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Entropy
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Entropy-Based Fault Diagnosis: From Theory to Applications
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Entropy-Based Fault Diagnosis: From Theory to Applications

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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 1140

Special Issue Editors

Dr. Xiaoan Yan

E-Mail Website
Guest Editor
School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Interests: condition monitoring and faults diagnosis; mechanical system dynamics modeling; signal processing and machine learning; artificial intelligence and pattern recognition; prognostics and health management; structural damage identification and health monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ling Xiang

E-Mail Website
Guest Editor
School of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Interests: vibration analysis and fault diagnosis; artificial Intelligence for IT operations; rotor system dynamics analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinde Zheng

E-Mail Website
Guest Editor
School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032, China
Interests: vibration signal testing and analysis; non-stationary signal processing; time-frequency analysis; mechanical equipment condition monitoring and fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Zhi-Xin Yang

E-Mail Website
Guest Editor
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao SAR 999078, China
Interests: prognostic health monitoring of engineering system; computer vision; robotics and intelligent safety monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

When a fault occurs in large-scale machinery (e.g., wind turbines, gas turbines, aero-engines, compressors, railway vehicles, and industrial robots), it will result in economic losses for the enterprise, and even cause serious accidents and endanger the safety of technicians. Therefore, it is of great research value to explore promising machinery condition monitoring and fault diagnosis techniques. As a tool for quantifying uncertainty and complexity, signal entropy can be applied to detect changes in system behavior and thus be used for equipment fault diagnosis and prediction. This also makes entropy an important theory for improving the efficiency of system monitoring and maintenance decision. Due to its prominent role in measuring the uncertainty and complexity of time series, entropy theory has been shown to be an effective and state-of-the-art technique in machinery condition monitoring and fault diagnosis. Research into advanced entropy-based methods for the real-time monitoring and diagnosis of machinery equipment conditions is an important trend in line with the current development of large-scale intelligent machinery equipment, as such methods comprehensively guarantee the operational safety and stability of machinery equipment, improve production efficiency, and reduce maintenance costs.

The aim of this Special Issue is to collect recent results on entropy theory-related condition monitoring and fault diagnosis methods in machinery equipment. We also accept contributions on novel perspectives, ongoing research, and discussions regarding existing methods. Thus, recent developments, ideas, and applications of entropy theory in the field of machinery condition monitoring and fault diagnosis all fall under the requirements of our Special Issue.

Dr. Xiaoan Yan
Prof. Dr. Ling Xiang
Prof. Dr. Jinde Zheng
Dr. Zhixin Yang
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

  • information entropy
  • sample entropy
  • permutation entropy
  • fuzzy entropy
  • dispersion entropy
  • hierarchical entropy
  • multiscale entropy
  • condition monitoring
  • fault diagnosis
  • fault prognostics
  • anomaly detection
  • feature extraction
  • machinery equipment

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 5127 KiB  
Article
Fault Root Cause Analysis Based on Liang–Kleeman Information Flow and Graphical Lasso
by Xiangdong Liu, Jie Liu, Xiaohua Yang, Zhiqiang Wu, Ying Wei, Zhuoran Xu and Juan Wen
Entropy 2025, 27(2), 213; https://doi.org/10.3390/e27020213 - 19 Feb 2025
Viewed by 274
Abstract
Root cause analysis is used to find the specific fault location and cause of a fault during system fault diagnosis. It is an important step in fault diagnosis. The root cause analysis method based on causality starts from the origin of the causal [...] Read more.
Root cause analysis is used to find the specific fault location and cause of a fault during system fault diagnosis. It is an important step in fault diagnosis. The root cause analysis method based on causality starts from the origin of the causal connection between transactions and infers the location and cause of the mechanism failure by analyzing the causal impact of variables between systems, which has methodological advantages. Causal analysis methods based on transfer entropy are proven to have biases in calculation results, so there is a phenomenon of calculating false causal relationships, which leads to the problem of insufficient accuracy in root cause analysis. Liang–Kleeman information flow (LKIF) is a kind of information entropy that can effectively carry out causal inference, which can avoid obtaining wrong causal relationships. We propose a root cause analysis method that combines graphical lasso and information flow. In view of the large amount of redundant information in industrial data due to the coupling effect of industrial systems, graphical lasso (Glasso) is a high-precision dimensionality reduction method suitable for large-scale and high-dimensional datasets. To ensure the timeliness of root cause analysis, graphical lasso uses dimensionality reduction of the data. Then, LKIF is used to calculate the information flow intensity of each relevant variable, infer the causal relationship between the variable pairs, and trace the root cause of the fault. On the Tennessee Eastman simulation platform, root cause analysis was performed on all faults, and two root cause analysis solutions, transfer entropy and information flow, were compared. Experimental results show that the LKIF–Glasso method can effectively detect the root cause of faults and display the propagation of faults throughout the process. It further shows that information flow has a better effect in root cause analysis than transfer entropy. And through the root cause analysis of the step failure of the stripper, the reason why information flow is superior to transfer entropy is explained in detail. Full article
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis: From Theory to Applications)
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15 pages, 5019 KiB  
Article
Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic Denoising
by Yu Wan, Shaochen Lin, Chuanling Jin, Yan Gao and Yang Yang
Entropy 2025, 27(1), 10; https://doi.org/10.3390/e27010010 - 27 Dec 2024
Viewed by 511
Abstract
During long-term operation in complex environments, the pressure pipeline systems are prone to damage and faults, and serious safety accidents may occur without real-time condition monitoring. Moreover, in traditional non-contact monitoring approaches, acoustic signals are widely employed for condition monitoring for pressure pipelines, [...] Read more.
During long-term operation in complex environments, the pressure pipeline systems are prone to damage and faults, and serious safety accidents may occur without real-time condition monitoring. Moreover, in traditional non-contact monitoring approaches, acoustic signals are widely employed for condition monitoring for pressure pipelines, which are easily contaminated by background noise and provide unsatisfactory accuracy. As a tool for quantifying uncertainty and complexity, signal entropy is applied to detect abnormal conditions. Based on the characteristics of entropy and acoustic signals, an improved entropy-based condition monitoring method is proposed for pressure pipelines through acoustic denoising. Specifically, this improved entropy-based noise reduction model is proposed to reduce the noise of monitoring acoustic signals through adversarial training. Based on the denoising of acoustic signals, an abnormal sound detection method is proposed to realize condition monitoring for pressure pipelines. In addition, the experimental platform is built to test the effectiveness and reliability of the proposed method. The results indicate that the quality of signal denoising can reach over 3 dB, while the accuracy of condition monitoring is about 92% for different conditions. Finally, the superiority of the proposed method is verified by comparing it with other methods. Full article
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis: From Theory to Applications)
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