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Machines
Special Issues
AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems
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AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 6996

Special Issue Editors

Prof. Dr. Dandan Peng

E-Mail Website
Guest Editor
School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
Interests: AI-based fault diagnosis and prognosis; health management; complex industrial systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoxi Hu

E-Mail Website
Guest Editor
State Key Laboratory of Intelligent Green Vehicle and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: artificial intelligence; transportation engineering; railway engineering; control systems engineering; condition monitoring; fault diagnosis; fault detection; remaining useful life prediction; computer vision; object detection; image segmentation; transport engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Jipu Li

E-Mail Website
Guest Editor
Department of Industrial and System Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: industrial big data; intelligent maintenance and health management; uncertainty qualification
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chuanjiang Li

E-Mail Website
Guest Editor
State Key Laboratory of Public Big Data, Guizhou University, Guizhou 550025, China
Interests: UAV big data; low-altitude equipment; UAV intelligent operation and maintenance and digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The safe, efficient, and intelligent operation of complex industrial systems is essential to the sustainable development of key industries such as energy and power, rail transportation, aerospace, process manufacturing, and intelligent equipment. These systems often exhibit strong coupling, time-varying operating conditions, and diverse failure modes. Traditional scheduled or experience-based maintenance strategies are increasingly insufficient to meet modern demands for high reliability, reduced costs, and full life-cycle management.

Recent advances in sensing, data acquisition, and computing have accelerated the adoption of data-driven prognostics and health management (PHM) methods. In particular, integrating artificial intelligence (AI), edge computing, digital twins, and foundation models has unlocked new capabilities in early fault detection, remaining useful life (RUL) prediction, and adaptive maintenance optimization. Furthermore, the emergence of explainable AI techniques has enhanced the transparency and trustworthiness of intelligent maintenance systems.

This Special Issue will gather high-quality original research and reviews on the latest innovations, methodologies, and applications in AI-enabled PHM for complex industrial systems. Topics include, but are not limited to, the following:

  • Multi-source heterogeneous data fusion;
  • Anomaly detection, fault diagnosis, and RUL prediction;
  • AI–digital twin integration for intelligent health management;
  • Hybrid modeling combining physics-based and data-driven methods;
  • Explainable AI and foundation models for industrial monitoring;
  • Applications across energy, transportation, aerospace, and manufacturing.

Prof. Dr. Dandan Peng
Dr. Xiaoxi Hu
Dr. Jipu Li
Prof. Dr. Chuanjiang Li
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

  • artificial intelligence
  • diagnostics and prognostics
  • digital twin
  • explainable AI
  • large foundation models

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Published Papers (6 papers)

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Research

37 pages, 2896 KB  
Article
Energy-Efficient Resilience Scheduling for Elevator Group Control via Queueing-Based Planning and Safe Reinforcement Learning
by Tingjie Zhang, Tiantian Zhang, Hao Zou, Chuanjiang Li and Jun Huang
Machines 2026, 14(3), 352; https://doi.org/10.3390/machines14030352 - 21 Mar 2026
Viewed by 417
Abstract
High-rise elevator group control systems operate under pronounced nonstationarity during commuting peaks, post-event surges, and capacity degradation, where the waiting time distribution becomes right-tail heavy and stresses service-level agreements (SLAs) defined by coverage and high-quantile targets. At the same time, the time-of-use tariffs [...] Read more.
High-rise elevator group control systems operate under pronounced nonstationarity during commuting peaks, post-event surges, and capacity degradation, where the waiting time distribution becomes right-tail heavy and stresses service-level agreements (SLAs) defined by coverage and high-quantile targets. At the same time, the time-of-use tariffs and carbon constraints sharpen the tension between peak-power control, energy savings, and service capacity. This paper proposes a two-layer resilience scheduling framework that integrates queueing-based planning with safe reinforcement learning (RL) fine-tuning. In the planning layer, parsimonious queueing approximations and scenario-based evaluation construct a finite set of implementable mode cards and emergency switching cards; Sample Average Approximation (SAA) combined with Conditional Value-at-Risk (CVaR) constraints filter candidates to enforce tail-risk-aware service limits while keeping power demand within a prescribed envelope. In the execution layer, online dispatch is formulated as a constrained Markov decision process; within the planning layer limits, action masking and Lagrangian safe RL learn small adaptive adjustments to suppress tail-waiting risk and improve recovery dynamics without increasing peak-power commitments. The experiments under morning peaks and post-event surges confirm tail risk reduction and accelerated recovery. For partial outages, the framework prioritizes SLA coverage and recovery speed, accepting a bounded increase in tail risk as a manageable trade-off. Throughout all tests, peak power remains within the prescribed limits. Improvements persist across random seeds and demand fluctuations, indicating distributional robustness and cross-scenario generalization. Ablation studies further reveal complementary roles: removing the planning layer CVaR screening worsens tail performance, while removing the execution layer action masking increases constraint violations and destabilizes recovery. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems)
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19 pages, 2577 KB  
Article
A Hybrid Large-Kernel CNN and Markov Feature Framework for Remaining Useful Life Prediction
by Yuke Wang, Che Su, Peng Wang, Junquan Zhen and Dong Wang
Machines 2026, 14(1), 57; https://doi.org/10.3390/machines14010057 - 1 Jan 2026
Cited by 1 | Viewed by 542
Abstract
Remaining Useful Life (RUL) prediction has become a crucial component in predictive maintenance and condition-based operation with the rapid advancement of industrial automation and the increasing complexity of mechanical systems. Although existing deep learning models, such as Long Short-Term Memory (LSTM) networks and [...] Read more.
Remaining Useful Life (RUL) prediction has become a crucial component in predictive maintenance and condition-based operation with the rapid advancement of industrial automation and the increasing complexity of mechanical systems. Although existing deep learning models, such as Long Short-Term Memory (LSTM) networks and conventional Convolutional Neural Networks (CNNs), have demonstrated effectiveness in modeling equipment degradation from multivariate sensor data, they still face several limitations. Recurrent architectures often suffer from vanishing gradients and struggle to capture long-term dependencies, while CNN-based methods typically rely on small convolutional kernels and deterministic feature extractors, limiting their ability to model long-range dependencies and stochastic degradation transitions. To address these challenges, this study proposes a novel hybrid deep learning framework that integrates large-kernel convolutional feature extraction with Markov transition modeling for RUL prediction. Specifically, the large-kernel CNN captures both local and global degradation patterns, while the Markov feature module encodes probabilistic state transitions to characterize the stochastic evolution of equipment health. Furthermore, a lightweight channel attention mechanism is incorporated to adaptively emphasize degradation-sensitive sensor information, thereby enhancing feature discriminability. Extensive experiments conducted on the NASA C-MAPSS turbofan engine dataset demonstrate that the proposed model consistently outperforms conventional CNN, LSTM, and hybrid baselines in terms of Root Mean Square Error (RMSE) and the NASA scoring metric. The results verify that combining deep convolutional representations with probabilistic transition information significantly enhances prediction accuracy and robustness in industrial RUL estimation tasks. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems)
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18 pages, 5530 KB  
Article
A Hybrid Fractal-NURBS Model for Characterizing Material-Specific Mechanical Surface Contact
by Leilei Zhang, Yingkun Mu, Kui Luo, Guang Ren and Zisheng Wang
Machines 2026, 14(1), 49; https://doi.org/10.3390/machines14010049 - 30 Dec 2025
Viewed by 394
Abstract
The reliability of mechanical systems hinges on analyzing the actual surface-to-surface contact area, which critically influences dynamic behavior, friction, material performance, and thermal dissipation. Uneven surfaces lead to incomplete contact, where only a fraction of asperities touch, creating a nominal contact area. This [...] Read more.
The reliability of mechanical systems hinges on analyzing the actual surface-to-surface contact area, which critically influences dynamic behavior, friction, material performance, and thermal dissipation. Uneven surfaces lead to incomplete contact, where only a fraction of asperities touch, creating a nominal contact area. This study proposes a novel fractal contact model for various mechanical behaviors between mechanical contact surfaces, integrating the Weierstrass–Mandelbrot fractal function and nonuniform rational B-spline interpolation (NURBS) to model material-dependent actual contact conditions. Furthermore, this research delved into the changes in thermal conductivity across the surfaces of metal materials within a simulated setting. It maintained a contact ratio ranging from 0.038% to 15.2%, a factor that remained unaffected by contact pressure. Both experimental and simulated findings unveiled an actual contact rate spanning from 0.44% to 1.06%, thereby underscoring the distinctive interface behaviors specific to different materials. The proposed approach provides fresh perspectives for investigating material–contact interactions and tackling associated engineering hurdles. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems)
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15 pages, 2877 KB  
Article
A Hybrid Approach Based on a Windowed-EMD Temporal Convolution–Reallocation Network and Physical Kalman Filtering for Bearing Remaining Useful Life Estimation
by Zhe Wei, Lang Lang, Mo Chen, Chao Ge, Enguo Tong and Liang Chen
Machines 2025, 13(9), 802; https://doi.org/10.3390/machines13090802 - 3 Sep 2025
Viewed by 991
Abstract
Rolling bearings are one of the core components of industrial equipment. Owing to the rapid development of deep learning methods, a multitude of data-driven remaining useful life (RUL) estimation approaches have been proposed recently. However, several challenges persist in existing methods: the limited [...] Read more.
Rolling bearings are one of the core components of industrial equipment. Owing to the rapid development of deep learning methods, a multitude of data-driven remaining useful life (RUL) estimation approaches have been proposed recently. However, several challenges persist in existing methods: the limited accuracy of traditional data-driven models, instability in sequence prediction, and poor adaptability to diverse operational environments. To address these issues, we propose a novel prognostics approach integrating three key components: time-intrinsic mode functions-derived feature representation (TIR) sequences, a one-dimensional temporal feature convolution–reallocation network (TFCR) with a flexible configuration scheme, and a physics-based Kalman filtering method. The approach first converts denoised signals into TIR-sequences using windowed empirical mode decomposition (EMD). The TFCR network then extracts hidden high-dimensional features from these sequences and maps them to the initial RUL. Finally, physics-based Kalman filtering is applied to enhance prediction stability and enforce physical constraints, producing refined RUL estimates. The experimental results based on the XJTU-SY dataset show the superiority of the proposed approach and further prove the feasibility of this method in bearing RUL estimation. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems)
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23 pages, 5190 KB  
Article
Fault Diagnosis of Rolling Bearing Based on Spectrum-Adaptive Convolution and Interactive Attention Mechanism
by Hongxing Zhao, Yongsheng Fan, Junchi Ma, Yinnan Wu, Ning Qin, Hui Wang, Jing Zhu and Aidong Deng
Machines 2025, 13(9), 795; https://doi.org/10.3390/machines13090795 - 2 Sep 2025
Cited by 1 | Viewed by 1510
Abstract
With the development of artificial intelligence technology, intelligent fault diagnosis methods based on deep learning have received extensive attention. Among them, convolutional neural network (CNN) has been widely applied in the fault diagnosis of rolling bearings due to its strong feature extraction ability. [...] Read more.
With the development of artificial intelligence technology, intelligent fault diagnosis methods based on deep learning have received extensive attention. Among them, convolutional neural network (CNN) has been widely applied in the fault diagnosis of rolling bearings due to its strong feature extraction ability. However, traditional CNN models still have deficiencies in the extraction of early weak fault features and the suppression of high noise. In response to these problems, this paper proposes a convolutional neural network (SAWCA-net) that integrates spectrum-guided dynamic variable-width convolutional kernels and dynamic interactive time-domain–channel attention mechanisms. In this model, the spectrum-adaptive wide convolution is introduced. Combined with the time-domain and frequency-domain statistical characteristics of the input signal, the receptive field of the convolution kernel is adaptively adjusted, and the sampling position is dynamically adjusted, thereby enhancing the model’s modeling ability for periodic weak faults in complex non-stationary vibration signals and improving its anti-noise performance. Meanwhile, the dynamic time–channel attention module was designed to achieve the collaborative modeling of the time-domain periodic structure and the feature dependency between channels, improve the feature utilization efficiency, and suppress redundant interference. The experimental results show that the fault diagnosis accuracy rates of SAWCA-Net on the bearing datasets of Case Western Reserve University (CWRU) and Xi’an Jiaotong University (XJTU-SY) reach 99.15% and 99.64%, respectively, which are superior to the comparison models and have strong generalization and robustness. The visualization results of t-distributed random neighbor embedding (t-SNE) further verified its good feature separability and classification ability. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems)
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26 pages, 4411 KB  
Article
Vibration Characteristic Analysis and Dynamic Reliability Modeling of Multi-Rotor UAVs
by Keyi Zhou, Di Zhou, Xiru Wang, Yonglin Guo and Huimin Chen
Machines 2025, 13(8), 697; https://doi.org/10.3390/machines13080697 - 6 Aug 2025
Cited by 3 | Viewed by 2360
Abstract
To address the unclear vibration failure mechanism and the lack of system-level reliability evaluation methods for multirotor transport UAVs under complex operating conditions, this paper proposes a comprehensive analysis method that combines fluid–structure interaction dynamics with dynamic reliability theory. First, the study analyzes [...] Read more.
To address the unclear vibration failure mechanism and the lack of system-level reliability evaluation methods for multirotor transport UAVs under complex operating conditions, this paper proposes a comprehensive analysis method that combines fluid–structure interaction dynamics with dynamic reliability theory. First, the study analyzes rotor dynamics and vibration characteristics under bidirectional fluid–structure coupling and obtains vibration displacement data. Then, it builds a dynamic reliability model using the Second-Order Reliability Method (SORM) and the Laplace method. The model explores reliability evolution in a dynamic airflow coupling environment. Finally, it establishes a multi-rotor UAV system reliability evaluation method and analyzes the impact of rotor number and layout on system reliability. The results provide a theoretical basis for structural optimization, reliability assurance, and fault tolerance improvement of multi-rotor UAVs under complex conditions. Full article
(This article belongs to the Special Issue AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems)
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