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Algorithms
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AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis...
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AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1517

Special Issue Editors

Dr. Sumika Chauhan

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Guest Editor
Faculty of Geoengineering, Mining and Geology, Wroclaw University of Sciecne and Technology, Na Grobli 15, 50-421 Wroclaw, Poland
Interests: optimization techniques; signal processing; machine learning; wireless sensor networks; filter design; classification; regression; vibration; diagnostic; rotating systems and interdisciplinary
Special Issues, Collections and Topics in MDPI journals
Dr. Govind Vashishtha

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Guest Editor
Faculty of Geoengineering, Mining and Geology, Wroclaw University of Sciecne and Technology, Na Grobli 15, 50-421 Wroclaw, Poland
Interests: mechanical components; vibration and acoustic signal processing; identification; measurement; defect prognosis; classification; optimization; machine learning; artificial intelligence and interdisciplinary
Special Issues, Collections and Topics in MDPI journals
Dr. Rajesh Kumar

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Guest Editor
Precision Metrology Laboratory, Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal 148 106, India
Interests: mechanical components; vibration and acoustic signal processing; identification; measurement; defect prognosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the revolutionary impact of AI on industrial predictive maintenance, focusing on the intelligent diagnosis of machine faults and its transformative effects on industrial operations. We invite submissions that showcase cutting-edge research, innovative applications, and future directions in this burgeoning field. Contributions may delve into the development and application of AI algorithms for fault detection and diagnosis, including deep learning, machine learning, ensemble methods, and hybrid approaches. We also welcome research examining data-driven condition monitoring techniques, such as sensor fusion, data analytics, and IoT integration. Additionally, we seek investigations on the realm of prognostic health management, including predicting remaining useful life, optimizing maintenance schedules, and minimizing downtime. Real-world applications and case studies highlighting the successful implementation of AI-based predictive maintenance in various industrial sectors are highly encouraged. Finally, we welcome discussions on the challenges and opportunities presented by AI-powered predictive maintenance, such as data quality, model interpretability, ethical considerations, and future research directions. This Special Issue aims to provide a comprehensive overview of the latest advancements in this domain and inspire further innovation in AI-driven industrial transformation.

Dr. Sumika Chauhan
Dr. Govind Vashishtha
Dr. Rajesh Kumar
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms 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 1800 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

  • AI-powered predictive maintenance
  • intelligent fault diagnosis
  • industrial operations
  • data-driven condition monitoring
  • prognostic health management

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Related Special Issue

Published Papers (3 papers)

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Research

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21 pages, 8142 KB  
Article
Robust Deep Learning for Multiclass Power System Fault Diagnosis Using Edge Deployment
by Rakesh Sahu, Pratap Kumar Panigrahi, Deepak Kumar Lal, Rudranarayan Pradhan and Chandrakanta Mahanty
Algorithms 2026, 19(4), 299; https://doi.org/10.3390/a19040299 - 11 Apr 2026
Viewed by 591
Abstract
This article introduces an intelligent framework using deep learning to recognize and classify different faults through the real-time detection of multiple faults in power distribution systems. A collection of data representing normal operating conditions, alongside various fault scenarios including line-to-ground (LG), line-to-line (LL), [...] Read more.
This article introduces an intelligent framework using deep learning to recognize and classify different faults through the real-time detection of multiple faults in power distribution systems. A collection of data representing normal operating conditions, alongside various fault scenarios including line-to-ground (LG), line-to-line (LL), double line-to-ground (LLG), and three-phase line (LLL) faults, was created using three phase current signals obtained from the Real-Time Digital Simulator (RTDS) microgrid test system. To properly model the system dynamics, a feature extraction method that integrates phase currents, differential currents, summation currents and magnitude results was developed. The temporal features of the fault signals were identified by using a sliding window approach to fit the data. A one-dimensional convolutional neural network (CNN) was developed to identify different types of faults. This model performed well, obtaining nearly 96.15% accuracy while testing. In order to evaluate the feasibility of the approach, the trained model was loaded on Raspberry Pi 5, NodeMCU, ESP32 and existing sensing devices. The fault classification performed in real-time was time-sensitive. The proposed intelligent framework is applicable to low-scale operation for smart grid fault monitoring and protection and it is an economically viable solution. Full article
(This article belongs to the Special Issue AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis (2nd Edition))
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25 pages, 4742 KB  
Article
An Edge-Enabled Predictive Maintenance Approach Based on Anomaly-Driven Health Indicators for Industrial Production Systems
by Bouzidi Lamdjad and Adem Chaiter
Algorithms 2026, 19(4), 286; https://doi.org/10.3390/a19040286 - 8 Apr 2026
Viewed by 536
Abstract
This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach [...] Read more.
This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach combines edge-level monitoring, anomaly detection, and predictive modeling to analyze operational signals and estimate system health conditions from high-frequency industrial data. Empirical validation was conducted using operational datasets collected from two industrial production facilities between 2024 and 2025. The model evaluates patterns associated with operational instability and degradation-related anomalies and translates them into interpretable health indicators that can support proactive intervention. The empirical results show strong predictive performance, with R2 reaching 0.989, a mean absolute percentage error of 3.67%, and a root mean square error of 0.79. In addition, the mitigation of early anomaly signals was associated with an observed improvement of approximately 3.99% in system stability. Unlike many existing studies that treat anomaly detection, predictive modeling, and prognostic analysis as separate tasks, the proposed framework connects these stages within a unified analytical structure designed for deployment in industrial environments. The findings indicate that edge-generated anomaly signals can provide meaningful early information about potential system deterioration and can assist in planning timely maintenance actions even when explicit failure labels are limited. The study contributes to the development of scalable predictive maintenance solutions that integrate artificial intelligence with edge-based industrial monitoring systems. Full article
(This article belongs to the Special Issue AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis (2nd Edition))
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Review

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39 pages, 5969 KB  
Review
Intelligent Identification, Classification, and Localization of Submarine Cable Faults for Offshore Wind Farms Using Time-Domain Reflectometric and Neural Network-Based Techniques
by Garrett Rose and Senthil Krishnamurthy
Algorithms 2026, 19(5), 388; https://doi.org/10.3390/a19050388 - 13 May 2026
Viewed by 4
Abstract
The development of offshore wind energy has increased the demand for reliable submarine transmission systems. In South Africa, research remains constrained due to the lack of operational offshore wind farms, despite favorable geographical conditions and persistent energy challenges such as load-shedding. Submarine cable [...] Read more.
The development of offshore wind energy has increased the demand for reliable submarine transmission systems. In South Africa, research remains constrained due to the lack of operational offshore wind farms, despite favorable geographical conditions and persistent energy challenges such as load-shedding. Submarine cable faults, primarily caused by manufacturing deficiencies, environmental factors, and human activities, contribute significantly to system downtime while accounting for only a small portion of overall installation costs. This study reviews submarine cable fault identification, classification, pre-determination, and localization techniques. Conventional methods, including time-domain reflectometry, the Murray loop, the Varley loop, and impulse-based techniques, are reviewed alongside artificial neural network models, such as convolutional and deep learning architectures. Findings imply that traditional techniques offer low error margins but lack the accuracy needed for pinpointing exact faults, as faults may extend over several kilometers. In contrast, neural network-based methods, particularly when integrated with signal processing methods, significantly improve fault classification and localization accuracy. The study concludes that hybrid approaches combining conventional diagnostic techniques with neural networks offer a robust framework for submarine cable fault analysis, providing real-world solutions to enhance reliability and efficiency in future offshore wind transmission systems. Full article
(This article belongs to the Special Issue AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis (2nd Edition))
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