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Entropy
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Topology Modeling and Fault Analysis of Complex Systems
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Topology Modeling and Fault Analysis of Complex Systems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 5602

Special Issue Editors

Dr. Fan Yang

E-Mail Website
Guest Editor
Department of Automation, Tsinghua University, Beijing 100084, China
Interests: prognosis and health management; smart alarm monitoring; intelligent fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Wenkai Hu

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: advanced alarm monitoring; process data analytics; data mining for complex industrial processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern engineering facilities, such as manufacturing factories, power plants, oil and gas pipelines, and communication networks, are built at a large scale and deployed with massive sensors, controllers, actuators, and communication devices. The development and complex interconnections of these components often lead to abnormalities or faults propagating along the streams, with widespread effects, such as plantwide oscillations or system shutdown. Therefore, it is of great importance to promptly track the propagation of abnormalities and detect the root causes, so as to shift the system back to its normal operating state and prevent serious consequences. Techniques such as topology modeling, fault diagnosis, and alarm monitoring are effective tools in this process. They leverage the prior process knowledge, system connectivity information, and massive operation data to discover cause–effect relations, diagnose fault types, and monitor the health status of complex engineering systems. However, the presences of complex features, such as nonlinearities, multi-rate data, time-varying characteristics, and multiple operating conditions, pose great challenges to the achievement of accurate and reliable analysis results. New tools, such as information theory, causality inference, and deep neural networks, exhibit high efficiency in handling such complex issues.

This Special Issue, entitled “Topology Modeling and Fault Analysis of Complex Systems”, aims at discussing recent advances, collecting new ideas, and presenting excellent research outcomes related to topology modeling, fault diagnosis, alarm monitoring, heath management, and root cause analysis in complex systems. Specifically, this Special Issue will accept unpublished research papers focusing on (but not restricted to) the following topics:

  • Knowledge-based or information-theory-based topology modeling;
  • Entropy-based techniques for causality analysis;
  • Data-driven fault detection, diagnosis, and isolation;
  • Deep learning for system monitoring, soft sensing, and fault diagnosis;
  • Causality inference for root cause analysis;
  • Prognostics and health management of complex equipment;
  • Advanced alarm monitoring and alarm system design;
  • Applications of topology modeling and fault analysis in complex systems.

Dr. Fan Yang
Dr. Wenkai Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • topology modeling
  • information theory
  • fault detection and diagnosis
  • prognostics and health management
  • root cause analysis
  • alarm monitoring
  • transfer entropy
  • machine learning
  • causality inference

Published Papers (3 papers)

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Research

19 pages, 3929 KiB  
Article
A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis
by Ding Li and Xin Cheng
Entropy 2024, 26(1), 30; https://0-doi-org.brum.beds.ac.uk/10.3390/e26010030 - 27 Dec 2023
Viewed by 965
Abstract
Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated [...] Read more.
Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform. Full article
(This article belongs to the Special Issue Topology Modeling and Fault Analysis of Complex Systems)
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30 pages, 10936 KiB  
Article
Topological Data Analysis for Multivariate Time Series Data
by Anass B. El-Yaagoubi, Moo K. Chung and Hernando Ombao
Entropy 2023, 25(11), 1509; https://0-doi-org.brum.beds.ac.uk/10.3390/e25111509 - 1 Nov 2023
Cited by 2 | Viewed by 2591
Abstract
Over the last two decades, topological data analysis (TDA) has emerged as a very powerful data analytic approach that can deal with various data modalities of varying complexities. One of the most commonly used tools in TDA is persistent homology (PH), which can [...] Read more.
Over the last two decades, topological data analysis (TDA) has emerged as a very powerful data analytic approach that can deal with various data modalities of varying complexities. One of the most commonly used tools in TDA is persistent homology (PH), which can extract topological properties from data at various scales. The aim of this article is to introduce TDA concepts to a statistical audience and provide an approach to analyzing multivariate time series data. The application’s focus will be on multivariate brain signals and brain connectivity networks. Finally, this paper concludes with an overview of some open problems and potential application of TDA to modeling directionality in a brain network, as well as the casting of TDA in the context of mixed effect models to capture variations in the topological properties of data collected from multiple subjects. Full article
(This article belongs to the Special Issue Topology Modeling and Fault Analysis of Complex Systems)
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26 pages, 2417 KiB  
Article
Analysis on Cascading Failures of Directed–Undirected Interdependent Networks with Different Coupling Patterns
by Xiaojie Xu and Xiuwen Fu
Entropy 2023, 25(3), 471; https://0-doi-org.brum.beds.ac.uk/10.3390/e25030471 - 8 Mar 2023
Cited by 3 | Viewed by 1258
Abstract
Most existing studies model interdependent networks as simple network systems consisting of two or more undirected subnets, and the interdependent edges between the networks are undirected. However, many real-world interdependent networks are coupled by a directed subnet and an undirected subnet, such as [...] Read more.
Most existing studies model interdependent networks as simple network systems consisting of two or more undirected subnets, and the interdependent edges between the networks are undirected. However, many real-world interdependent networks are coupled by a directed subnet and an undirected subnet, such as supply chain networks coupled with cyber networks, and cyber manufacturing networks coupled with service networks. Therefore, in this work, we focus on a ubiquitous type of interdependent network—the directed–undirected interdependent network—and research the cascading failures of directed–undirected interdependent networks with different coupling patterns. Owing to the diversity of coupling patterns to realistic interdependent network systems, we introduce two types of interdependent edges (i.e., directed-to-undirected and undirected-to-directed interdependent edges). On this basis, we generated different types of directed–undirected interdependent networks with varying coupling patterns (i.e., one-to-one, one-to-many, and many-to-one) and investigated the cascading failure robustness of these types of networks. Finally, we explored the cascading robustness of directed–undirected interdependent networks under two different attack strategies (single-node attack and multi-node attack). Through extensive experiments, we have obtained some meaningful findings: (1) the cascading robustness of directed–undirected interdependent networks is positively related to the overload tolerance coefficient and load exponential coefficient; (2) high-degree nodes and high-in-degree nodes should be protected to improve the cascading robustness of directed–undirected interdependent networks; (3) the cascading robustness of one-to-many interdependent networks can be improved by adding directed-to-undirected interdependent edges; and the cascading robustness of many-to-one interdependent networks can be improved by adding undirected-to-directed interdependent edges. Full article
(This article belongs to the Special Issue Topology Modeling and Fault Analysis of Complex Systems)
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