{"title":"Hierarchical grouping and visualization of correlated alarms using time-augmented word embedding","authors":"Aliakbar Davoodi, Ahmad W. Al-Dabbagh","doi":"10.1016/j.conengprac.2024.106130","DOIUrl":null,"url":null,"abstract":"<div><div>In industrial processes, a large number of alarms displayed on human–machine interface screens may overwhelm human operators. This prevents them from taking appropriate corrective actions in a timely manner. Therefore, this paper proposes a three-stage computational procedure for grouping and visualizing correlated alarms, such that root-cause abnormalities can be more easily identified by the human operators. In the first stage, using a word embedding-based approach, alarm tags are transformed into real-valued vectors, where time stamps of the alarms are used rather than their order of occurrence. In the second stage, a multi-level density-based clustering approach is utilized to group correlated alarms hierarchically. In the third stage, a hierarchical visualization approach is developed to display alarm groups to human operators, which depicts hierarchical and statistical information. The implementation and effectiveness of the three-stage computational procedure are demonstrated using an alarm dataset generated for the benchmark Tennessee Eastman process system.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066124002892","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In industrial processes, a large number of alarms displayed on human–machine interface screens may overwhelm human operators. This prevents them from taking appropriate corrective actions in a timely manner. Therefore, this paper proposes a three-stage computational procedure for grouping and visualizing correlated alarms, such that root-cause abnormalities can be more easily identified by the human operators. In the first stage, using a word embedding-based approach, alarm tags are transformed into real-valued vectors, where time stamps of the alarms are used rather than their order of occurrence. In the second stage, a multi-level density-based clustering approach is utilized to group correlated alarms hierarchically. In the third stage, a hierarchical visualization approach is developed to display alarm groups to human operators, which depicts hierarchical and statistical information. The implementation and effectiveness of the three-stage computational procedure are demonstrated using an alarm dataset generated for the benchmark Tennessee Eastman process system.
期刊介绍:
Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper.
The scope of Control Engineering Practice matches the activities of IFAC.
Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.