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Machines
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Intelligent Fault Diagnosis and Predictive Maintenance Systems: Advanced Methods for...
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Intelligent Fault Diagnosis and Predictive Maintenance Systems: Advanced Methods for Industrial Equipment and Dynamic Operating Conditions

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2821

Special Issue Editors

Prof. Dr. Huitian Lu

E-Mail Website
Guest Editor
Ness School of Management and Economics, South Dakota State University, Brookings, SD 57007, USA
Interests: system reliability; dynamical systems; data modeling and forecasting; decision theory; measurement error
Dr. Mohammad Omidi

E-Mail Website
Guest Editor
School of Electrical Engineering, Hebei University, Baoding, China
Interests: turbomachinery; fluid dynamics; green industries; renewable energy systems
Dr. Ali Naderi Bakhtiyari

E-Mail Website
Guest Editor
1. Centre for Advanced Laser Manufacturing (CALM), School of Mechanical Engineering, Shandong University of Technology, Zibo, China
2. R & D Department, Bodor laser Co., Jinan, China
Interests: smart manufacturing; machine learning; AI-driven predictive modeling and optimization; laser material processing; digital twins in manufacturing; intelligent control systems

Special Issue Information

Dear Colleagues,

The evolution toward Industry 4.0 has initiated a paradigm shift in industrial maintenance, moving from traditional reactive or scheduled interventions to data-driven predictive strategies. Central to this transformation is the ability to accurately predict the remaining useful life (RUL) of machinery, which stands as a cornerstone for optimizing operational efficiency, ensuring system reliability, and minimizing economic losses. The proliferation of advanced sensors and the Industrial Internet of Things (IIoT) now provides an unprecedented volume of data, creating fertile ground for the development of sophisticated prognostic models.

This Special Issue seeks to collate seminal research and cutting-edge applications focused on RUL prediction for industrial equipment. We welcome high-quality submissions on novel machine learning and deep learning models, hybrid approaches that synergize physics-based principles with data-driven techniques, advanced methods for sensor data fusion, and compelling real-world case studies that demonstrate the tangible impact of predictive maintenance systems. By showcasing these advancements, this Issue aims to accelerate the deployment of intelligent health management systems across modern industries.

Prof. Dr. Huitian Lu
Dr. Mohammad Omidi
Dr. Ali Naderi Bakhtiyari
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. 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

  • remaining useful life (RUL)
  • prognostics and health management (PHM)
  • performance degrading and assessment
  • predictive maintenance
  • deep learning
  • fault diagnosis
  • data-driven prognostics
  • turbomachinery

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Published Papers (4 papers)

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Research

18 pages, 972 KB  
Article
CPU Deployment-Oriented Evaluation of Compact Neural Networks for Remaining Useful Life Prediction
by Ali Naderi Bakhtiyari, Vahid Hassani and Mohammad Omidi
Machines 2026, 14(4), 375; https://doi.org/10.3390/machines14040375 - 28 Mar 2026
Viewed by 435
Abstract
Remaining Useful Life (RUL) prediction is a key component of prognostics and health management for modern industrial systems. While deep learning methods have significantly improved prediction accuracy, many existing approaches rely on large neural networks that are difficult to deploy on resource-constrained edge [...] Read more.
Remaining Useful Life (RUL) prediction is a key component of prognostics and health management for modern industrial systems. While deep learning methods have significantly improved prediction accuracy, many existing approaches rely on large neural networks that are difficult to deploy on resource-constrained edge devices. This study presents a deployment-oriented evaluation of compact neural networks for RUL prediction using the NASA C-MAPSS turbofan engine benchmark. Two lightweight hybrid architectures, CNN–GRU and CNN–TCN, were developed with approximately 28k–32k parameters to represent realistic models for CPU-based edge inference. A systematic experimental analysis was conducted across all four C-MAPSS subsets (FD001–FD004), which represent increasing levels of operational and fault complexity. In addition to baseline performance, two post-training compression techniques (i.e., global unstructured magnitude pruning and dynamic INT8 quantization) were evaluated. To assess real deployment behavior, inference latency was measured on both a high-performance Intel x86 workstation and a resource-constrained ARM platform. Results show that CNN–GRU generally achieves higher predictive accuracy, whereas CNN–TCN provides more consistent and lower inference latency due to its convolution-only temporal modeling. Unstructured pruning can yield modest improvements in prediction accuracy, suggesting a regularization effect, but it does not reliably reduce model size or latency on standard CPUs due to the overhead associated with pruning masks. Dynamic quantization substantially reduces model size (particularly for CNN–GRU) while preserving predictive accuracy; however, it increases runtime latency because of additional quantization and dequantization operations. These findings demonstrate that compression techniques commonly used for large models do not necessarily translate into deployment benefits for already compact RUL architectures and highlight the importance of hardware-aware evaluation when designing edge prognostics systems. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Predictive Maintenance Systems: Advanced Methods for Industrial Equipment and Dynamic Operating Conditions)
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32 pages, 2621 KB  
Article
State-Space Estimation in Discriminant Subspace: A Kalman Filtering Approach for Turbofan Engine RUL Prediction
by Uğur Yıldırım and Hüseyin Afșer
Machines 2026, 14(2), 226; https://doi.org/10.3390/machines14020226 - 14 Feb 2026
Viewed by 518
Abstract
Accurate remaining useful life (RUL) prediction of turbofan engines is critical for aviation safety and maintenance optimization; however, deep learning approaches often lack interpretability and require extensive training data. This study proposes a framework integrating Linear Discriminant Analysis (LDA) with Kalman filtering for [...] Read more.
Accurate remaining useful life (RUL) prediction of turbofan engines is critical for aviation safety and maintenance optimization; however, deep learning approaches often lack interpretability and require extensive training data. This study proposes a framework integrating Linear Discriminant Analysis (LDA) with Kalman filtering for turbofan engine prognostics. The methodology projects high-dimensional sensor measurements onto a two-dimensional LDA subspace, where degradation trajectories are tracked using state-space estimation, with RUL predictions derived from distances to learned critical failure boundaries. A health index-based classification scheme partitions engine states into three operational regions: Critical, Warning, and Healthy. Three Kalman filter variants—Linear Kalman Filter (LKF), Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF)—were compared against an Autoregressive (AR) baseline using the NASA C-MAPSS dataset. Using the Prognostics and Health Management 2008 asymmetric scoring function, UKF achieved the best performance with a Score of 552572, representing a 54.9% improvement over AR (1224299), indicating substantially fewer late predictions. While RMSE values remained comparable across methods (36–37 cycles), the Kalman filter variants demonstrated meaningful improvements in avoiding dangerous late predictions critical for safety-oriented maintenance scheduling. EKF also demonstrated substantial improvement with 36.1% Score reduction. Classification accuracy improved from 70.72% (AR) to 73.27% (UKF). The proposed LDA–Kalman framework provides a computationally efficient and geometrically interpretable alternative to deep learning methods for real-time engine health monitoring. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Predictive Maintenance Systems: Advanced Methods for Industrial Equipment and Dynamic Operating Conditions)
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21 pages, 2194 KB  
Article
Convolutional Autoencoder-Based Method for Predicting Faults of Cyber-Physical Systems Based on the Extraction of a Semantic State Vector
by Konstantin Zadiran and Maxim Shcherbakov
Machines 2026, 14(1), 126; https://doi.org/10.3390/machines14010126 - 22 Jan 2026
Viewed by 371
Abstract
Modern industrial equipment is a cyber-physical system (CPS) consisting of physical production components and digital controls. Lowering maintenance costs and increasing availability is important to improve its efficiency. Modern methods, based on solving event prediction problem, in particular, prediction of remaining useful life [...] Read more.
Modern industrial equipment is a cyber-physical system (CPS) consisting of physical production components and digital controls. Lowering maintenance costs and increasing availability is important to improve its efficiency. Modern methods, based on solving event prediction problem, in particular, prediction of remaining useful life (RUL), are used as a crucial step in a framework of reliability-centered maintenance to increase efficiency. But modern methods of RUL forecasting fall short when dealing with real-world scenarios, where CPS are described by multidimensional continuous high-frequency data with working cycles with variable duration. To overcome this problem, we propose a new method for fault prediction, which is based on extraction of semantic state vectors (SSVs) from working cycles of equipment. To implement SSV extraction, a new method, based on convolutional autoencoder and extraction of hidden state, is proposed. In this method, working cycles are detected in input data stream, and then they are converted to images, on which an autoencoder is trained. The output of an intermediate layer of an autoencoder is extracted and processed into SSVs. SSVs are then combined into a time series on which RUL is forecasted. After optimization of hyperparameters, the proposed method shows the following results: RMSE = 1.799, MAE = 1.374. These values are significantly more accurate than those obtained using existing methods: RMSE = 14.02 and MAE = 10.71. Therefore, SSV extraction is a viable technique for forecasting RUL. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Predictive Maintenance Systems: Advanced Methods for Industrial Equipment and Dynamic Operating Conditions)
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22 pages, 5185 KB  
Article
AI-Based Predictive Maintenance for Rotor Crack Fault Diagnosis for Variable-Speed Machines Using Transfer Learning
by Sudhar Rajagopalan, Seemu Sharma and Ashish Purohit
Machines 2026, 14(1), 17; https://doi.org/10.3390/machines14010017 - 21 Dec 2025
Viewed by 839
Abstract
Fatigue-related ‘rotor crack’ can cause catastrophic failure if neglected. Thus, IoT-enabled AI-based predictive maintenance for fault detection and diagnosis is explored. Training and testing AI models under similar conditions improves their prediction performance. On variable speed machines, loss of performance occurs when the [...] Read more.
Fatigue-related ‘rotor crack’ can cause catastrophic failure if neglected. Thus, IoT-enabled AI-based predictive maintenance for fault detection and diagnosis is explored. Training and testing AI models under similar conditions improves their prediction performance. On variable speed machines, loss of performance occurs when the testing speed differs from the training speed. This research addresses significant performance loss issues using convolutional neural network (CNN)-based transfer learning models. The main causes of performance loss are domain shift, overfitting, data class imbalance, low fault data availability, and biassed prediction. All the above difficult issues make CNN-based fault prediction systems function badly under varying operating conditions. The proposed methodology addresses all domain adaptation challenges. The proposed methodology was tested by collecting vibration data from an experimental rotor system under varied operating conditions. The proposed methodology outperforms classical machine learning (ML) and deep learning (DL) models, overcoming the overfitting issue with optimised hyperparameters, achieving a prediction accuracy of 99.5%. Under varying operating conditions, it outperforms with a prediction accuracy of 93.2%, and in the ‘data class imbalanced’ scenario, the maximal transfer learning capability achieved was 84.4% with the highest F1-Score. Thus, CNN-based transfer learning enables industrial variable speed machines diagnose rotor crack flaws better than ML and DL models. Full article
(This article belongs to the Special Issue Intelligent Fault Diagnosis and Predictive Maintenance Systems: Advanced Methods for Industrial Equipment and Dynamic Operating Conditions)
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