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Fault Diagnosis and Health Management Based on Artificial Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 1018

Special Issue Editors

College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: PHM; CBM; fault diagnosis based on AI
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Interests: fault diagnosis and health management
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
Interests: predictive maintenance; virtual maintenance

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Guest Editor
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: intelligent diagnostics; prognostics and health management (PHM) for electromechanical and hydraulic systems; artificial intelligence and signal processing; digital twins and intelligent robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurately assessing health status and identifying failure modes as early as possible are key to the continuous and reliable operation of products, as well as representing the key technology of prognosis and health management (PHM). With the continuous development of big data technology, product fault diagnosis and health management based on artificial intelligence have become current research hotspots and frontiers. However, the complex and harsh operating conditions result in such advances facing the model simplicity problem of difficult hyperparameter setting and tuning, as well as the data complexity problem of insufficient labeled data, imbalanced data distribution and low signal quality, which restrict the effective application of AI-based fault diagnosis and health management. Therefore, this Special Issue focuses on these challenges and welcomes research from global scholars proposing effective solutions aimed at revealing the nonlinear mapping laws between health characterization data and fault space in multiple scenarios to provide key technological support for product condition-based maintenance (CBM). Topics of interest include, but are not limited to, the following:

  • Industrial operation data analysis and processing methods;
  • Convenient fault diagnosis models with simpler hyperparameter settings;
  • Intelligent fault diagnosis methods with small training samples;
  • Transfer fault diagnosis methods with imbalanced data distribution;
  • Enhanced fault diagnosis methods with low signal quality;
  • Early fault and remaining useful life prediction methods;
  • Health status evaluation methods based on multi-source data;
  • Maintainability design and analysis based on artificial intelligence.

Dr. Jiayu Chen
Prof. Dr. Jian Ma
Prof. Dr. Dong Zhou
Dr. Xiaoli Zhao
Guest Editors

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Keywords

  • intelligent diagnosis
  • health evaluation
  • PHM
  • CBM
  • transfer diagnosis
  • enhanced diagnosis
  • multi-source data fusion
  • small training sample

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Published Papers (1 paper)

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Research

21 pages, 455 KiB  
Article
Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
by Cem Haydaroğlu, Heybet Kılıç, Bilal Gümüş and Mahmut Temel Özdemir
Appl. Sci. 2025, 15(4), 1908; https://doi.org/10.3390/app15041908 - 12 Feb 2025
Viewed by 719
Abstract
In this paper, the fault type and location of high-impedance short-circuit faults, which are difficult to detect in distribution networks, are determined in real time using the Real-Time Digital Simulator (RTDS). In this study, an IEEE 39-bar system model is created using the [...] Read more.
In this paper, the fault type and location of high-impedance short-circuit faults, which are difficult to detect in distribution networks, are determined in real time using the Real-Time Digital Simulator (RTDS). In this study, an IEEE 39-bar system model is created using the Real-Time Simulation Software Package (RSCAD). In this model, a short-circuit fault is generated at different fault impedance values. For high-impedance short-circuit fault detection, 14 feature vectors are created. Six of these feature vectors are newly developed, and it is found that these six new feature vectors contribute 10% to the detection of hard-to-detect high-impedance short-circuit faults. We propose a data-driven online algorithm for fault type and location detection based on robust regularized random vector function networks (ORR-RVFLNs). Moreover, the robustness of the model is improved by adding a certain amount of noise to the detected short-circuit fault data. In this study, the method ORR-RVFLN for the 39-bus system IEEE detects the average error type for all error impedances, with 92.2% success for the data with noise added. In this study, the fault location is shown to be more than 90% accurate for distances greater than 400 m. Full article
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