Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.7
Journals
Entropy
Special Issues
Fault Diagnosis and Fault-Tolerant Control for Complex Systems
entropy-logo
Submit to Entropy Review for Entropy Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Fault Diagnosis and Fault-Tolerant Control for 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: closed (30 April 2023) | Viewed by 8089

Special Issue Editor

Dr. Tao Wang

E-Mail Website
Guest Editor
School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China
Interests: fault diagnosis of power systems and electrical equipment; safe and stable operation of smart grids and energy Internets; application of artificial intelligence technology in power systems and integrated energy systems

Special Issue Information

Dear Colleagues,

A complex system refers to one with a moderate number of intelligent and adaptive agents that act based on local information. With the development of new information technology and intelligent systems, complex systems have been widely used in the fields of electric power, integrated energy, electronics, machinery, aerospace, intelligent transportation, petrochemical industry and so on. These modern systems have complex structures and high manufacturing costs. System failures can cause great economic losses; therefore, fault prediction and diagnosis of complex systems and their related equipment, as well as system fault tolerance control, are of increasing interest.

This Special Issue will focus on the application of different techniques for fault diagnosis and fault-tolerant control of complex systems. Particular attention will be paid to statistical/entropy-based detection/diagnosis/control techniques. Approaches of interest include quantitative approaches with wide and efficient physical modeling, qualitative approaches and data-driven ones. Both theoretical and applicative works will be considered. Applications in tune with time, such as smart grids, integrated energy systems, renewable-energy-based systems, intelligent transportation, etc., are welcome.

Dr. Tao Wang
Guest Editor

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

  • fault detection, diagnosis and recovery
  • fault and system modeling
  • intelligent fault-tolerant control strategy
  • diagnostic decision making
  • information fusion in fault diagnosis
  • uncertainty handling in fault diagnosis and fault-tolerant control
  • detection methodologies for diagnosis
  • fault prediction and health management
  • fault classification and machine learning
  • fault prediction, diagnosis, recovery and power big data
  • application of artificial intelligence in fault diagnosis

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 3148 KiB  
Article
Active Fault Isolation for Multimode Fault Systems Based on a Set Separation Indicator
by Kezhen Han, Shaohang Lu, Zhengce Liu and Zipeng Wang
Entropy 2023, 25(6), 876; https://doi.org/10.3390/e25060876 - 30 May 2023
Viewed by 1007
Abstract
This paper considers the active fault isolation problem for a class of uncertain multimode fault systems with a high-dimensional state-space model. It has been observed that the existing approaches in the literature based on a steady-state active fault isolation method are often accompanied [...] Read more.
This paper considers the active fault isolation problem for a class of uncertain multimode fault systems with a high-dimensional state-space model. It has been observed that the existing approaches in the literature based on a steady-state active fault isolation method are often accompanied by a large delay in making the correct isolation decision. To reduce such fault isolation latency significantly, this paper proposes a fast online active fault isolation method based on the construction of residual transient-state reachable set and transient-state separating hyperplane. The novelty and benefit of this strategy lies in the embedding of a new component called the set separation indicator, which is designed offline to distinguish the residual transient-state reachable sets of different system configurations at any given moment. Based on the results delivered by the set separation indicator, one can determine the specific moments at which the deterministic isolation is to be implemented during online diagnostics. Meanwhile, some alternative constant inputs can also be evaluated for isolation effects to determine better auxiliary excitation signals with smaller amplitudes and more differentiated separating hyperplanes. The validity of these results is verified by both a numerical comparison and an FPGA-in-loop experiment. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control for Complex Systems)
►▼ Show Figures

Figure 1

26 pages, 1072 KiB  
Article
Fault Isolation and Estimation in Networks of Linear Process Systems
by Wijaya Kurniawan, Katalin M. Hangos and Lőrinc Márton
Entropy 2023, 25(6), 862; https://doi.org/10.3390/e25060862 - 28 May 2023
Viewed by 874
Abstract
Fault detection and isolation is a ubiquitous task in current complex systems even in the linear networked case when the complexity is mainly caused by the complex network structure. A simple yet practically important special case of networked linear process systems is considered [...] Read more.
Fault detection and isolation is a ubiquitous task in current complex systems even in the linear networked case when the complexity is mainly caused by the complex network structure. A simple yet practically important special case of networked linear process systems is considered in this paper with only a single conserved extensive quantity but with a network structure containing loops. These loops make fault detection and isolation challenging to perform because the effect of fault is propagated back to where it first occurred. As a dynamic model of network elements, a two input single output (2ISO) LTI state-space model is proposed for fault detection and isolation where the fault enters as an additive linear term into the equations. No simultaneously occurring faults are considered. A steady state analysis and superposition principle are used to analyse the effect of faults in a subsystem that propagates to the sensors’ measurements at different positions. This analysis is the basis of our fault detection and isolation procedure that provides the position of the faulty element in a given loop of the network. A disturbance observer is also proposed to estimate the magnitude of the fault inspired by a proportional-integral (PI) observer. The proposed fault isolation and fault estimation methods have been verified and validated by using two simulation case studies in the MATLAB/Simulink environment. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control for Complex Systems)
►▼ Show Figures

Figure 1

13 pages, 382 KiB  
Article
Uncertainty Management in Assessment of FMEA Expert Based on Negation Information and Belief Entropy
by Lei Wu, Yongchuan Tang, Liuyuan Zhang and Yubo Huang
Entropy 2023, 25(5), 800; https://doi.org/10.3390/e25050800 - 15 May 2023
Cited by 3 | Viewed by 1085
Abstract
The failure mode and effects analysis (FMEA) is a commonly adopted approach in engineering failure analysis, wherein the risk priority number (RPN) is utilized to rank failure modes. However, assessments made by FMEA experts are full of uncertainty. To deal with this issue, [...] Read more.
The failure mode and effects analysis (FMEA) is a commonly adopted approach in engineering failure analysis, wherein the risk priority number (RPN) is utilized to rank failure modes. However, assessments made by FMEA experts are full of uncertainty. To deal with this issue, we propose a new uncertainty management approach for the assessments given by experts based on negation information and belief entropy in the Dempster–Shafer evidence theory framework. First, the assessments of FMEA experts are modeled as basic probability assignments (BPA) in evidence theory. Next, the negation of BPA is calculated to extract more valuable information from a new perspective of uncertain information. Then, by utilizing the belief entropy, the degree of uncertainty of the negation information is measured to represent the uncertainty of different risk factors in the RPN. Finally, the new RPN value of each failure mode is calculated for the ranking of each FMEA item in risk analysis. The rationality and effectiveness of the proposed method is verified through its application in a risk analysis conducted for an aircraft turbine rotor blade. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control for Complex Systems)
►▼ Show Figures

Figure 1

19 pages, 6627 KiB  
Article
Research on Performance Degradation Estimation of Key Components of High-Speed Train Bogie Based on Multi-Task Learning
by Junxiao Ren, Weidong Jin, Yunpu Wu, Zhang Sun and Liang Li
Entropy 2023, 25(4), 696; https://doi.org/10.3390/e25040696 - 20 Apr 2023
Cited by 2 | Viewed by 1198
Abstract
The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation [...] Read more.
The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control for Complex Systems)
►▼ Show Figures

Figure 1

16 pages, 6101 KiB  
Article
Multi-Source Partial Discharge Fault Location with Comprehensive Arrival Time Difference Extraction Method and Multi-Data Dynamic Weighting Algorithm
by Disheng Wang, Lin Du, Tao Wang and Xiuna Zhao
Entropy 2023, 25(4), 572; https://doi.org/10.3390/e25040572 - 27 Mar 2023
Viewed by 828
Abstract
The location of the partial discharge source is an important part of fault diagnosis inside power equipment. As a key step of the ultra-high frequency location method, the extraction of the time difference of arrival can generate large errors due to interference. To [...] Read more.
The location of the partial discharge source is an important part of fault diagnosis inside power equipment. As a key step of the ultra-high frequency location method, the extraction of the time difference of arrival can generate large errors due to interference. To achieve accurate time difference extraction and further multi-source partial discharge location, a location method with comprehensive time difference extraction and a multi-data dynamic weighting algorithm is proposed. For time difference extraction, the optimized energy accumulation curve method applies wavelet transform and mode maximization calculations such that it overcomes the effect of interference signals before the wave peak. The secondary correlation method improves the interference capability by performing two rounds of correlation calculations. Both extraction methods are combined to reduce the error in time difference extraction. Then, the dynamic weighting algorithm effectively utilizes multiple data and improves the location accuracy. Experimental results on multi-source partial discharge locations performed in a transformer tank validate the accuracy of the proposed method. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control for Complex Systems)
►▼ Show Figures

Figure 1

24 pages, 5527 KiB  
Article
Security Risk Assessment Approach for Distribution Network Cyber Physical Systems Considering Cyber Attack Vulnerabilities
by Buxiang Zhou, Binjie Sun, Tianlei Zang, Yating Cai, Jiale Wu and Huan Luo
Entropy 2023, 25(1), 47; https://doi.org/10.3390/e25010047 - 27 Dec 2022
Cited by 7 | Viewed by 2085
Abstract
With the increasing digitalization and informatization of distribution network systems, distribution networks have gradually developed into distribution network cyber physical systems (CPS) which are deeply integrated with traditional power systems and cyber systems. However, at the same time, the network risk problems that [...] Read more.
With the increasing digitalization and informatization of distribution network systems, distribution networks have gradually developed into distribution network cyber physical systems (CPS) which are deeply integrated with traditional power systems and cyber systems. However, at the same time, the network risk problems that the cyber systems face have also increased. Considering the possible cyber attack vulnerabilities in the distribution network CPS, a dynamic Bayesian network approach is proposed in this paper to quantitatively assess the security risk of the distribution network CPS. First, the Bayesian network model is constructed based on the structure of the distribution network and common vulnerability scoring system (CVSS). Second, a combination of the fuzzy analytic hierarchy process (FAHP) and entropy weight method is used to correct the selectivity of the attacker to strike the target when cyber attack vulnerabilities occur, and then after considering the defense resources of the system, the risk probability of the target nodes is obtained. Finally, the node loads and node risk rates are used to quantitatively assess the risk values that are applied to determine the risk level of the distribution network CPS, so that defense strategies can be given in advance to counter the adverse effects of cyber attack vulnerabilities. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault-Tolerant Control for Complex Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop