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Aerospace
Special Issues
State Monitoring and Health Management of Complex Equipment (2nd Edition)
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State Monitoring and Health Management of Complex Equipment (2nd Edition)

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6476

Special Issue Editor

Prof. Dr. Cheng-Wei Fei

E-Mail Website
Guest Editor
Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China
Interests: aircraft design and optimization; model updating; probabilistic modeling; structural health monitoring; structural reliability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of complex industrial equipment combines mechanical, electronics, materials, and other interdisciplinary studies. The state monitoring and health management of complex equipment in aerospace, high-speed rail systems, and other industrial sectors are becoming increasingly complex. The difficulties of state monitoring and health management are not only due to the complexity of equipment but also to the integration of modeling techniques, mathematical algorithms, and maintenance policies. Therefore, the development of advanced state monitoring methods, prediction methods, and health assessment technology in industry would result in substantial benefits. This Special Issue is intended to collect state-of-the-art and future trends in state monitoring and health management methods in complex industrial equipment. Moreover, the potential objective of this Special Issue is to improve the reliability, safety, economy, and maintainability of complex equipment. Topics include papers on, but not limited to, reliability analysis, reliability optimization, failure prediction, signal processing and fault diagnosis, faults/state monitoring, remaining useful life estimation, health assessment, maintenance decision optimization, etc. This Special Issue welcomes papers on theoretical, analytical, technical, engineering, and experimental investigations of complex equipment. The contributions from this Special Issue will improve structural/system reliability analysis techniques, model-based and data-driven modeling methods, computer simulation technologies, reliability-based design optimization techniques, maintenance police optimization techniques, and other related interdisciplinary techniques in complex equipment reliability and health management.

Potential topics include, but are not limited to, the following:

  • Structural/system state monitoring;
  • Reliability evaluation and prediction;
  • Reliability-based design optimization;
  • Advanced signal processing, fault diagnosis, and fault monitoring methods;
  • Model-based and data-driven detection for state monitoring and health management;
  • Modeling and simulation methods for estimating the remaining useful life of complex systems or components;
  • Health monitoring technologies;
  • Machine learning and deep learning models for complex equipment health assessment;
  • Maintenance and policy optimization for complex equipment;
  • Performance estimation and prediction of complex equipment.

Prof. Dr. Cheng-Wei Fei
Guest Editor

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. Aerospace 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

  • complex equipment
  • state monitoring
  • health management
  • modeling techniques
  • fault diagnosis and prediction
  • operation and maintenance

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Related Special Issue

Published Papers (6 papers)

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Research

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14 pages, 8735 KiB  
Article
Knowledge Graph Construction Method for Commercial Aircraft Fault Diagnosis Based on Logic Diagram Model
by Huanchun Peng and Weidong Yang
Aerospace 2024, 11(9), 773; https://doi.org/10.3390/aerospace11090773 - 20 Sep 2024
Viewed by 733
Abstract
Commercial aircraft fault diagnosis is an important means to ensure the reliability and safety of commercial aircraft. Traditional knowledge-driven and data-driven fault diagnosis methods lack interpretability in engineering mechanisms, making them difficult to promote and apply. To address the issue of lack of [...] Read more.
Commercial aircraft fault diagnosis is an important means to ensure the reliability and safety of commercial aircraft. Traditional knowledge-driven and data-driven fault diagnosis methods lack interpretability in engineering mechanisms, making them difficult to promote and apply. To address the issue of lack of interpretability, this paper conducts a fault knowledge graph for commercial aircraft fault diagnosis, using the fault logic in the logic diagram to increase the interpretability of diagnostic work. Firstly, to avoid the inefficiency of logic diagram applications, an executable logic diagram model is established, which can perform mathematical analysis and achieve fault diagnosis and localization using operational data as input. Then, the logic diagram is sorted out to obtain the hidden fault knowledge in the logic diagram, which is used to construct a fault knowledge graph to help achieve cause localization and rapid troubleshooting. The methods proposed in this paper are all validated through case studies of abnormal low-pressure faults in domestic commercial aircraft hydraulic systems. The results show that the logic diagram model can perform model simulation and fault diagnosis based on operational data, and the fault knowledge graph can quickly locate abnormal monitoring parameters and guide troubleshooting work based on existing information. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment (2nd Edition))
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19 pages, 6203 KiB  
Article
MobGSim-YOLO: Mobile Device Terminal-Based Crack Hole Detection Model for Aero-Engine Blades
by Xinyao Hou, Hao Zeng, Lu Jia, Jingbo Peng and Weixuan Wang
Aerospace 2024, 11(8), 676; https://doi.org/10.3390/aerospace11080676 - 16 Aug 2024
Viewed by 728
Abstract
Hole detection is an important means of crack detection for aero-engine blades, and the current technology still mainly relies on manual operation, which may cause safety hazards for visual reasons. To address this problem, this paper proposes a deep learning-based, aero-engine blade crack [...] Read more.
Hole detection is an important means of crack detection for aero-engine blades, and the current technology still mainly relies on manual operation, which may cause safety hazards for visual reasons. To address this problem, this paper proposes a deep learning-based, aero-engine blade crack detection model. First, the K-means++ algorithm is used to recalculate the anchor points, which reduces the influence of the anchor frame on the accuracy; second, the backbone network of YOLOv5s is replaced with Mobilenetv3 for a lightweight design; then, the slim-neck module is embedded into the neck part, and the activation function is replaced with Hard Sigmoid for redesign, which improves the accuracy and the convergence speed. Finally, in order to improve the learning ability for small targets, the SimAM attention mechanism is embedded in the head. A large number of ablation tests are conducted in real engine blade data, and the results show that the average precision of the improved model is 93.1%, which is 29.3% higher; the number of parameters of the model is 12.58 MB, which is 52.96% less, and the Frames Per Second (FPS) can be up to 95. The proposed algorithm meets the practical needs and is suitable for hole detection. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment (2nd Edition))
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19 pages, 10377 KiB  
Article
Fatigue Life Prediction for 2060 Aluminium–Lithium Alloy with Impact Damage
by Lei Li, Xiongfei Li, Zhixin Zhan, Weiping Hu and Qingchun Meng
Aerospace 2024, 11(7), 536; https://doi.org/10.3390/aerospace11070536 - 29 Jun 2024
Cited by 1 | Viewed by 810
Abstract
The paper investigates the issue of post-impact fatigue damage of the 2060 aluminium–lithium alloy, a representative material of third-generation aluminium–lithium alloys extensively employed in the fuselage of C919 aircraft due to its notable attributes of high specific stiffness and strength. Initial impact damage [...] Read more.
The paper investigates the issue of post-impact fatigue damage of the 2060 aluminium–lithium alloy, a representative material of third-generation aluminium–lithium alloys extensively employed in the fuselage of C919 aircraft due to its notable attributes of high specific stiffness and strength. Initial impact damage is identified utilizing a residual stress–strain field obtained from a quasi-static simulation. Then, the continuum damage mechanics approach is applied to predict the fatigue life of the impacted 2060 aluminium–lithium alloy plates accounting for the combined effects of residual stress, plastic damage, and fatigue loading. A comparative analysis between calculated and experimental results is conducted to validate the efficacy of the proposed methodology. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment (2nd Edition))
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22 pages, 3818 KiB  
Article
Precise Modeling and Analysis of Aviation Power System Reliability via the Aviation Power System Reliability Probability Network Model
by Yao Wang, Fengtao Wang, Shujuan Li and Yongjie Zhang
Aerospace 2024, 11(7), 530; https://doi.org/10.3390/aerospace11070530 - 28 Jun 2024
Viewed by 1032
Abstract
This study addresses the challenges of accurately analyzing the reliability of aviation power systems (APS) using traditional models by introducing the Aviation Power System Reliability Probability Network Model (APS-RPNM). The model directly transforms the system architecture into an equivalent probability network, aiming to [...] Read more.
This study addresses the challenges of accurately analyzing the reliability of aviation power systems (APS) using traditional models by introducing the Aviation Power System Reliability Probability Network Model (APS-RPNM). The model directly transforms the system architecture into an equivalent probability network, aiming to develop a precise reliability model that captures system functions and fault logic. By classifying APS components into five distinct structural patterns and mapping them to corresponding nodes in the APS-RPNM, the model is successfully constructed. Specifically, None-Input-to-Multiple-Output components are transformed into two-state nodes, while Multiple-Input-to-None-Output, Single-Input-to-Multiple-Output, and Multiple-Input-to-Single-Output components are mapped to three-state nodes. For Multiple-Input-to-Multiple-Output components, a novel approach employing multiple two-state sub-nodes is adopted to capture their complex functional logic. A case study comparing the performance of the APS-RPNM with the traditional minimal path set method in reliability analysis was conducted. The results demonstrate that the APS-RPNM not only simplifies the model construction process and eliminates errors stemming from subjective engineering judgments but also enables the efficient computation of power supply reliability for all load points in a single inference by integrating all of the components. This significantly improves computational efficiency and system dependency analysis capabilities, highlighting the APS-RPNM’s tremendous potential in optimizing the reliability design of APS. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment (2nd Edition))
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14 pages, 2769 KiB  
Article
A Joint Surface Contact Stiffness Model Considering Micro-Asperity Interaction
by Tian Xia, Jie Qu and Yong Liu
Aerospace 2024, 11(6), 472; https://doi.org/10.3390/aerospace11060472 - 12 Jun 2024
Viewed by 864
Abstract
Mechanical joint interfaces are widely found in mechanical equipment, and their contact stiffness directly affects the overall performance of the mechanical system. Based on the fractal and elastoplastic contact mechanics theories, the K-E elastoplastic contact model is introduced to establish the contact stiffness [...] Read more.
Mechanical joint interfaces are widely found in mechanical equipment, and their contact stiffness directly affects the overall performance of the mechanical system. Based on the fractal and elastoplastic contact mechanics theories, the K-E elastoplastic contact model is introduced to establish the contact stiffness model for mechanical joint interfaces. This model considers the interaction effects between micro-asperities in the fully deformed state, including elasticity, first elastoplasticity, second elastoplasticity, and complete plastic deformation state. Based on this model, the effects of fractal parameters on normal contact stiffness and contact load are analyzed. It can be found that the larger fractal dimension D or smaller characteristic scale coefficient G will weaken the interaction between micro-asperities. The smoother processing surfaces lead to higher contact stiffness in mechanical joint interfaces. The applicability and effectiveness of the proposed model are verified by comparing it with the traditional contact model calculation results. Under the same load, the interaction between micro-rough surfaces leads to an increase in both overall deformation and contact stiffness. The accuracy of the predicted contact stiffness model is also validated by comparing it with experimental results. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment (2nd Edition))
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Review

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31 pages, 5456 KiB  
Review
A Survey of Aero-Engine Blade Modeling and Dynamic Characteristics Analyses
by Yaqiong Zhang, Fubin Wang, Jinchao Liu, Heng Zhao, Chao Fu, Weihao Zhai and Kuan Lu
Aerospace 2024, 11(8), 638; https://doi.org/10.3390/aerospace11080638 - 5 Aug 2024
Viewed by 1561
Abstract
The rotating blade is a key component of an aero-engine, and its vibration characteristics have an important impact on the performance of the engine and are vital for condition monitoring. This paper reviews the research progress of blade dynamics, including three main aspects: [...] Read more.
The rotating blade is a key component of an aero-engine, and its vibration characteristics have an important impact on the performance of the engine and are vital for condition monitoring. This paper reviews the research progress of blade dynamics, including three main aspects: modeling of blades, solution methods, and vibration characteristics. Firstly, three popular structural dynamics models for blades are reviewed, namely lumped-mass model, finite element model, and semi-analytical model. Then, the solution methods for the blade dynamics are comprehensively described. The advantages and limitations of these methods are summarized. In the third part, this review summarizes the properties of the modal and vibration responses of aero-engine blades and discusses the typical forms and mechanisms of blade vibration. Finally, the deficiencies and limitations in the current research on blade modeling and vibration analysis are summarized, and the directions for future efforts are pointed out. The purpose of this review is to provide meaningful insights to researchers and engineers in the field of aero-engine blade modeling and dynamic characteristics analysis. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment (2nd Edition))
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