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Quantile-based optimization under uncertainties for complex engineering structures using an active learning basis-adaptive PC-Kriging model

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Chinese Journal of Aeronautics 2025年第1期38卷 340-352页

作者： Yulian GONG Jianguo ZHANG Dan XU Ying HUANG School of Reliability and Systems Engineering Beihang UniversityBeijing 100191China

The reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation *** this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a *** active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO ***,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit *** with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.

关键词： reliability-based design optimization Quantile-based Basis-adaptive PC-Kriging Complex engineering structures Active learning Uncertainty

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Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

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Computer Modeling in engineering & Sciences 2023年第10期137卷 527-554页

作者： Zeyu Wu Bo Sun Qiang Feng Zili Wang Junlin Pan School of Reliability and Systems Engineering Beihang UniversityBeijingChina Institute of Reliability Engineering Beihang UniversityBeijingChina

Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart ***,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation *** article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting *** incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial *** augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind ***,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete *** incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s *** with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.

关键词： Physics-informed method probabilistic forecasting wind power generative adversarial network extreme learning machine day-ahead forecasting incomplete data smart grids

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Belief reliability:a scientific exploration of reliability engineering

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Journal of systems engineering and Electronics 2024年第3期35卷 619-643页

作者： ZHANG Qingyuan LI Xiaoyang ZU Tianpei KANG Rui International Innovation Institute Beihang UniversityHangzhou 311115China Science and Technology on Reliability and Environmental Engineering Laboratory Beijing 100191China School of Reliability Engineering and Systems Engineering Beihang UniversityBeijing 100191China School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China

This paper systematically introduces and reviews a scientific exploration of reliability called the belief *** with the origin of reliability engineering,the problems of present theories for reliability engineering are summarized as a query,a dilemma,and a ***,through philosophical reflection,we introduce the theoretical solutions given by belief reliability theory,including scientific principles,basic equations,reliability science experiments,and mathematical *** basic methods and technologies of belief reliability,namely,belief reliability analysis,function-oriented belief reliability design,belief reliability evaluation,and several newly developed methods and technologies are sequentially elaborated and *** on the above investigations,we summarize the significance of belief reliability theory and make some prospects about future research,aiming to promote the development of reliability science and engineering.

关键词： belief reliability performance margin reliability experiment chance measure uncertainty

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reliability modelling and assessment of CMOS image sensor under radiation environment

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Chinese Journal of Aeronautics 2024年第9期37卷 297-311页

作者： Zhao TAO Wenbin CHEN Xiaoyang LI Rui KANG School of Reliability and Systems Enginering Beihang UniversityBejing 00191China Science and Technologyon Reliabiliy and Environmental Engineering Laboratory Beijing 100191China

The Complementary Metal-Oxide Semiconductor(CMOS)image sensor is a critical component with the function of providing accurate positioning in many space application *** long-time operation in space environments,there are radiation related degradation and var-ious uncertainties affecting the positioning accuracy of CMOS image sensors,which further leads to a reliability reduction of CMOS image ***,the reliability of CMOS image sensors is related to their specified function,degradation,and uncertainties;however,current research has not fully described this *** this paper,a comprehensive approach to reliability modelling of CMOs image sensors is proposed based on the reliability science ***,the perfor-mance margin modelling of centroid positioning accuracy is ***,the degradation model of CMOS image sensors is derived considering the dark current increase induced by the total ionizing dose ***,various uncertainties are analyzed and quantified,and the measure-ment equation of reliability is proposed.A case study of a CMOS image sensor is conducted to apply the proposed method,and the sensitivity analysis can provide suggestions for design and use of CMOS image sensors to ensure reliability.A simulation study is conducted to present the advantages oftheproposed comprehensive approach.

关键词： Complementary metal-oxide semiconductor image sensor Degradation reliability reliability science principles Total ionizingdose effects Uncertainty analysis

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Pharmacokinetic model for extravascular administration based on uncertain differential equation

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13887-13899页

作者： Liu, Zhe Kang, Rui School of Reliability and Systems Engineering Beihang University Beijing100191 China Science and Technology on Reliability and Environmental Engineering Laboratory Beijing100083 China

Pharmacokinetics studies the time course of drug concentration in body compartments, and one of the commonly used methods of administration is extravascular administration. Undoubtedly, biological systems are subject to various internal and external noises that change over time. To rationally deal with these dynamic noises, this paper deduces a pharmacokinetic model for extravascular administration using uncertain differential equations for the first time. Some essential pharmacokinetic indexes such as drug concentration, area under the curve, and maximum drug concentration are investigated. Based on the three basic principles of reliability science, i.e., margin-based reliable principle, eternal degradation principle and uncertainty principle, the belief reliability for drug is investigated. The minimum cover estimation for the set of unknown parameters in this uncertain pharmacokinetic model is provided. Numerical example and a real data analysis illustrate our method in detail. Finally, the paradox of a stochastic pharmacokinetic model is presented. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： reliability

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Impact Analysis of Microscopic Defect Types on the Macroscopic Crack Propagation in Sintered Silver Nanoparticles

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Computer Modeling in engineering & Sciences 2024年第4期139卷 441-458页

作者： Zhongqing Zhang Bo Wan Guicui Fu Yutai Su Zhaoxi Wu Xiangfen Wang Xu Long School of Reliability and Systems Engineering Beihang UniversityBeijing100191China School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an710072China China Aerospace Components Engineering Center Beijing100098China

Sintered silver nanoparticles(AgNPs)arewidely used in high-power electronics due to their exceptional ***,the material reliability is significantly affected by various microscopic *** this work,the three primary micro-defect types at potential stress concentrations in sintered AgNPs are identified,categorized,and *** dynamics(MD)simulations are employed to observe the failure evolution of different microscopic *** dominant mechanisms responsible for this evolution are dislocation nucleation and dislocation *** the same time,this paper clarifies the quantitative relationship between the tensile strain amount and the failure mechanism transitions of the three defect types by defining key strain *** impact of defect types on the failure process is also ***,traction-separation curves extracted from microscopic defect evolutions serve as a bridge to connect the macro-scale *** validity of the crack propagation model is confirmed through tensile ***,we thoroughly analyze how micro-defect types influence macro-crack propagation and attempt to find supporting evidence from the MD *** findings provide a multi-perspective reference for the reliability analysis of sintered AgNPs.

关键词： Sintered silver nanoparticles defect types microscopic defect evolution macroscopic crack propagation molecular dynamics simulation cohesive zone model

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Intelligent Driving Safety Monitoring Based on Machine Learning - An Example of Students Research Training Program in the Context of New engineering Disciplines 24

Intelligent Driving Safety Monitoring Based on Machine Learn...

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3rd International Conference on Computer, Artificial Intelligence and Control engineering, CAICE 2024

作者： Liu, Jiacheng Luo, Haoyue Gao, Yushu Liang, Ruiyu Zeng, Ruoyan Pan, Xing School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798400716942

The development of new engineering technologies such as artificial intelligence, machine learning, and big data analysis has prompted engineering colleges to keep up with the times in their courses and introduce relevant knowledge to cultivate professionals in different fields. Research training, as an important form of practical teaching in current colleges and universities, plays an important role in improving the innovation and entrepreneurship abilities of undergraduate students. This article takes a comprehensive research project conducted by the junior students at Beihang University as an example, and provides an example of cross disciplinary research training using emerging technologies such as machine learning and quality and reliability related professional knowledge in the context of new engineering. Quality and reliability are interdisciplinary fields involving aerospace, information, transportation, new energy, and other fields. Through scientific research training in new engineering, students can aim at these fields and comprehensively apply professional knowledge learned in the classroom and new engineering technologies mastered in practice to solve practical problems. This research training example focuses on comprehensive security monitoring in the field of intelligent transportation, using machine learning and other methods to process driver personnel data, environmental data, and vehicle data related to driving safety. Through training, mathematical models and methods that characterize the safety status of human-machine co driving are obtained, and corresponding software systems are developed. Although this training example is targeted at the emerging field of intelligent transportation, it also has certain reference significance for practical teaching in other professions and practical applications in other fields. © 2024 ACM.

关键词： Students

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Capacity Prediction Model of Lithium-ion Battery Based on 1/f Noise and Electrochemical Impedance Spectroscopy 6

Capacity Prediction Model of Lithium-ion Battery Based on 1/...

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6th International Conference on System reliability and Safety engineering, SRSE 2024

作者： Zhang, Yanji Wang, Zijun Zheng, Zimu Zhan, Jiaxuan An, Ning Chen, Yunxia Beihang University School of Reliability and Systems Engineering Beijing China

ISBN: (纸本)9798350356083

Gaining a deep understanding of the degradation mechanisms and characteristics of lithium-ion batteries, as well as accurately predicting their remaining capacity, is crucial for ensuring the reliability of circuit system equipment and achieving real-time monitoring for lithium-ion battery safety.A lithium-ion battery remaining capacity prediction model based on 1/f noise and electrochemical impedance spectroscopy (EIS) was proposed. The analysis of EIS and 1/f noise can sensitively reflect internal degradation and defects. In terms of experiments, this project investigates an economical and simple self-built impedance spectroscopy measurement device for lithium-ion batteries. A battery cycling charge-discharge testing platform was constructed, and 65 cycles of charge-discharge aging tests were conducted on 8 14500 batteries, obtaining datasets of noise and impedance spectra during degradation. Regarding model construction, deep learning methods were employed to build convolutional neural networks (CNNs) based on single-type data and combined neural networks based on two types of data, utilizing collected EIS and ECN data to train the neural networks to obtain regression models. Using a random search algorithm, the hyperparameter combinations of the models were optimized, resulting in three types of lithium-ion battery remaining capacity prediction models based on battery failure process data. Through validation, it was found that the prediction models obtained through deep learning can accurately predict the remaining capacity of lithium-ion batteries. Additionally, prediction results obtained through deep learning using two types of data exhibit higher accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep LSTM-based autoencoder for CFRP damage imaging using Lamb wave and correlation factor weighted delay-and-sum algorithm

Deep LSTM-based autoencoder for CFRP damage imaging using La...

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2023 International Symposium on Structural Dynamics of Aerospace, ISSDA 2023

作者： Ji, Dingcheng Lin, Jing Gao, Fei School of Reliability and Systems Engineering Beihang University Beijing China

The ultrasonic Lamb wave testing (ULWT) has proven valuable in non-destructive testing (NDT) due to its high sensitivity and wide coverage. However, the classical post-processing algorithm, the delay-and-sum (DAS) technique, suffers from notable artifacts and inadequate accuracy in Lamb wave inspection due to the existence of reflection and superposition, especially on composite materials. In this study, we proposed a novel algorithm, the correlation factor weighted DAS imaging algorithm based on the Long short-term memory-autoencoder (LSTM-AE), to address these deterioration issues. The LSTM-AE demonstrates the capability to extract potential features and accurately reconstruct input signals. By incorporating the concept of anomaly detection, an LSTM-AE trained with intact signals produces a significantly distorted output when exposed to damaged signals as input. We formulated a novel damage index (DI) based on the Pearson Correlation Coefficient (PCC) between the output and input signals as the weighting factor. The proposed method has undergone experimental validation, confirming its effectiveness in Lamb wave inspection. © Published under licence by IOP Publishing Ltd.

关键词： Surface waves

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A reliability Test Scenario Generation Method for Unmanned Vehicle Off-Road Tracking Task Based on Deep Reinforcement Learning

A Reliability Test Scenario Generation Method for Unmanned V...

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2024 IEEE International Conference on Control Science and systems engineering, ICCSSE 2024

作者： Wang, Yiwei Ren, Yi Yang, Dezhen Li, Xiaobin Jia, Lulu Gao, Yuntian School of Reliability and Systems Engineering Beihang University Beijing China

ISBN: (纸本)9798331517199

It is important to conduct simulation tests of unmanned vehicles (UV). However, some test scenario generation methods face challenges such as a lack of off-road scenarios and insufficient consideration of interferences. This paper proposes an unmanned vehicle off-road task reliability test scenario generation method. First, the static test environment is built based on the TORCS simulation platform. Subsequently, different types of unmanned vehicle-target vehicle (TV) tracking interaction behaviour models are generated based on the modified DDPG method. Finally, the unmanned vehicle task reliability test under different disturbances is achieved. The results show that the method can not only effectively generate off-road tracking test scenarios, but also adjust the interference to test unmanned vehicles' task reliability. The method provides valuable guidance for testing and enhancing the reliability of the model to some degree. © 2024 IEEE.

关键词： Off road vehicles

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