{"title":"Dynamic alarm monitoring with data-driven ellipsoidal threshold learning","authors":"Kaixin Cui , Wenjing Wu , Jun Shang , Dawei Shi","doi":"10.1016/j.conengprac.2025.106282","DOIUrl":null,"url":null,"abstract":"<div><div>Alarm systems are essential for the safety maintenance and health management of industrial systems. In this work, a dynamic alarm monitoring approach with data-driven ellipsoidal threshold learning is proposed, and an unknown system is directly learned using noisy data without model identification. An ellipsoid-based normal operating zone of the system variable is iteratively predicted based on system dynamics, and is updated as an external approximation of the intersection of a predicted ellipsoid and a measurement-based ellipsoid with an event-triggering condition. Then, the dynamic alarm limits are calculated for each dimension of the output by an ellipsoid-based quadratic equation, and a projection strategy from output points to the predicted ellipsoids is designed to have two different solutions to the equation. The effectiveness of the proposed dynamic alarm monitoring approach is illustrated by experimental results on the sensor fault and actuator fault detection of an ultrasonic motor with and without an event-triggering condition, respectively.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"158 ","pages":"Article 106282"},"PeriodicalIF":4.6000,"publicationDate":"2025-02-18","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/S0967066125000450","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
Alarm systems are essential for the safety maintenance and health management of industrial systems. In this work, a dynamic alarm monitoring approach with data-driven ellipsoidal threshold learning is proposed, and an unknown system is directly learned using noisy data without model identification. An ellipsoid-based normal operating zone of the system variable is iteratively predicted based on system dynamics, and is updated as an external approximation of the intersection of a predicted ellipsoid and a measurement-based ellipsoid with an event-triggering condition. Then, the dynamic alarm limits are calculated for each dimension of the output by an ellipsoid-based quadratic equation, and a projection strategy from output points to the predicted ellipsoids is designed to have two different solutions to the equation. The effectiveness of the proposed dynamic alarm monitoring approach is illustrated by experimental results on the sensor fault and actuator fault detection of an ultrasonic motor with and without an event-triggering condition, respectively.
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.
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