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Machines
Special Issues
Physical-Informed Fault Monitoring and Fault-Tolerant Control of Industrial System
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Physical-Informed Fault Monitoring and Fault-Tolerant Control of Industrial System

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 366

Special Issue Editors

Dr. Qing Li

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: fault diagnosis and condition monitoring of electromechanical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Yu Shao

E-Mail Website
Guest Editor
Automatic Control and Complex Systems (AKS), University of Duisburg-Essen, 47057 Duisburg, Germany
Interests: fault-tolerant control; adaptive control and identification of network systems
Dr. Xiang Chen

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, China
Interests: condition monitoring of complex systems; fault diagnosis of electromechanical systems

Special Issue Information

Dear Colleagues,

Owing to the rapid development of Internet of Things (IOT) and sensors networks in industrial enginnering, prognostic and health management (PHM) schemes have been widely adopted in next generation of industrial systems such as aircraft engines, energies, high speed train, vehicles, and other heavy industries areas. In particular, physics-informed fault monitoring and fault-tolerant control has emerged as a promising technique for condition monitoring of complex industrial system, which combines the superiorities of degradation mechanism, models interpretability and performance reliability even in the event of system failure through fusing domain knowledge and degraded information of system. This special issue aims at addressing and reporting the recent advances and new breakthrough in physical-informed fault monitoring and tolerance control of industrial system. Authors are encouraged to submit research papers or practical applications. Areas to be covered in this Research Topic may include, but are not limited to:

  • Theoretical modeling for fault monitoring & tolerance control
  • Physics-informed deep learning for fault diagnosis & classification
  • Physics-informed deep learning for failure prognosis & life-time prediction
  • Active fault-tolerant control of industrial system
  • Passive fault-tolerant control of industrial system
  • Implementation of physics-informed modeling in embedded environment for IoT&PHM applications
  • Self-healing, evaluation, validation & invitigation of physics-informed models/algorithms
  • Fault compensation, redundant decision and adaptive control to industrial systems
  • Applications of physical-informed fault monitoring to various industrial systems/domains
  • Uncertainty and stability assessment in physical-informed models

Dr. Qing Li
Dr. Yu Shao
Dr. Xiang Chen
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

  • theoretical modeling for fault monitoring and tolerance control
  • physics-informed deep learning for fault diagnosis and classification
  • physics-informed deep learning for failure prognosis and life-time prediction
  • active fault-tolerant control of industrial system
  • passive fault-tolerant control of industrial system
  • implementation of physics-informed modeling in embedded environment for IoT and PHM applications
  • self-healing, evaluation, validation, and investigation of physics-informed models/algorithms
  • fault compensation, redundant decision, and adaptive control in industrial systems
  • applications of physical-informed fault monitoring in various industrial systems/domains
  • uncertainty and stability assessments in physical-informed models

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Published Papers (2 papers)

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Research

23 pages, 5359 KiB  
Article
Relationship Analysis Between Helicopter Gearbox Bearing Condition Indicators and Oil Temperature Through Dynamic ARDL and Wavelet Coherence Techniques
by Lotfi Saidi, Eric Bechhofer and Mohamed Benbouzid
Machines 2025, 13(8), 645; https://doi.org/10.3390/machines13080645 - 24 Jul 2025
Abstract
This study investigates the dynamic relationship between bearing gearbox condition indicators (BGCIs) and the lubrication oil temperature within the framework of health and usage monitoring system (HUMS) applications. Using the dynamic autoregressive distributed lag (DARDL) simulation model, we quantified both the short- and [...] Read more.
This study investigates the dynamic relationship between bearing gearbox condition indicators (BGCIs) and the lubrication oil temperature within the framework of health and usage monitoring system (HUMS) applications. Using the dynamic autoregressive distributed lag (DARDL) simulation model, we quantified both the short- and long-term responses of condition indicators to shocks in oil temperature, offering a robust framework for a counterfactual analysis. To complement the time-domain perspective, we applied a wavelet coherence analysis (WCA) to explore time–frequency co-movements and phase relationships between the condition indicators under varying operational regimes. The DARDL results revealed that the ball energy, cage energy, and inner and outer race indicators significantly increased in response to the oil temperature in the long run. The WCA results further confirmed the positive association between oil temperature and the condition indicators under examination, aligning with the DARDL estimations. The DARDL model revealed that the ball energy and the inner race energy have statistically significant long-term effects on the oil temperature, with p-values < 0.01. The adjusted R2 of 0.785 and the root mean square error (MSE) of 0.008 confirm the model’s robustness. The wavelet coherence analysis showed strong time–frequency correlations, especially in the 8–16 scale range, while the frequency-domain causality (FDC) tests confirmed a bidirectional influence between the oil temperature and several condition indicators. The FDC analysis showed that the oil temperature significantly affected the BGCIs, with evidence of feedback effects, suggesting a mutual dependency. These findings contribute to the advancement of predictive maintenance frameworks in HUMSs by providing practical insights for enhancing system reliability and optimizing maintenance schedules. The integration of dynamic econometric approaches demonstrates a robust methodology for monitoring critical mechanical components and encourages further research in broader aerospace and industrial contexts. Full article
(This article belongs to the Special Issue Physical-Informed Fault Monitoring and Fault-Tolerant Control of Industrial System)
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38 pages, 5575 KiB  
Article
Explainable Data Mining Framework of Identifying Root Causes of Rocket Engine Anomalies Based on Knowledge and Physics-Informed Feature Selection
by Xiaopu Zhang, Wubing Miao and Guodong Liu
Machines 2025, 13(8), 640; https://doi.org/10.3390/machines13080640 - 23 Jul 2025
Viewed by 111
Abstract
Liquid rocket engines occasionally experience abnormal phenomena with unclear mechanisms, causing difficulty in design improvements. To address the above issue, a data mining method that combines ante hoc explainability, post hoc explainability, and prediction accuracy is proposed. For ante hoc explainability, a feature [...] Read more.
Liquid rocket engines occasionally experience abnormal phenomena with unclear mechanisms, causing difficulty in design improvements. To address the above issue, a data mining method that combines ante hoc explainability, post hoc explainability, and prediction accuracy is proposed. For ante hoc explainability, a feature selection method driven by data, models, and domain knowledge is established. Global sensitivity analysis of a physical model combined with expert knowledge and data correlation is utilized to establish the correlations between different types of parameters. Then a two-stage optimization approach is proposed to obtain the best feature subset and train the prediction model. For the post hoc explainability, the partial dependence plot (PDP) and SHapley Additive exPlanations (SHAP) analysis are used to discover complex patterns between input features and the dependent variable. The effectiveness of the hybrid feature selection method and its applicability under different noise combinations are validated using synthesized data from a high-fidelity simulation model of a pressurization system. Then the analysis of the causes of a large vibration phenomenon in an active engine shows that the prediction model has good accuracy, and the feature selection results have a clear mechanism and align with domain knowledge, providing both accuracy and interpretability. The proposed method shows significant potential for data mining in complex aerospace products. Full article
(This article belongs to the Special Issue Physical-Informed Fault Monitoring and Fault-Tolerant Control of Industrial System)
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