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Machines
Special Issues
Intelligent Predictive Maintenance and Machine Condition Monitoring
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Intelligent Predictive Maintenance and Machine Condition Monitoring

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1031

Special Issue Editors

Dr. Cai Yi

E-Mail Website
Guest Editor
State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, Chengdu, China
Interests: train inspection and fault diagnosis technology
Dr. Qiuyang Zhou

E-Mail
Guest Editor
CRRC Academy Co., Ltd., Beijing 100160, China
Interests: failure analysis; fault diagnosis; higher-order statistics

Special Issue Information

Dear Colleagues,

The growing integration of intelligence and automation in high-end industrial equipment—including high-speed trains, wind turbines, engines, gas turbines, compressors, and machine tools—has heightened the importance of operational safety and reliability among both academic and industrial communities. Intelligent predictive maintenance and machine condition monitoring have emerged as essential approaches to sustain operational continuity, lower maintenance expenditures, and increase overall productivity. In response, scholars and engineers are actively developing sophisticated intelligent solutions to enable precise fault prediction, performance assessment, and maintenance decision-making. Advances in sensing systems, IoT infrastructure, and large-scale data processing have greatly facilitated the acquisition and analysis of equipment status data, establishing new opportunities for data-driven monitoring and prognostic methods. Accordingly, this Special Issue is dedicated to presenting cutting-edge research on condition monitoring, fault diagnosis, and predictive maintenance of high-end equipment through the application of advanced signal processing and artificial intelligence techniques.

This Special Issue welcomes the submission of high-quality original research and review manuscripts that introduce innovative concepts, algorithms, methodologies, and technologies contributing to the evolution of intelligent maintenance frameworks. The scope of this Special Issue includes, but is not limited to, the following topics:

  1. Intelligent fault prediction and condition management in high-speed trains, wind turbines, engines, gas turbines, compressors, and machine tools;
  2. Monitoring and maintenance strategies for critical components such as bearings, gears, and rotors;
  3. Emerging sensing and data collection techniques for equipment condition assessment;
  4. Artificial intelligence, machine learning, and deep learning applications in prognostics and maintenance;
  5. Signal processing and feature analysis methods for fault detection and performance tracking;
  6. Implementation of digital twin and cyber–physical systems in health management.

We invite researchers and practitioners to contribute their original work to this Special Issue.

Dr. Cai Yi
Dr. Qiuyang Zhou
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

  • intelligent fault prediction
  • machine condition monitoring
  • maintenance

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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

22 pages, 1708 KB  
Article
Few-Shot Fault Diagnosis of Rotating Machinery Using Complex Convolution and Disentangled Representation Learning
by Qiuyang Zhou, Xiaoyu Xian, Zhengyu Chen, Lei Yan, Yuming Fan and Kexin Yin
Machines 2026, 14(6), 655; https://doi.org/10.3390/machines14060655 (registering DOI) - 4 Jun 2026
Abstract
Few-shot fault diagnosis is a challenging task in rotating machinery health monitoring because only limited labeled fault samples are available in practical industrial scenarios. Under such conditions, deep learning models are prone to overfitting and may fail to extract stable fault-sensitive features from [...] Read more.
Few-shot fault diagnosis is a challenging task in rotating machinery health monitoring because only limited labeled fault samples are available in practical industrial scenarios. Under such conditions, deep learning models are prone to overfitting and may fail to extract stable fault-sensitive features from vibration signals. Moreover, the weak fault-related components are usually coupled with operating-condition variations, background vibration, and environmental noise, which further degrades the discriminability and generalization ability of diagnostic models. To address these problems, this paper proposes a complex-valued disentangled representation learning network for few-shot fault diagnosis of rotating machinery. First, a direction-pair complex augmentation strategy is developed for triaxial vibration measurements. Two directional vibration components are selected and organized as the real and imaginary branches of a complex-valued input, which increases sample diversity under few-shot conditions. Then, a lightweight complex-valued convolution block is designed to model the coupled dynamic characteristics between different vibration directions and extract fault-sensitive representations. Furthermore, a dual-branch disentangled representation structure is developed to decompose the learned features into fault-sensitive representations and condition-related interference representations. To enhance the separability of fault embeddings under limited samples, a cosine-based disentangled representation loss is introduced, which improves intra-class compactness and inter-class discrimination while suppressing irrelevant interference information. Finally, a few-shot diagnosis strategy is constructed to identify fault categories with only a small number of labeled samples. Experimental results demonstrate that the proposed method consistently outperforms representative methods in terms of diagnostic accuracy, feature separability, and robustness, especially under extremely limited labeled samples. Full article
(This article belongs to the Special Issue Intelligent Predictive Maintenance and Machine Condition Monitoring)
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26 pages, 5623 KB  
Article
A Domain Adaptation Method for Fault Diagnosis of Planetary Gearboxes Under Varying Operating Conditions with Time–Frequency Enhanced Attention
by Mingyu Shen, Lixiang Duan, Jiaqi Zhu, Shuang Cai and Stevan Dubljevic
Machines 2026, 14(6), 623; https://doi.org/10.3390/machines14060623 - 1 Jun 2026
Viewed by 165
Abstract
Deep learning-based methods have achieved promising results in planetary gearbox fault diagnosis. However, complex vibration signals often contain redundant information and disturbance-related responses, and varying operating conditions can cause distribution discrepancy between training and testing data, leading to degraded diagnostic performance. To address [...] Read more.
Deep learning-based methods have achieved promising results in planetary gearbox fault diagnosis. However, complex vibration signals often contain redundant information and disturbance-related responses, and varying operating conditions can cause distribution discrepancy between training and testing data, leading to degraded diagnostic performance. To address this coupled challenge, a diagnostic method termed DART18 (Domain Adaptation diagnosis of ResNet18 embedded with a Time–frequency enhanced attention mechanism) is proposed. DART18 is designed to improve both the discriminability and transferability of fault features by combining input-level time–frequency refinement with feature-level distribution alignment. Specifically, vibration signals are first transformed by the optimal generalized S-Transform (OGST) into time–frequency representations to characterize their joint time–frequency information. Then, TFEAM is designed to refine the input time–frequency representations before deep feature extraction. By aggregating features from different receptive fields and adaptively emphasizing fault-related time–frequency structures, TFEAM provides more informative inputs for subsequent feature learning. On this basis, ResNet18 is employed to extract fault features, and multi-kernel maximum mean discrepancy (MK-MMD) is introduced to statistically align the feature distributions of the source and target domains by jointly using labeled source-domain data and unlabeled target-domain data. Experimental results on two planetary gearbox datasets under multiple domain adaptation tasks show that DART18 consistently outperforms five comparative methods in terms of accuracy and F1-score, demonstrating its effectiveness and robustness for fault diagnosis under varying operating conditions. Full article
(This article belongs to the Special Issue Intelligent Predictive Maintenance and Machine Condition Monitoring)
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17 pages, 4987 KB  
Article
A Pre-Activated Residual Parallel Convolutional Block-Based BiGRU Model for Remaining Useful Life Prediction
by Yifan Sun, Qiuyang Zhou and Yu Xia
Machines 2026, 14(2), 159; https://doi.org/10.3390/machines14020159 - 30 Jan 2026
Viewed by 424
Abstract
The accurate prediction of the Remaining Useful Life (RUL) of key mechanical equipment in modern industry is crucial for reducing production risks and optimizing maintenance decisions. However, existing Convolutional Neural Network (CNN)-based models lack an inherent memory mechanism, and single convolutional kernel-based CNN [...] Read more.
The accurate prediction of the Remaining Useful Life (RUL) of key mechanical equipment in modern industry is crucial for reducing production risks and optimizing maintenance decisions. However, existing Convolutional Neural Network (CNN)-based models lack an inherent memory mechanism, and single convolutional kernel-based CNN models fail to capture multi-scale temporal features effectively. Moreover, some existing methods fail to account for the stability of the model training process, which tends to result in prolonged training time and an elevated risk of overfitting. To overcome these problems, a pre-activated residual parallel convolutional block-based BiGRU model (PRPC-BiGRU) is proposed in this study. First, the residual parallel convolutional block (RPCB) is constructed to simultaneously extract multi-scale temporal features. Subsequently, the pre-activated convolutional structure, which applies normalization and activation function prior to convolution operations, is utilized to improve gradient propagation and training stability. Finally, experimental results using the aero-engine benchmark datasets to verify the effectiveness and superior prediction performance of the proposed PRPC-BiGRU model. Full article
(This article belongs to the Special Issue Intelligent Predictive Maintenance and Machine Condition Monitoring)
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