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Entropy
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Diagnosis and Prognosis of Incipient Faults Using Information Processing or...
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Diagnosis and Prognosis of Incipient Faults Using Information Processing or Machine Learning, and Deep Learning

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 27 July 2026 | Viewed by 2740

Editors

Prof. Dr. Claude Delpha

E-Mail Website
Guest Editor
CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, Université Paris Saclay, 91192 Gif Sur Yvette, France
Interests: data and signal processing; incipient fault diagnosis; detection and estimation; data hiding; watermarking; complex systems; statistical learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Demba Diallo

E-Mail Website
Guest Editor
Group of Electrical Engineering of Paris, CNRS, CentraleSupelec, Université Paris Saclay, 91192 Gif sur Yvette, France
Interests: electrical drives; incipient fault diagnosis; fault tolerant control; renewable energy
Special Issues, Collections and Topics in MDPI journals
Dr. Khalid Dahi

E-Mail Website
Guest Editor
Ecole Centrale Casablanca, Ville Verte Côté Latéral Est, Bouskoura, Casablanca 27182, Morocco
Interests: data and signal processing; fault diagnosis; machine and deep learning; condition-based maintenance

Special Issue Information

Dear Colleagues,

Incipient faults can be defined on the basis of their effects (loss of performance), their causes (changes in material features or information properties, for example), or the properties of the signals (high signal to noise ratio (SNR) and low fault to noise ratio (FNR)). Within the health-monitoring framework, reaching good performance (low false alarm rate, low miss detection rates, high classification accuracy, etc.) becomes more challenging when dealing with slowly evolving faults, particularly in noisy environments. The accurate estimation of the remaining useful lifetime also becomes more tedious because of the uncertainties and complex non-linear phenomena, such as regeneration in electrochemical energy storage devices. 

The aim of this Special Issue is to provide a forum for academics and the industry to discuss significant recent advances in the development of tools and methods derived from information theory (distance and divergence) and systems theory and their application in accurately diagnosing incipient faults in a timely manner, thus predicting their evolution and assessing the RUL. Discussions on computational requirements (quantity of data and computational capabilities), as well as the tolerance of uncertainties, are welcome. The Special Issue is also an opportunity to discuss the standards and best practices (sensor technologies, dataset building, performance comparison, guidelines, etc.) and future trends. The Special Issue is open to all application sectors, including biomedical, transport, energy production, etc.

Prof. Dr. Claude Delpha
Prof. Dr. Demba Diallo
Dr. Khalid Dahi
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • incipient faults
  • resilience
  • statistical and information measures
  • machine and deep learning
  • predictive maintenance
  • cybersecurity

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Published Papers (3 papers)

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Research

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18 pages, 3324 KB  
Article
Entropy-Constrained M2ANet for Early Fault Prediction of Wind Turbines
by Jingchan Lv and Zhihai Yao
Entropy 2026, 28(6), 666; https://doi.org/10.3390/e28060666 - 11 Jun 2026
Viewed by 200
Abstract
Early fault prediction of wind turbines is critical for ensuring wind farm safety and reducing operation and maintenance costs. However, the latent and progressive nature of incipient faults, together with concurrent failures across multiple subsystems, makes accurate root-cause identification challenging. In addition, severe [...] Read more.
Early fault prediction of wind turbines is critical for ensuring wind farm safety and reducing operation and maintenance costs. However, the latent and progressive nature of incipient faults, together with concurrent failures across multiple subsystems, makes accurate root-cause identification challenging. In addition, severe class imbalance between normal and faulty samples further degrades prediction performance, particularly for minority fault types. To address these challenges, this paper proposes a novel fault prediction model, M2ANet, using SCADA data within a 30-min pre-fault window. The model combines a dual-memory module with progressive dilated convolutions to efficiently capture multi-scale temporal dependencies from high-dimensional operational variables. An entropy-bias penalty is further introduced into the loss function to adaptively regularize the predicted probability distribution, alleviating overconfidence under imbalanced data conditions and improving the recognition of minority faults. Experiments on a real-world wind farm dataset show that M2ANet achieves an overall accuracy of 90.73% and a weighted F1-score of 90.62% in multi-class fault prediction, outperforming 10 representative baseline models. In addition to these aggregate metrics, per-class evaluation confirms the model’s robustness under class imbalance. Notably, for yaw system faults, which account for only 1.9% of the samples, M2ANet achieves a recall of 95.92% with a 30-min-ahead warning. These results demonstrate its effectiveness and reliability for early fault prediction in practical wind turbine applications. Full article
(This article belongs to the Special Issue Diagnosis and Prognosis of Incipient Faults Using Information Processing or Machine Learning, and Deep Learning)
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15 pages, 4202 KB  
Article
State of Health Evaluation of Lithium-Ion Batteries Using the Statistical Properties of the Voltage
by Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Claude Delpha and Hamid Gualous
Entropy 2026, 28(2), 221; https://doi.org/10.3390/e28020221 - 14 Feb 2026
Viewed by 702
Abstract
Conventional indicators of battery health, such as capacity and energy, are difficult to measure directly and are therefore often estimated. This article proposes assessing lithium-ion battery health using the statistical properties of the voltage across the battery terminals, a measurement already available in [...] Read more.
Conventional indicators of battery health, such as capacity and energy, are difficult to measure directly and are therefore often estimated. This article proposes assessing lithium-ion battery health using the statistical properties of the voltage across the battery terminals, a measurement already available in battery management systems. The evolution of the voltage probability density function during the cycle is assessed using Kullback–Leibler divergence (KLD) as a health indicator. It is studied for two battery chemistries (Lithium iron Phosphate (LFP) and Nickel Manganese Cobalt (NMC)). The batteries are subjected to cycles with a dynamic current profile derived from globally harmonised test cycles for light vehicles (WLTC). Spearman’s correlation coefficients, above 86% for NMC cells and 74% for LFP cells, also indicate that this new health indicator is strongly correlated with conventional measurements of battery health (capacity or energy). The analysis also shows that the divergence not only closely follows the degradation trend even at high noise levels (SNR = 10 dB) but is also insensitive to noise levels higher than 30 dB. Full article
(This article belongs to the Special Issue Diagnosis and Prognosis of Incipient Faults Using Information Processing or Machine Learning, and Deep Learning)
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Review

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32 pages, 2313 KB  
Review
Review of Prognosis Approaches Applied to Power SiC MOSFETs for Health State and Remaining Useful Life Prediction
by Sanjiv Kumar, Bruno Allard, Malorie Hologne-Carpentier, Guy Clerc and François Auger
Entropy 2026, 28(2), 234; https://doi.org/10.3390/e28020234 - 17 Feb 2026
Viewed by 1191
Abstract
The use of Silicon Carbide (SiC) MOSFETs significantly improves converter performance by increasing efficiency and reducing costs, to the detriment of electro-magnetic emission and reliability. Implementing a predictive maintenance strategy based on a prognosis tool can mitigate this limitation. This literature review offers [...] Read more.
The use of Silicon Carbide (SiC) MOSFETs significantly improves converter performance by increasing efficiency and reducing costs, to the detriment of electro-magnetic emission and reliability. Implementing a predictive maintenance strategy based on a prognosis tool can mitigate this limitation. This literature review offers a methodological synthesis of prognosis design tools for SiC MOSFETs, while also encompassing studies on IGBTs and silicon-based power MOSFETs where these approaches are transferable. The analysis focuses on wear-out prognosis under nominal operating conditions of standard package device, excluding environmental constraints. Articles published up to 2025 were identified in the OpenAlex database using a keyword-based search and manually filtered according to the study scope. Most reviewed works rely on Data-Based prognosis methods, mostly based on neural networks, though out-of-sample validation remains uncommon. Our study also highlights the dependence of Data-Based prognosis performance on the shape of degradation indicator trends. Moreover, the estimation of prediction uncertainty is rarely addressed in the reviewed literature. Despite notable methodological advances, ensuring the reliability of prognosis tools for SiC MOSFETs remains an ongoing research challenge. Full article
(This article belongs to the Special Issue Diagnosis and Prognosis of Incipient Faults Using Information Processing or Machine Learning, and Deep Learning)
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