthermore,<br>
the proposed approach provides confidence intervals of the time differences<br>
between the subsequent predicted alarm events for time prediction.<br>
Such real-time assistance during alarm flood situations can greatly simplify<br>
the decision-making process for industrial operators.<br>
Finally, a reinforcement learning (RL) approach is proposed for early prediction<br>
of industrial alarm floods and to provide real-time guidance to plant<br>
operators in prompt mitigation of such situations. Based on various association<br>
rule metrics, irrelevant alarms are identified and eliminated to avoid<br>
inaccurate recommendations. A sequence reconstruction strategy is adopted<br>
to generate potential online scenarios by exploiting the alarm relations that<br>
exist in the historical sequences. Additionally, several criteria are introduced<br>
and implemented in the existing historical sequences to reformulate the training set for improved learning. To ensure accuracy and early recommendations,<br>
a double deep Q-network (DDQN) algorithm is incorporated into the proposed<br>
method.<br>
The effectiveness of these proposed methods is demonstrated by industrial<br>
case studies based on real industrial data from an oil refinery plant.<br>
By adopting these proposed approaches, plant operators could handle alarm<br>
floods proactively, resulting in an improvement in operational efficiency and<br>
safety.</p>