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Processes
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Control Process, Fault Detection and Classification in Power Systems and...
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Control Process, Fault Detection and Classification in Power Systems and Industrial Machinery

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3090

Special Issue Editors

Dr. José de Jesús Rangel-Magdaleno

E-Mail Website
Guest Editor
Digital Systems Group, National Institute for Astrophysics, Optics and Electronics, Puebla 72840, Mexico
Interests: FPGA; instrumentation; mechatronics; digital systems; fault detection
Special Issues, Collections and Topics in MDPI journals
Dr. Jose Hugo Barron-Zambrano

E-Mail Website
Guest Editor
Digital Systems Group, National Institute for Astrophysics, Optics and Electronics, Puebla 72840, Mexico
Interests: FPGA; neural networks; computer vision; instrumentation

Special Issue Information

Dear Colleagues,

Thanks to the development of technology and Industry 4.0, control, fault detection, and classification processes in power systems and industrial machinery have become highly relevant.

The application of neural networks, artificial intelligence, and advanced signal processing has made it possible to solve classification and fault detection problems in incipient stages or complex control processes.

With technological advances, high computing power, and the use of embedded systems and FPGAs, it has been possible to implement complex mathematical algorithms that help signal processing. In addition, fractional calculus has increased the options and improved the control, detection, and classification stages by allowing better resolution in the systems and the space transforms.

This Special Issue provides a forum for presenting new and improved techniques for control, fault detection, and classification in power systems and industrial machinery.

Dr. José de Jesús Rangel-Magdaleno
Dr. Jose Hugo Barron-Zambrano
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. Processes 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 2400 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

  • control
  • embedded systems
  • power systems
  • monitoring
  • signal processing
  • intelligent systems
  • image processing
  • fractional calculus
  • industrial applications
  • deep neural networks

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Published Papers (2 papers)

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Research

22 pages, 4013 KiB  
Article
Detection of Short-Circuit Faults in Induction Motor Winding Turns Using a Neural Network and Its Implementation in FPGA
by Luz del Carmen García-Rodríguez, Raúl Santiago-Montero, Jose de Jesus Rangel-Magdaleno, Francisco Javier Pérez-Pinal, Rogelio José González-González, Allan G. S. Sánchez and Alejandro Espinosa-Calderón
Processes 2025, 13(3), 815; https://doi.org/10.3390/pr13030815 - 11 Mar 2025
Viewed by 790
Abstract
Nowadays, induction motors are an essential part of industrial development. Faults due to short-circuit turns within induction motors are “incipient faults”. This type of failure affects engine operation through undesirable vibrations. Such vibrations negatively affect the operation of the system or the products [...] Read more.
Nowadays, induction motors are an essential part of industrial development. Faults due to short-circuit turns within induction motors are “incipient faults”. This type of failure affects engine operation through undesirable vibrations. Such vibrations negatively affect the operation of the system or the products with which said motor is in contact. Early fault detection prevents sudden downtime in the industry that can result in heavy economic losses. The incipient failures these motors can present have been a vast research topic worldwide. Existing methodologies for detecting incipient faults in alternating current motors have the problem that they are implemented at the simulation level, or are invasive, or do not allow in situ measurements, or their digital implementation is complex. This article presents the design and development of a purpose-specific system capable of detecting short-circuit faults in the turns of the induction motor winding without interrupting the motor’s working conditions, allowing online measurements. This system is standalone, portable and allows non-invasive and in situ measurements to obtain phase currents. These data form classified descriptors using a multilayer perceptron neural network. This type of neural network enables agile and efficient digital processing. The developed neural network could classify current faults with an accuracy rate of 93.18%. The neural network was successfully implemented on a low-cost and low-range purpose-specific Field Programmable Gate Array board for online processing, taking advantage of its computing power and real time processing features. The measurement of phase current and the class of fault detected is displayed on a liquid-crystal display screen, allowing the user to take necessary actions before major faults occur. Full article
(This article belongs to the Special Issue Control Process, Fault Detection and Classification in Power Systems and Industrial Machinery)
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54 pages, 10843 KiB  
Article
AI-Driven Fault Detection and Maintenance Optimization for Aviation Technical Support Systems
by Igor Kabashkin, Vladimir Perekrestov and Maksim Pivovar
Processes 2025, 13(3), 666; https://doi.org/10.3390/pr13030666 - 26 Feb 2025
Viewed by 1480
Abstract
This study investigates the integration of customization and personalization approaches in aviation maintenance through Aviation Technical Support as a Service (ATSaaS) platform. Through a comprehensive survey of 86 small and medium-sized airlines, combined with mathematical modeling of fault detection systems, the study develops [...] Read more.
This study investigates the integration of customization and personalization approaches in aviation maintenance through Aviation Technical Support as a Service (ATSaaS) platform. Through a comprehensive survey of 86 small and medium-sized airlines, combined with mathematical modeling of fault detection systems, the study develops and validates a hybrid framework that integrates traditional maintenance approaches with AI-driven solutions. The comparative analysis demonstrates that the hybrid model significantly outperforms both pure customization and pure personalization approaches, achieving a 95% fault detection rate compared to 75% for customization-only and 88% for personalization-only models. The hybrid approach also showed superior performance in predictive maintenance effectiveness (96%), operational downtime reduction (92%), and cost optimization (90%). The research presents three architectural frameworks for ATSaaS implementation—customization-based, personalization-based, and hybrid—providing a structured approach for different airline categories. Large airlines, with their extensive technical expertise and complex operational requirements, benefit from enhancing their customized maintenance programs with personalization tools, improving overall maintenance efficiency by 23%. Simultaneously, smaller operators, often constrained by limited resources, can use ATSaaS platforms to access sophisticated maintenance capabilities without extensive in-house expertise, reducing operational costs by 35% compared to traditional approaches. The study concludes that the successful integration of customization and personalization through ATSaaS platforms represents a promising direction for optimizing aviation maintenance operations, supporting the industry’s movement toward data-driven, adaptive maintenance solutions. Full article
(This article belongs to the Special Issue Control Process, Fault Detection and Classification in Power Systems and Industrial Machinery)
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