Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations

Alarm flood similarity analysis (AFSA) methods are frequently used as a primary step for root-cause analysis, alarm flood pattern mining, and online operator support. AFSA methods have been promoted in several research activities in recent years. However, addressing an often-observed ambiguity of th...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.154965-154974
Main Authors: Manca, Gianluca, Dix, Marcel, Fay, Alexander
Format: Article
Language:English
Subjects:
Abnormal situations
alarm analysis
alarm floods
alarm management
Alarms
Ambiguity
Clustering
Control systems
Data mining
Dimensionality reduction
Floods
industrial alarm systems
Industrial electronics
industrial process diagnosis
Measurement
Outliers (statistics)
Pattern analysis
Process control
Root cause analysis
Task analysis
Tennessee-Eastman-Process
Time series analysis
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Summary:Alarm flood similarity analysis (AFSA) methods are frequently used as a primary step for root-cause analysis, alarm flood pattern mining, and online operator support. AFSA methods have been promoted in several research activities in recent years. However, addressing an often-observed ambiguity of the order of alarms and the annunciation of irrelevant alarms in otherwise similar alarm subsequences remains a challenging task. To address and solve these limitations, this paper presents a novel AFSA method that uses alarm series as input to two extended term frequency-inverse document frequency (TF-IDF)-based clustering approaches, a dimensionality reduction technique, and a novel outlier validation. The method proposed here utilizes both characteristic alarm variables and their coactivations, thus, emphasizing the dynamic properties of alarms to a greater extent. Our method is compared to three relevant methods from the literature. The effectiveness and performance of the examined methods are illustrated by means of an openly accessible dataset based on the "Tennessee-Eastman-Process". It is shown that the integration of alarm series data improves the overall performance and robustness of the AFSA. Furthermore, the clustering results are less influenced by the ambiguity of the order of alarms and irrelevant alarms, thus overcoming a persistent challenge in alarm management research.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3128695