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Development of Data-Driven Methods for Alarm Flood Monitoring and Analysis
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- Author / Creator
- Parvez, Md Rezwan
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Alarm floods present substantial challenges to industrial process safety,
given their diverse causes and potential for severe consequences. Modern
process industries involve sophisticated networks of devices that are interconnected
in both upstream and downstream directions. The interconnected
nature of these units and devices is a result of complex industrial production
processes and the necessity to effectively manage numerous variables. Therefore,
when a fault or abnormal condition occurs in an upstream or downstream
component, it can lead to fault propagation due to the interconnected nature
of the units and the feedback mechanisms that exist to control and regulate the
overall system. Thus, this phenomenon leads to an increased number of alarms
being generated across the system, causing an alarm flood. As industrial processes
become more complex, plant operators often find it increasingly difficult
to respond effectively, particularly during an alarm flood. The increased rate
of alarms during an alarm flood overwhelms plant operators, resulting in delayed
response times and further deterioration of the situation. Consequently,
decision supports in alarm flood situations are in great demand to assist plant
operators in assessing the root causes and taking corrective actions in time.
Therefore, this thesis focuses on developing data-driven methods to efficiently
manage alarm flood situations and minimize their impacts.
Three research topics are considered. Firstly, early prediction of an incoming
alarm flood sequence can provide valuable information to industrial operators, facilitating them to take corrective actions in time. A real-time
pattern matching and ranking approach is proposed in this work to conduct
similarity analysis under an online alarm flood situation and to export the
results as a ranking list of historical alarm flood sequences. Unit and setbased
pre-matching mechanisms are proposed to remove irrelevant sequences,
and a set-based indexing and extension strategy is applied to further avoid
unnecessary computation. Real-time decision supports in the form of ranking
of similar historical alarm flood sequences are presented to the industrial
operators.
Secondly, a novel association rule mining approach is proposed for real-time
prediction of alarm events during an alarm flood situation. This approach integrates
a modified compact prediction tree model with new features, namely,
the timetable and co-occurrence matrix, and is constructed based on historical
alarm sequences. An alarm relevancy detection strategy is designed to
identify and eliminate irrelevant alarms from the ongoing alarm flood. Furthermore,
the proposed approach provides confidence intervals of the time differences
between the subsequent predicted alarm events for time prediction.
Such real-time assistance during alarm flood situations can greatly simplify
the decision-making process for industrial operators.
Finally, a reinforcement learning (RL) approach is proposed for early prediction
of industrial alarm floods and to provide real-time guidance to plant
operators in prompt mitigation of such situations. Based on various association
rule metrics, irrelevant alarms are identified and eliminated to avoid
inaccurate recommendations. A sequence reconstruction strategy is adopted
to generate potential online scenarios by exploiting the alarm relations that
exist in the historical sequences. Additionally, several criteria are introduced
and implemented in the existing historical sequences to reformulate the training set for improved learning. To ensure accuracy and early recommendations,
a double deep Q-network (DDQN) algorithm is incorporated into the proposed
method.
The effectiveness of these proposed methods is demonstrated by industrial
case studies based on real industrial data from an oil refinery plant.
By adopting these proposed approaches, plant operators could handle alarm
floods proactively, resulting in an improvement in operational efficiency and
safety. -
- Graduation date
- Spring 2024
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.