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Machines
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Trustworthy and Intelligent Systems for Machine Health Monitoring and Predictive...
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Trustworthy and Intelligent Systems for Machine Health Monitoring and Predictive Maintenance

A Special Issue of Machines (ISSN 2075-1702) belonging to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2107

Editors

Dr. Tongtong Yan

E-Mail Website
Guest Editor
Department of Civil Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Interests: machine health monitoring; performance degradation modeling; health index construction; battery health management
Dr. Yaoxiang Yu

E-Mail Website
Guest Editor
Mechanical Engineering Department, Tsinghua University, Beijing, China
Interests: dynamic analysis; condition monitoring; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Yankai Wang

E-Mail Website
Guest Editor
School of Software, Tsinghua University, Beijing, China
Interests: complex equipment digital intelligence systems; industrial data intelligence software systems; edge artificial intelligence; optimal control integrating mechanism-based intelligent learning; AI agents

Special Issue Information

Dear Colleagues,

In modern industrial systems, the continuous monitoring of machinery health is crucial for implementing predictive maintenance strategies. Real-time condition monitoring not only facilitates the early detection of potential faults but also helps avoid unnecessary maintenance activities, thereby sustaining optimal system performance.

Within condition-based maintenance, both diagnosis and prognosis are integral and complementary processes. Diagnosis involves assessing the historical and current health status of machinery using monitored signal data. At the same time, prognosis focuses on predicting the remaining useful life based on past and ongoing operational profiles.

Various methodologies have been developed for diagnosing and prognosing machinery health. Among these, data-driven approaches leveraging machine learning and deep learning techniques have gained significant attention in recent years. These methods are often considered more adaptable and scalable alternatives to physics-based models, which may struggle to incorporate real-time updates of health data. Despite the promise of data-driven approaches in uncovering correlations between operational data and equipment health, reliably detecting incipient faults and forecasting future machine conditions in a trustworthy and interpretable manner remains a significant challenge. Thus, these areas continue to represent key research challenges in machinery health management.

This Special Issue invites contributions from both academic researchers and industry practitioners. It seeks to showcase the latest theoretical advances and practical applications in trustworthy data-driven health monitoring for intelligent machinery. We welcome the submission of experimental studies as well as theoretical papers, with the expectation that the latter offer novel insights and feasible solutions to relevant industrial problems. Potential topics include, but are not limited to:

  • Dynamics modelling and simulation of machines;
  • Data cleaning and data quality improvement;
  • Condition monitoring and health assessment;
  • Signal processing and fault feature extraction;
  • Fault detection and quantitative analysis;
  • Data-driven intelligent fault diagnosis and prognosis;
  • Vibration analysis of components of machines;
  • Big model for general prognostics and health management.

Dr. Tongtong Yan
Dr. Yaoxiang Yu
Dr. Yankai Wang
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-anonymized 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

  • dynamics modelling and simulation
  • machine health monitoring
  • fault diagnosis in deep learning
  • performance degradation assessment
  • big model

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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

30 pages, 11935 KB  
Article
DDGF-Net: A Novel Dual-Domain Generative-Discriminative Fusion Network for Gearbox Fault Diagnosis Under Strong Noise Conditions
by Ruihan Ma, Xuanyue Wang, Jiajun Cheng, Tao Xie, Shuo Li and Chaoge Wang
Machines 2026, 14(9), 1013; https://doi.org/10.3390/machines14091013 (registering DOI) - 5 Sep 2026
Abstract
In industrial scenarios, strong background noise can easily overwhelm the weak impulsive signatures of gearbox faults, leading to time-domain waveform distortion and frequency-domain spectral aliasing, which in turn degrades the feature extraction capability of conventional diagnostic models and significantly reduces their diagnostic accuracy [...] Read more.
In industrial scenarios, strong background noise can easily overwhelm the weak impulsive signatures of gearbox faults, leading to time-domain waveform distortion and frequency-domain spectral aliasing, which in turn degrades the feature extraction capability of conventional diagnostic models and significantly reduces their diagnostic accuracy and robustness. To overcome this limitation, a dual-domain generative–discriminative fusion network (DDGF-Net) is proposed for robust gearbox fault diagnosis under strong noise interference. The proposed framework consists of three collaborative components. Firstly, an improved conditional variational autoencoder (CVAE) integrating soft-threshold shrinkage and spectral consistency constraints is designed to perform joint time–frequency denoising and signal reconstruction, thereby preserving subtle fault characteristics while effectively suppressing noise. Secondly, parallel time-domain and frequency-domain encoding branches are constructed to extract transient fault impulses and fault characteristic frequencies, respectively, compensating for the inadequacy of single-domain feature representations. Thirdly, a lightweight bidirectional cross-attention mechanism is introduced to overcome the limitations of conventional fixed-weight fusion strategies, enabling dynamic adjustment of the interaction weights between dual-domain features according to the instantaneous noise intensity, thus maximizing the complementary value of cross-domain features. Extensive comparative experiments conducted on the Southeast University(SEU) gearbox dataset demonstrate that DDGF-Net achieves superior diagnostic performance and noise robustness over eight representative methods, particularly under strong noise interference, thereby validating its effectiveness and superiority in harsh diagnostic scenarios. Full article
(This article belongs to the Special Issue Trustworthy and Intelligent Systems for Machine Health Monitoring and Predictive Maintenance)
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28 pages, 9805 KB  
Article
Multi-Feature Relational Modeling and Conditional-Memory-Augmented Anomaly Detection for Multi-Cylinder Diesel Engines Under Variable Operating Conditions
by Yue Gao, Bingjie Ma, Hangfeng Mo, Tao Tao, Zhinong Jiang and Zhiwei Mao
Machines 2026, 14(8), 914; https://doi.org/10.3390/machines14080914 - 9 Aug 2026
Viewed by 306
Abstract
When multi-cylinder diesel engines operate under variable speed and load conditions, the distribution of normal vibration responses drifts with the operating conditions. This makes it difficult for diagnostic models to distinguish normal operating-condition fluctuations from genuine fault deviations, leading to false alarms or [...] Read more.
When multi-cylinder diesel engines operate under variable speed and load conditions, the distribution of normal vibration responses drifts with the operating conditions. This makes it difficult for diagnostic models to distinguish normal operating-condition fluctuations from genuine fault deviations, leading to false alarms or missed detections. Meanwhile, fault samples are usually limited in practical applications. To address these problems, this study proposes an anomaly detection method based on multi-feature relational modeling and conditional-memory augmentation. The method performs the cycle-wise alignment of multi-point vibration signals according to the firing phase of each cylinder. It integrates local waveform morphology, impact energy, and energy-centroid information in the non-uniform angular domain to construct a raw–relative dual relational representation. It further uses speed conditions to modulate latent features and employs a sparse normal memory to constrain reconstruction sources, enabling the model to learn normal relational patterns under different operating conditions using only normal samples. Tests involving misfire, intake-valve clearance anomaly, and exhaust-valve clearance anomaly were conducted on a TBD234V12 diesel-engine test bench. The proposed method achieved an accuracy, true positive rate (TPR), F1-score, and area under the receiver operating characteristic curve (AUROC) of 97.44%, 98.98%, 98.30%, and 98.88%, respectively, with a false-positive rate (FPR) of 7.21% under the sample-level alarm definition. The results show that the method reduces the interference of operating-condition-induced normal-pattern drift with anomaly determination and improves the accuracy of fault warning within the range of the operating conditions covered in this study. Full article
(This article belongs to the Special Issue Trustworthy and Intelligent Systems for Machine Health Monitoring and Predictive Maintenance)
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15 pages, 2629 KB  
Article
Temporal Domain Vibration Fault Diagnosis of Drone Blades via Selective Embedding
by Mert Sehri, Tongtong Yan, Sumika Chauhan and Govind Vashishtha
Machines 2026, 14(2), 241; https://doi.org/10.3390/machines14020241 - 20 Feb 2026
Viewed by 943
Abstract
Rotor blades are the primary cause of drone failure. To assess the health status of drone blades, vibration monitoring is required; however, this is challenging due to noisy signals and limited labeled datasets. This study investigates a data loading strategy called selective embedding [...] Read more.
Rotor blades are the primary cause of drone failure. To assess the health status of drone blades, vibration monitoring is required; however, this is challenging due to noisy signals and limited labeled datasets. This study investigates a data loading strategy called selective embedding (SE), which is shown to improve data diagnosis across engineering fields. The hypothesis is that this strategy can improve the classification accuracy of drone blade conditions with multi-axis vibration data. Accelerometer signals are collected under different blade health conditions; the signals are then processed and fed into a deep learning model for multi class condition classification. An ablation study is conducted with different data loading strategies, including traditional single channel, parallel channel, and SE. The results show that SE improves classification accuracy, reduces performance variance, and achieves higher generalization performance across multiple blade fault conditions. These improvements are observed consistently across domain evaluations, where traditional data loading strategies have difficulty generalizing to unseen temporal segments. The findings demonstrate that SE can effectively support vibration fault diagnostics for aerospace applications, offering a reliable way to improve safety in drone monitoring. Full article
(This article belongs to the Special Issue Trustworthy and Intelligent Systems for Machine Health Monitoring and Predictive Maintenance)
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