State Monitoring and Health Management of Complex Equipment

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 24233

Special Issue Editor


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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 involves a combination of mechanical, electronics, materials, and other interdisciplinary studies, and 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 faced in state monitoring and health management are not only due to the complexity of equipment but also the integration of modeling techniques, mathematical algorithms, and maintenance polices. Therefore, the development of advanced state monitoring methods, prediction methods, and health assessment technologies in industry would result in substantial benefits. We plan to launch this Special Issue on Aerospace with the intention of discussing state-of-the-art and future trends in state monitoring and health management methods for complex industrial equipment. The objective of this Special Issue is to improve the reliability, safety, economy and maintainability of complex equipment. Topics of interest include, but not limited to, reliability analysis, reliability optimization, failure prediction, signal processing and fault diagnosis, fault/state monitoring, remaining useful life estimation, health assessment, and maintenance decision optimization. This Special Issue welcomes the submissions describing theoretical, analytical, technical, engineering, and experimental investigations of complex equipment. Through its contributions, this Special Issue aims to drive further improvements in 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 of interest include, but are not limited to structural/system state monitoring; reliability evaluation and prediction; reliability-based design optimization; advanced signal processing, fault diagnosis, and faults monitoring methods; model-based and data-driven detection for state monitoring and health management; modeling and simulation methods on remaining useful life estimates of complex systems or components; health monitoring technologies; machine learning, deep learning models for complex equipment health assessment; maintenance policy optimization for complex equipment; performance estimation and prediction for complex equipment.

Prof. Dr. Cheng-Wei Fei
Guest Editor

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Keywords

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

Published Papers (8 papers)

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Research

14 pages, 3522 KiB  
Article
An Analysis of the Vibration Characteristics of an Aviation Hydraulic Pipeline with a Clamp
by Yong Liu, Jinting Wei, Hao Du, Zhenpeng He and Fangchao Yan
Aerospace 2023, 10(10), 900; https://doi.org/10.3390/aerospace10100900 - 22 Oct 2023
Cited by 1 | Viewed by 1119
Abstract
Taking an aviation hydraulic pipeline as the research object, a fluid–solid coupling vibration model of the pipeline system, considering the influence of the clamp, is established. The clamp is equivalent to the combined form of the constraint point and the pipeline. The equivalent [...] Read more.
Taking an aviation hydraulic pipeline as the research object, a fluid–solid coupling vibration model of the pipeline system, considering the influence of the clamp, is established. The clamp is equivalent to the combined form of the constraint point and the pipeline. The equivalent stiffness of the clamp in each direction is obtained via the finite element method and substituted into the vibration model. The vibration response of the hydraulic pipeline system is obtained by changing the boundary conditions. The validity and accuracy of the vibration model were verified via the finite element method. The results show that the maximum error of the natural frequency of the pipeline system is within the acceptable range, which can prove that the model can better simulate the dynamic characteristics of the pipeline system and has a certain engineering reference value for the vibration analysis of hydraulic pipelines in aviation. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment)
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16 pages, 7901 KiB  
Article
Multi-Extremum Adaptive Fuzzy Network Method for Dynamic Reliability Estimation Method of Vectoring Exhaust Nozzle
by Chunyi Zhang, Zheshan Yuan, Huan Li, Jiongran Wen, Shengkai Zheng and Chengwei Fei
Aerospace 2023, 10(7), 618; https://doi.org/10.3390/aerospace10070618 - 06 Jul 2023
Viewed by 723
Abstract
To enhance the accuracy and efficiency of reliability analysis for an aero-engine vectoring exhaust nozzle (VEN), a multi-extremum adaptive fuzzy network (MEAFN) method is developed by absorbing an adaptive neuro-fuzzy inference system (ANFIS) into the multi-extremum surrogate model (MESM) method. In the proposed [...] Read more.
To enhance the accuracy and efficiency of reliability analysis for an aero-engine vectoring exhaust nozzle (VEN), a multi-extremum adaptive fuzzy network (MEAFN) method is developed by absorbing an adaptive neuro-fuzzy inference system (ANFIS) into the multi-extremum surrogate model (MESM) method. In the proposed method, the MERSM is used to establish the surrogate models of many output responses for the multi-objective integrated reliability analysis of the VEN. The ANFIS method is regarded as the basis function of the MESM method and adopted to improve the modeling precision of the MESM by introducing the membership degree into the input parameters and weights to improve the approximation capability of the neural network model to the high nonlinear reliability analysis of the VEN. The mathematical model of the MEAFN method and reliability analysis thoughts of the VEN is provided in this study. Then, the proposed MEAFN method is applied to conduct the dynamic reliability analysis of the expansion sheet and the triangular connecting rod in the VEN by considering the aerodynamic loads, operation temperature, and material parameters as the random input variables and the stresses and deformations as the output responses, compared with the Monte Carlo method and the extremum response surface method. From the comparison of the methods, it is indicated that the MEAFN method is promising to improve computational efficiency while maintaining accuracy. The efforts of this study provide guidance for the optimization design of the VEN and enrich the reliability theory of the flexible mechanism. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment)
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16 pages, 5784 KiB  
Article
An ML-Based Approach for HCF Life Prediction of Additively Manufactured AlSi10Mg Considering the Effects of Powder Size and Fatigue Damage
by Zhi Bian, Xiaojia Wang, Zhe Zhang, Chao Song, Tongzhou Gao, Weiping Hu, Linlin Sun and Xiao Chen
Aerospace 2023, 10(7), 586; https://doi.org/10.3390/aerospace10070586 - 27 Jun 2023
Cited by 1 | Viewed by 821
Abstract
As a popular technique, additive manufacturing (AM) has garnered extensive utilization in various engineering domains. Given that numerous AM metal components are exposed to fatigue loads, it is of significant importance to investigate the life prediction methodology. This study aims to investigate the [...] Read more.
As a popular technique, additive manufacturing (AM) has garnered extensive utilization in various engineering domains. Given that numerous AM metal components are exposed to fatigue loads, it is of significant importance to investigate the life prediction methodology. This study aims to investigate the high-cycle fatigue (HCF) behavior of AM AlSi10Mg, taking into account the influences of powder size and fatigue damage, and a novel ML-based approach for life prediction is presented. First, the damage-coupled constitutive model and fatigue damage model are derived, and the Particle Swarm Optimization method is employed for the material parameters’ calibration of M AlSi10Mg. Second, the numerical implementation of theoretical models is carried out via the development of a user-defined material subroutine. The predicted fatigue lives of AM AlSi10Mg with varying powder sizes fall within the triple error band, which verifies the numerical method and the calibrated material parameters. After that, the machine learning approach for HCF life prediction is presented, and the Random Forest (RF) and K-Nearest Neighbor (KNN) models are employed to predict the fatigue lives of AM AlSi10Mg. The RF model achieves a smaller MSE and a larger R2 value compared to the KNN model, signifying its superior performance in predicting the overall behavior of AM AlSi10Mg. Under the same maximum stress, a decrease in the stress ratio from 0.5 to −1 leads to a reduction in fatigue life for both powder sizes. As the powder size decreases, the rate of damage evolution accelerates, leading to shorter fatigue life. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment)
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15 pages, 5088 KiB  
Article
An AEFA-Based Optimum Design of Fuzzy PID Controller for Attitude Control Flywheel with BLDC Motor
by Zhizhou Zhang and Yang Li
Aerospace 2022, 9(12), 789; https://doi.org/10.3390/aerospace9120789 - 03 Dec 2022
Cited by 5 | Viewed by 1450
Abstract
A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC [...] Read more.
A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC motor is the basic part of the small satellite attitude control flywheel. In order to accurately control the attitude of the small satellite, a good motor control system is very important. Firstly, the mathematical model of the BLDC motor is established and the BLDC motor speed control system using traditional PID control is designed. Secondly, considering that the small satellite speed control system is a nonlinear system, a fuzzy PID control is designed to solve the shortcomings of the fixed parameters of the traditional PID control. Finally, we find that the control accuracy of the fuzzy PID control will change with the range of the input. Therefore, we introduce the AEFA to optimize fuzzy PID to achieve high-precision attitude control of small satellites. By simulating the BLDC motor system, the proposed fuzzy PID controller based on AEFA is compared with the traditional PID controller and the fuzzy PID controller. Results from different controllers show that the proposed control method could effectively reduce steady state error. In addition, the proposed fuzzy PID–AEFA controller has the better anti-jamming capability. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment)
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26 pages, 8087 KiB  
Article
A Leakage Rate Model for Metal-to-Metal Seals Based on the Fractal Theory of Porous Medium
by Yong Liu, Hao Du, Xinjiang Ren, Baichun Li, Junze Qian and Fangchao Yan
Aerospace 2022, 9(12), 779; https://doi.org/10.3390/aerospace9120779 - 01 Dec 2022
Cited by 2 | Viewed by 1944
Abstract
Due to the complexity of sealing surface topography, it is difficult to take the surface topography into consideration when building a leakage rate model theoretically. Therefore, a theoretical model for estimating the leakage rate of metal-to-metal seals based on the fractal theory of [...] Read more.
Due to the complexity of sealing surface topography, it is difficult to take the surface topography into consideration when building a leakage rate model theoretically. Therefore, a theoretical model for estimating the leakage rate of metal-to-metal seals based on the fractal theory of porous medium, which can objectively reflect the influence of sealing surface topography from a microscopic perspective, is proposed in the present work. In the approach, fractal parameters are adopted to characterize the sealing surface. The sealing interface is supposed to be a porous medium space and the intrinsic parameters are obtained through rigorous theoretical derivation. The results show that the topography parameters of the sealing surface have a significant effect on the intrinsic parameters of the pore space and lead to a significant influence on the leakage rate of metal-to-metal seals. Specifically, the smoother the sealing surface, the lower the leakage rate of the metal-to-metal seal. Moreover, the leakage rate decreases with an increase in the contact pressure, and, if the fluid pressure difference is too large, the sealing performance will be seriously reduced. The proposed model provides a novel way to calculate the leakage rate of metal-to-metal seals. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment)
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17 pages, 4661 KiB  
Article
Parameter Identification Method for a Periodic Time-Varying System Using a Block-Pulse Function
by Zhi Wang, Jun Wang, Jing Tian and Yu Liu
Aerospace 2022, 9(10), 614; https://doi.org/10.3390/aerospace9100614 - 17 Oct 2022
Viewed by 1270
Abstract
For periodic time-varying systems, a method of parameter identification based on the block-pulse function is presented. Firstly, the state-space equation of the system was expanded using the block-pulse function, then the recursion formula of the parameter identification of a time-varying system was obtained, [...] Read more.
For periodic time-varying systems, a method of parameter identification based on the block-pulse function is presented. Firstly, the state-space equation of the system was expanded using the block-pulse function, then the recursion formula of the parameter identification of a time-varying system was obtained, according to the irrespective and orthogonal characteristics of the block-pulse function. This study provides a wide range of applications by saving time in calculation with a highly accurate method. The parameter identification was carried out by including the numerical simulation model of a three-degree freedom system and the vibration experiment results of an asymmetrical rotor system. The state space wavelet method and EMD method were compared cross-sectionally with the proposed method; this shows that the proposed method is accurate and effective, which makes it valuable in numerous applications. It also has a certain application value for several related projects. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment)
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15 pages, 8677 KiB  
Article
Optimizable Image Segmentation Method with Superpixels and Feature Migration for Aerospace Structures
by Chengwei Fei, Jiongran Wen, Lei Han, Bo Huang and Cheng Yan
Aerospace 2022, 9(8), 465; https://doi.org/10.3390/aerospace9080465 - 21 Aug 2022
Cited by 7 | Viewed by 2326
Abstract
The lack of high-quality, highly specialized labeled images, and the expensive annotation cost are always critical issues in the image segmentation field. However, most of the present methods, such as deep learning, generally require plenty of train cost and high-quality datasets. Therefore, an [...] Read more.
The lack of high-quality, highly specialized labeled images, and the expensive annotation cost are always critical issues in the image segmentation field. However, most of the present methods, such as deep learning, generally require plenty of train cost and high-quality datasets. Therefore, an optimizable image segmentation method (OISM) based on the simple linear iterative cluster (SLIC), feature migration model, and random forest (RF) classifier, is proposed for solving the small sample image segmentation problem. In the approach, the SLIC is used for extracting the image boundary by clustering, the Unet feature migration model is used to obtain multidimensional superpixels features, and the RF classifier is used for predicting and updating the image segmentation results. It is demonstrated that the proposed OISM has acceptable accuracy, and it retains better target boundary than improved Unet model. Furthermore, the OISM shows the potential for dealing with the fatigue image identification of turbine blades, which can also be a promising method for the effective image segmentation to reveal the microscopic damages and crack propagations of high-performance structures for aeroengine components. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment)
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21 pages, 5086 KiB  
Article
A Prognostic and Health Management Framework for Aero-Engines Based on a Dynamic Probability Model and LSTM Network
by Yufeng Huang, Jun Tao, Gang Sun, Hao Zhang and Yan Hu
Aerospace 2022, 9(6), 316; https://doi.org/10.3390/aerospace9060316 - 10 Jun 2022
Cited by 7 | Viewed by 9939
Abstract
In this study, a prognostics and health management (PHM) framework is proposed for aero-engines, which combines a dynamic probability (DP) model and a long short-term memory neural network (LSTM). A DP model based on Gaussian mixture model-adaptive density peaks clustering algorithm, which has [...] Read more.
In this study, a prognostics and health management (PHM) framework is proposed for aero-engines, which combines a dynamic probability (DP) model and a long short-term memory neural network (LSTM). A DP model based on Gaussian mixture model-adaptive density peaks clustering algorithm, which has the advantages of an extremely short training time and high enough precision, is employed for modelling engine fault development from the beginning of engine service, and principal component analysis is introduced to convert complex high-dimensional raw data into low-dimensional data. The model can be updated from time to time according to the accumulation of engine data to capture the occurrence and evolution process of engine faults. In order to address the problems with the commonly used data driven methods, the DP + LSTM model is employed to estimate the remaining useful life (RUL) of the engine. Finally, the proposed PHM framework is validated experimentally using NASA’s commercial modular aero-propulsion system simulation dataset, and the results indicate that the DP model has higher stability than the classical artificial neural network method in fault diagnosis, whereas the DP + LSTM model has higher accuracy in RUL estimation than other classical deep learning methods. Full article
(This article belongs to the Special Issue State Monitoring and Health Management of Complex Equipment)
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