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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

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2084

Special Issue Editors

Dr. Sumika Chauhan

E-Mail Website
Guest Editor
Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science 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
Dr. Govind Vashishtha

E-Mail Website
Guest Editor
Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science 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
Dr. Rajesh Kumar

E-Mail Website
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 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 might 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 into 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 aspires 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 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. 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 1600 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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Published Papers (2 papers)

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Research

21 pages, 5919 KiB  
Article
A Computationally Efficient Method for the Diagnosis of Defects in Rolling Bearings Based on Linear Predictive Coding
by Mohammad Mohammad, Olga Ibryaeva, Vladimir Sinitsin and Victoria Eremeeva
Algorithms 2025, 18(2), 58; https://doi.org/10.3390/a18020058 - 21 Jan 2025
Viewed by 680
Abstract
Monitoring the condition of rolling bearings is a crucial task in many industries. An efficient tool for diagnosing bearing defects is necessary since they can lead to complete machine failure and significant economic losses. Traditional diagnosis solutions often rely on a complex artificial [...] Read more.
Monitoring the condition of rolling bearings is a crucial task in many industries. An efficient tool for diagnosing bearing defects is necessary since they can lead to complete machine failure and significant economic losses. Traditional diagnosis solutions often rely on a complex artificial feature extraction process that is time-consuming, computationally expensive, and too complex to deploy in practice. In actual working conditions, however, the amount of labeled fault data available is relatively small, so a deep learning model with good generalization and high accuracy is difficult to train. This paper proposes a solution that uses a simple feedforward artificial neural network (NN) for classification and adopts the linear predictive coding (LPC) algorithm for feature extraction. The LPC algorithm finds several coefficients for a given signal segment containing information about the signal spectrum, which is sufficient for further classification. The LPC-NN solution was tested on the Case Western Reserve University (CWRU) and South Ural State University (SUSU) datasets. The results demonstrated that, in most cases, LPC-NN yielded an accuracy of 100%. The proposed method achieves higher diagnostic accuracy and stability to load changes than other advanced techniques, has a significantly improved time performance, and is conducive to real-time industrial fault diagnosis. Full article
(This article belongs to the Special Issue AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis)
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16 pages, 6457 KiB  
Article
Intelligent Fault Diagnosis for Rotating Mechanical Systems: An Improved Multiscale Fuzzy Entropy and Support Vector Machine Algorithm
by Yuxin Pan, Yinsheng Chen, Xihong Fei, Kang Wang, Tian Fang and Jing Wang
Algorithms 2024, 17(12), 588; https://doi.org/10.3390/a17120588 - 20 Dec 2024
Viewed by 856
Abstract
Rotating mechanical systems (RMSs) are widely applied in various industrial fields. Intelligent fault diagnosis technology plays a significant role in improving the reliability and safety of industrial equipment. A new algorithm based on improved multiscale fuzzy entropy and support vector machine (IMFE-SVM) is [...] Read more.
Rotating mechanical systems (RMSs) are widely applied in various industrial fields. Intelligent fault diagnosis technology plays a significant role in improving the reliability and safety of industrial equipment. A new algorithm based on improved multiscale fuzzy entropy and support vector machine (IMFE-SVM) is proposed for the automatic diagnosis of various fault types in elevator rotating mechanical systems. First, the empirical mode decomposition (EMD) method is utilized to construct a decomposition model of the vibration data for the extraction of relevant parameters related to the fault feature. Secondly, the improved multiscale fuzzy entropy (IMFE) model is employed, where the scale factor of the multiscale fuzzy entropy (MFE) is extended to multiple subsequences to resolve the problem of insufficient coarse granularity in the traditional MFE. Subsequently, linear discriminant analysis (LDA) is applied to reduce the dimensionality of the extracted features in order to overcome the problem of feature redundancy. Finally, a support vector machine (SVM) model is utilized to construct the optimal hyperplane for the diagnosis of fault types. Experimental results indicate that the proposed method outperforms other state-of-the-art methods in the fault diagnosis of elevator systems. Full article
(This article belongs to the Special Issue AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis)
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