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Machines
Special Issues
AI-Driven Reliability Analysis and Predictive Maintenance
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AI-Driven Reliability Analysis and Predictive Maintenance

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 2135

Special Issue Editors

Dr. Phuc Do

E-Mail Website
Guest Editor
CNRS, CRAN, Université de Lorraine, 54000 Nancy, France
Interests: stochastic modelling for evaluation/prediction of performance indicators; reliability and maintenance modelling; AI-based maintenance optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cristiano Cavalcante

E-Mail Website
Guest Editor
Departamento de Engenharia de Produção, Universidade Federal de Pernambuco, Recife, Brazil
Interests: reliability modeling and assessment; maintenance modeling and optimization; AI-based maintenance decision making

Special Issue Information

Dear Colleagues,

AI-driven reliability analysis and predictive maintenance is a proactive and data-driven approach that leverages artificial intelligence (AI) technologies to analyse the reliability and proactively optimize maintenance of industrial systems and equipment. By harnessing the power of machine learning algorithms, predictive analytics, and advanced data processing techniques, this methodology allows us to efficiently anticipate and prevent equipment failures before they occur, thereby minimizing downtime, reducing maintenance costs, and maximizing operational efficiency.

This Special Issue will gather innovative research contributions and practical applications in the field of leveraging AI technologies for optimizing reliability analysis and predictive maintenance strategies in industrial settings.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Advanced prognostics approaches;
  • Advanced predictive maintenance models;
  • Data-driven approaches for equipment failure prediction and prognosis;
  • AI for predictive maintenance;
  • Reinforcement learning for maintenance decision-making;
  • Data mining and big data in prognostics and maintenance;
  • Maintenance schedules and resource allocation using AI techniques;
  • Integration of AI-driven solutions for reliability assessment and analysis;
  • Challenges and opportunities involved in deploying AI-driven reliability analysis solutions in diverse industrial sectors.

Dr. Phuc Do
Prof. Dr. Cristiano Cavalcante
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. 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.

Keywords

  • predictive maintenance
  • data-driven prognosis
  • AI-driven reliability analysis
  • machine learning

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

Published Papers (3 papers)

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Research

25 pages, 6005 KiB  
Article
Simplified Data-Driven Models for Gas Turbine Diagnostics
by Igor Loboda, Juan Luis Pérez Ruíz, Iván González Castillo, Jonatán Mario Cuéllar Arias and Sergiy Yepifanov
Machines 2025, 13(5), 344; https://doi.org/10.3390/machines13050344 - 22 Apr 2025
Abstract
The maintenance of gas turbines relies a lot on gas path diagnostics (GPD), which includes two approaches. The first approach employs a physics-based model (thermodynamic model) to convert measurement shifts (deviations) induced by deterioration into fault parameters, which drastically simplify diagnostics. The second [...] Read more.
The maintenance of gas turbines relies a lot on gas path diagnostics (GPD), which includes two approaches. The first approach employs a physics-based model (thermodynamic model) to convert measurement shifts (deviations) induced by deterioration into fault parameters, which drastically simplify diagnostics. The second approach relies on data-driven models, makes diagnosis in the space of measurement deviations, and involves pattern recognition techniques. Although a thermodynamic model is an essential element of GPD, it has limitations. This model is a complex software critical to computer resources, and the computation sometimes does not converge. Therefore, it is difficult to use the model in online applications. Since the 1990s, we have developed many thermodynamic models for different engines. Since the 2000s, simplified data-driven models were investigated. This paper proposes to substitute a thermodynamic model for novel simplified data-driven models that have the same functionality, i.e., take into consideration the influence of both operating conditions and engine faults. The proposed models are formed and compared with the underlying thermodynamic model. To obtain a solid conclusion about these models, they are verified in twelve test cases formed by three test-case engines, two model types, and two approximation functions. Although the accuracy of the simplified models varies from 1.15% to 0.0082%, it was found acceptable even for the worst case. Thus, these simple-but-accurate models with the functionality of a physics-based model represent a good replacement for the latter. It is expected that the models will stimulate the further development of advanced diagnostic systems. Full article
(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)
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23 pages, 1962 KiB  
Article
Data Reduction in Proportional Hazards Models Applied to Reliability Prediction of Centrifugal Pumps
by Marc Vila Forteza, Diego Galar, Uday Kumar and Kai Goebel
Machines 2025, 13(3), 215; https://doi.org/10.3390/machines13030215 - 7 Mar 2025
Viewed by 483
Abstract
This paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry. To that end, a dataset collected over 8 years including both design and operational variables from [...] Read more.
This paper presents the use of proportional hazards regression models for predicting the Mean Time Between Failures (MTBF) of centrifugal pumps in the oil and gas industry. To that end, a dataset collected over 8 years including both design and operational variables from 675 pumps in an oil refinery was used to fit statistical models. Parametric and non-parametric transformations and restricted cubic splines were used to fit the covariates, thereby relaxing linearity assumptions and potentiating predictors with strong nonlinear effects on the outcome. Standard Principal Component Analysis (PCA) and sparse robust PCA methods were used for data reduction to simplify the fitted models and minimize overfitting. Models fitted with sparse robust PCA on non-parametrically transformed variables using an additive variance stabilizing (AVAS) method are suggested for further investigation. The complexity of the fitted models was reduced by 85% while at the same time providing for a more robust model as indicated by an improvement of the calibration slope from 0.830 to 0.936 with an essentially stable Akaike information criterion (AIC) (0.34% increase). Full article
(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)
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17 pages, 9200 KiB  
Article
Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network
by Hehua Yan, Jinbiao Tan, Yixiong Luo, Shiyong Wang and Jiafu Wan
Machines 2024, 12(12), 891; https://doi.org/10.3390/machines12120891 - 6 Dec 2024
Viewed by 748
Abstract
The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured [...] Read more.
The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured labeling scheme is introduced to allow for multi-granularity fault annotation. A hierarchical multi-granularity diagnostic network is designed to automatically learn multi-level fault information from condition data using feature extractors of varying granularity, allowing for the extraction of shared fault information across conditions. Additionally, a multi-granularity fault loss function is developed to help the deep network learn tree-structured labels, improving intra-class compactness and reducing hierarchical similarity between classes. Two experimental cases demonstrate that the proposed method exhibits robust cross-condition domain adaptability and performs better in unseen conditions than state-of-the-art methods. Full article
(This article belongs to the Special Issue AI-Driven Reliability Analysis and Predictive Maintenance)
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