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Aircrafts Reliability and Health Management Volume II
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Aircrafts Reliability and Health Management Volume II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 8065

Special Issue Editors

Dr. Behrooz Keshtegar

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Zabol, Zabol, Iran
Interests: structural reliability analysis; reliability-based design optimization; data driven-based modelling approaches and artificial intelligent
Special Issues, Collections and Topics in MDPI journals
Dr. Zhixin Zhan

E-Mail Website
Guest Editor
School of Aeronautical Science and Engineering, Beihang University, Beijing 100191, China
Interests: health monitoring; evaluation models and approaches for aircrafts and aero-engine; detection, modeling, and prediction methods of the crack; failure/fault; fatigue and lifetime of systems and components
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yunwen Feng

E-Mail Website
Guest Editor
School of Aeronautics, Northwestern Polytechnical University, Xi'an, China
Interests: aircrafts maintenance; reliability; safety and airworthiness
Special Issues, Collections and Topics in MDPI journals
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,

There have been rapid advances in aircraft technology in recent years, with breakthroughs in fields such as supersonic aircraft, civil aircraft, and advanced fighter jets. The performance and reliability requirements of such aircraft are high, requiring an increased focus on the reliability evaluation and health management of systems. The reliability analyses and health evaluations of aircraft systems are thus complex and inherently multidisciplinary processes that involve several disciplines ranging from aerodynamics, propulsion, structures, electric/hydraulic systems, and guidance to navigation and control. Each area of research incorporates different groups of highly experienced experts and advanced high-fidelity simulation models. Therefore, it is imperative that we develop new and efficient approaches to enable the performance of suitable health monitoring and reliability design analyses of advanced aircrafts. When it comes to these approaches and techniques, the development of a large range of mathematical algorithms will allow us to face up to the challenges induced by aerospace system design and simulation.

This Special Issue will focus on the current state of the art, latest advances in, and future trends of aircrafts reliability and health management. Our key objective is to improve the performance, reliability, safety, airworthiness, and maintainability of aircrafts and aero-engines by overcoming relevant key technical and scientific issues. The aircraft structures include fuselage covers, wing structure, undercarriage, aero-engine structures or components (i.e., aero-engine fan, compressor, combustor, turbine, blade, casings, and other components) and other related components found in aircraft systems. The topics of this Special Issue might include, but are not limited to, structural or system optimization, reliability evaluation analysis, fatigue analysis and design, lifetime analysis (or prediction) and design, faults monitoring and diagnosis, operation and maintenance, seaworthiness, safety evaluation, health management, etc. This Special Issue welcomes theoretical, analytical, and experimental investigations into and discussions of aircraft structures and systems. These contributions should advance the body of knowledge and its applications in structural/system health monitoring, system optimization and reliability design with respect to the structural dynamics, nonlinear vibrations, time series modeling techniques, strategies of aerospace systems, mathematical modeling methods, computer simulation technologies, reliability-based design optimization techniques, multidisciplinary optimization approaches, and other related algorithms and new applications in aircraft reliability and health management.

Potential topics include, but are not limited to:

  • Health monitoring technologies for aircraft flight condition;
  • Evaluation models and approaches of aero-engine operation status;
  • Detection, modeling and prediction methods of the crack, failure/fault, fatigue and lifetime of systems and components;
  • Optimization design methods for assembled structures (or multi-component systems), and a single structure (component) of aircraft;
  • The optimization strategy and algorithm, multidisciplinary simulation approach and surrogate modeling technique, in optimization and reliability design of structures and systems;
  • Advanced probabilistic analysis and design methods for aircraft structures including parameter influential analysis, sensitivity analysis, and reliability-based design optimization methods;
  • The applications of emerging technologies and algorithms in aircraft system analysis, such as big data analytics, cloud computing, intelligent algorithm, etc. In particular, we look into their applications in the analysis, simulation, and modeling for structural optimization and reliability design;
  • Decision support and simulation-based optimization and reliability design which contribute to improve the performance and reliability of aircraft and structures;
  • Maintenance/reliability/safety/airworthiness modeling and optimization based on new theory, method and technolog

Dr. Behrooz Keshtegar
Dr. Zhixin Zhan
Prof. Dr. Yunwen Feng
Prof. Dr. Cheng-Wei Fei
Guest Editors

Manuscript Submission Information

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Published Papers (4 papers)

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Research

16 pages, 3505 KiB  
Article
A Novel Remaining Useful Life Probability Prediction Approach for Aero-Engine with Improved Bayesian Uncertainty Estimation Based on Degradation Data
by Yanyan Hu, Yating Bai, En Fu and Pengpeng Liu
Appl. Sci. 2023, 13(16), 9194; https://doi.org/10.3390/app13169194 - 12 Aug 2023
Viewed by 1647
Abstract
As the heart of aircraft, the aero-engine is not only the main power source for aircraft flight but also an essential guarantee for the safe flight of aircraft. Therefore, it is of great significance to find effective methods for remaining useful life (RUL) [...] Read more.
As the heart of aircraft, the aero-engine is not only the main power source for aircraft flight but also an essential guarantee for the safe flight of aircraft. Therefore, it is of great significance to find effective methods for remaining useful life (RUL) prediction for aero-engines in order to avoid accidents and reduce maintenance costs. With the development of deep learning, data-driven approaches show great potential in dealing with the above problem. Although many attempts have been made, few works consider the error of the point prediction result caused by uncertainties. In this paper, we propose a novel RUL probability prediction approach for aero-engines with prediction uncertainties fully considered. Before forecasting, a principal component analysis (PCA) is first utilized to cut down the dimension of sensor data and extract the correlation between multivariate data to reduce the network computation. Then, a multi-layer bidirectional gate recurrent unit (BiGRU) is constructed to predict the RUL of the aero-engine, while prediction uncertainties are quantized by the improved variational Bayesian inference (IVBI) with a Gaussian mixture distribution. The proposed method can give not only the point prediction of RUL but also the confidence interval of the prediction result, which is very helpful for real-world applications. Finally, the experimental study illustrates that the proposed method is feasible and superior to several other comparative models. Full article
(This article belongs to the Special Issue Aircrafts Reliability and Health Management Volume II)
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12 pages, 3038 KiB  
Article
A Study on the Movement Characteristics of Fuel in the Fuel Tank during the Maneuvering Process
by Yameng Wang, Chenran Ruan, Song Lu and Zhenghong Li
Appl. Sci. 2023, 13(15), 8636; https://doi.org/10.3390/app13158636 - 26 Jul 2023
Cited by 1 | Viewed by 1593
Abstract
To investigate the movement characteristics of fuel within an aircraft’s fuel tank during maneuvering, this study employs numerical simulations using the Finite Pointset Method (FPM) with specific boundary conditions and excitation. The simulation results successfully capture the complex phenomena of fuel free surface [...] Read more.
To investigate the movement characteristics of fuel within an aircraft’s fuel tank during maneuvering, this study employs numerical simulations using the Finite Pointset Method (FPM) with specific boundary conditions and excitation. The simulation results successfully capture the complex phenomena of fuel free surface fluctuations, surging, rolling, and breaking, along with the corresponding changes in the center of gravity induced by fuel movement. Throughout the aircraft’s maneuvering process, the longitudinal center of gravity experiences minimal variations, whereas the aerodynamic center of gravity exhibits significant fluctuations, reaching a range of 0.47 m. The longitudinal center of gravity consistently shifts backward with a displacement of 0.41 m. The maximum height attained by the liquid’s free surface during aircraft maneuvering measures approximately 0.22 m, reaching the upper panel’s height of the wing fuel tank, resulting in a certain level of impact on the upper panel. The maximum impact force generated during the aircraft maneuvering process amounts to 2.3 × 105 pascals, with the point of action located at the junction of the transverse frame and the upper panel. The findings of this work provide support for the safe design of aircraft fuel tank systems. Full article
(This article belongs to the Special Issue Aircrafts Reliability and Health Management Volume II)
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11 pages, 7602 KiB  
Article
Research on Oxygenation Components under a High-Pressure Oxygen Environment
by Haowen Yang and Dongsheng Jiang
Appl. Sci. 2023, 13(13), 7703; https://doi.org/10.3390/app13137703 - 29 Jun 2023
Cited by 1 | Viewed by 1223
Abstract
Oxygen nozzles play an important role in aircraft oxygen systems, and the oxygen valve is one of the most important components of the oxygen nozzle. In order to investigate the ignition source of three fires of a certain type of aircraft, combustion analysis, [...] Read more.
Oxygen nozzles play an important role in aircraft oxygen systems, and the oxygen valve is one of the most important components of the oxygen nozzle. In order to investigate the ignition source of three fires of a certain type of aircraft, combustion analysis, energy spectrum analysis, and material analysis were conducted. Based on the results of the analysis, oxygen impact tests were carried out under different conditions to identify the superior material and to calculate the lifetime of a PA1010 oxygen valve. The test platform was constructed at the National Key Experimental Base of Fire Science. The PA1010 oxygen nozzle and the F3 oxygen nozzle were the main test subjects. The test results show that the lifetime of the PA1010 oxygen valve under high-pressure oxygen was about 2532, and F3 was the safer material for the oxygen valve. This research provides a reference for the safety design of oxygen valves under high-pressure environments and facilitates further research into aircraft oxygen systems. Full article
(This article belongs to the Special Issue Aircrafts Reliability and Health Management Volume II)
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17 pages, 3210 KiB  
Article
Remaining Useful Life Prediction of Aircraft Turbofan Engine Based on Random Forest Feature Selection and Multi-Layer Perceptron
by Hairui Wang, Dongwen Li, Dongjun Li, Cuiqin Liu, Xiuqi Yang and Guifu Zhu
Appl. Sci. 2023, 13(12), 7186; https://doi.org/10.3390/app13127186 - 15 Jun 2023
Cited by 19 | Viewed by 3108
Abstract
The accurate prediction of the remaining useful life (RUL) of aircraft engines is crucial for improving engine safety and reducing maintenance costs. To tackle the complex issues of nonlinearity, high dimensionality, and difficult-to-model degradation processes in aircraft engine monitoring parameters, a new method [...] Read more.
The accurate prediction of the remaining useful life (RUL) of aircraft engines is crucial for improving engine safety and reducing maintenance costs. To tackle the complex issues of nonlinearity, high dimensionality, and difficult-to-model degradation processes in aircraft engine monitoring parameters, a new method for predicting the RUL of aircraft engines based on the random forest algorithm and a Bayes-optimized multilayer perceptron (MLP) was proposed here. First, the random forest algorithm was used to evaluate the importance of historical monitoring parameters of the engine, selecting the key features that significantly impact the engine’s lifetime operation cycle. Then, the single exponent smoothing (SES) algorithm was introduced for smoothing the extracted features to reduce the interference of original noise. Next, an MLP-based RUL prediction model was established using a neural network. The Bayes’ online parameter updating formula was used to solve the objective function and return the optimal parameters of the MLP training model and the minimum value of the evaluation index RMSE. Finally, the probability density function of the predicted RUL value of the aircraft engine was calculated to obtain the RUL prediction results.The effectiveness of the proposed method was verified and analyzed using the C-MAPSS dataset for turbofan engines. Experimental results show that, compared with several other methods, the RMSE of the proposed method in the FD001 test set decreases by 6.1%, demonstrating that the method can effectively improve the accuracy of RUL prediction for aircraft engines. Full article
(This article belongs to the Special Issue Aircrafts Reliability and Health Management Volume II)
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