Open AccessClustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations
Author(s) -
Gianluca Manca,
Marcel Dix,
Alexander Fay
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3128695
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.