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Machines
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Computational Intelligence for Fault Detection and Classification
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Computational Intelligence for Fault Detection and Classification

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 4841

Special Issue Editor

Prof. Dr. Alma Y. Alanis

E-Mail Website
Guest Editor
Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), University of Guadalajara, Guadalajara 44330, Mexico
Interests: intelligent control; discrete-time nonlinear systems; artificial neural networks; applications to electromechanical systems; biomedical systems; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence has become increasingly important for fault detection and classification in a wide range of systems, including manufacturing processes, power grids, and transportation systems. By analyzing large volumes of data generated by these systems, computational intelligence techniques can identify patterns and detect anomalies that might indicate the presence of faults, errors, or intrusions. These algorithms can also be used to predict when faults might occur, allowing for preventative maintenance and reducing the risk of downtime or safety hazards. In addition, computational intelligence techniques can aid in isolating the cause of a fault, which can be crucial for efficient repairs and minimizing the impact on the overall system. As such, computational intelligence for fault detection and classification represents a promising approach for improving the safety, reliability, and efficiency of complex systems, mainly in this modern interconnected world.

Papers with mathematical analysis and real-world application are particularly welcome.

Topics of interest include but are not limited to:

  • Computational intelligence for fault modeling;
  • Computational intelligence for fault tolerant control;
  • Computational intelligence for fault detection and diagnosis;
  • Computational intelligence for fault classification and isolation;
  • Computational intelligence for false data injection;
  • Computational intelligence in cybersecurity;
  • Computational intelligence to detect anomalies;
  • Fault detection and classification applied to robotics, dynamic systems, complex networks, biomedical systems, energy systems, industry, transportation, mechatronics, cyber-physical systems, economics and others.

Prof. Dr. Alma Y. Alanis
Guest Editor

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. Machines 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.

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

Published Papers (2 papers)

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19 pages, 887 KiB  
Article
Fault-Tolerant Closed-Loop Controller Using Online Fault Detection by Neural Networks
by Alma Y. Alanis, Jesus G. Alvarez, Oscar D. Sanchez, Hannia M. Hernandez and Arturo Valdivia-G
Machines 2024, 12(12), 844; https://doi.org/10.3390/machines12120844 - 25 Nov 2024
Cited by 1 | Viewed by 842
Abstract
This paper presents an online model-free sensor fault-tolerant control scheme capable of tolerating the most common faults affecting an induction motor. This approach involves using neural networks for fault detection to provide the controller with sufficient information to counteract adverse consequences due to [...] Read more.
This paper presents an online model-free sensor fault-tolerant control scheme capable of tolerating the most common faults affecting an induction motor. This approach involves using neural networks for fault detection to provide the controller with sufficient information to counteract adverse consequences due to sensor faults, such as degradation in performance, reliability, and even failures in the control system. The proposed approach does not consider the knowledge of the nominal model of the system or when the fault may occur. Therefore, a high-order recurrent neural network trained online by the Extended Kalman Filter is used to obtain a mathematical model of the system. The obtained model is used to synthesize a discrete-time sliding mode control. Then, the fault-detection and -isolation stage is performed by independent neural networks, which have as input the signal from the current sensor and the position sensor, respectively. In this way, the neural classifiers continuously monitor the sensors, showing the ability to know the sensor status. The combination of controller and fault detection maintains the operation of the motor during the time of the fault occurrence, whether due to sensor disconnection, degradation, or connection failure. In fact, the MLP neural network achieves an accuracy between 95% and 99% and shows an AUC of 97% to 99%, and this neural network correctly classifies true positives with acceptable performance. The Recall value is high, between 97% and 99%, and the F1 score confirms a good performance. In contrast, the CNN shows a higher accuracy, between 96% and 99% in accuracy and 98% to 99% in AUC. In addition, its Recall and F1 reflect a better balance and capacity to handle complex data, demonstrating its superiority to MLP in fault classification. Therefore, neural networks are a promising approach in areas such as fault-tolerant control. Full article
(This article belongs to the Special Issue Computational Intelligence for Fault Detection and Classification)
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17 pages, 4409 KiB  
Article
Wavelet-Based Computational Intelligence for Real-Time Anomaly Detection and Fault Isolation in Embedded Systems
by Jesus Pacheco, Victor H. Benitez, Guillermo Pérez and Agustín Brau
Machines 2024, 12(9), 664; https://doi.org/10.3390/machines12090664 - 22 Sep 2024
Cited by 2 | Viewed by 2838
Abstract
In today’s technologically advanced landscape, sensors feed critical data for accurate decision-making and actions. Ensuring the integrity and reliability of sensor data is paramount to system performance and security. This paper introduces an innovative approach utilizing discrete wavelet transforms (DWT) embedded within microcontrollers [...] Read more.
In today’s technologically advanced landscape, sensors feed critical data for accurate decision-making and actions. Ensuring the integrity and reliability of sensor data is paramount to system performance and security. This paper introduces an innovative approach utilizing discrete wavelet transforms (DWT) embedded within microcontrollers to scrutinize sensor data meticulously. Our methodology aims to detect and isolate malfunctions, misuse, or any anomalies before they permeate the system, potentially causing widespread disruption. By leveraging the power of wavelet-based analysis, we embed computational intelligence directly into the microcontrollers, enabling them to monitor and validate their outputs in real-time. This proactive anomaly detection framework is designed to distinguish between normal and aberrant sensor behaviors, thereby safeguarding the system from erroneous data propagation. Our approach significantly enhances the reliability of embedded systems, providing a robust defense against false data injection attacks and contributing to overall cybersecurity. Full article
(This article belongs to the Special Issue Computational Intelligence for Fault Detection and Classification)
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