Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.5
Journals
Machines
Special Issues
AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems
share announcement

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: 28 February 2026 | Viewed by 923

Special Issue Editors

Dr. Dandan Peng

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: intelligent prognostics and health management (PHM); anomaly detection; remaining useful life (RUL) prediction; sensor-based AI modeling; physics-informed machine learning for complex industrial systems (e.g., EVs, wind turbines, aerospace, energy storage systems)
Dr. Xiaoxi Hu

E-Mail Website
Guest Editor
School of Vehicle and Mobility, State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Interests: intelligent fault diagnosis; railway transportation; industrial intelligence; prognosis and health management
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.

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 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

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

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 120
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)
►▼ Show Figures

Figure 1

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
Viewed by 127
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)
►▼ Show Figures

Figure 1

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
Viewed by 340
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop