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

作者： Yazhou, Li Shuang, Yu Wei, Dai Beihang University School of Reliability and Systems Engineering Beijing China Systems Engineering Research Center Aero Engine Academy of China Beijing China

ISBN: (纸本)9798350356083

As the manufacturing process becomes increasingly complex, the occurrence of concurrent abnormal control chart pattern (CCP) is becoming more common. However, collecting and labeling abnormal concurrent CCP samples in real industrial settings is challenging. Due to the lack of training samples of concurrent CCP, the existing CCPR methods are difficult to accurately identify concurrent CCP. In this paper, the idea of zero-shot learning (ZSL) is introduced into process control, and an intelligent recognition method for zero-shot concurrent CCP is proposed. An attribute description space (ADS) of CCP is established to realize the reasoning from single CCP to concurrent CCP. Additionally, the ordinal pattern (OP) feature considering the sequential relationship between CCP data is extracted. The ADS is embedded between the OP features and the sample labels. The attribute classifier associates OP features with CCP attributes. The results show that the proposed method can achieve zerosample concurrent CCPR without their samples. © 2024 IEEE.

关键词： Zero-shot learning

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Trust State Monitoring of Human-machine Co-driving Based on EEG Signals in Intelligent Driving Scenarios 14

Trust State Monitoring of Human-machine Co-driving Based on ...

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14th International Conference on reliability, Maintainability and Safety, ICRMS 2023

作者： Cao, Wenyan Zheng, Rencheng Ding, Song Wu, Jiayue Cheng, Jie Liu, Jiacheng Pan, Xing School of Reliability and Systems Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350329988

The rapid development and widespread adaption of Artificial Intelligence (AI) technology are reshaping the dynamics of human-machine interaction. Intelligent driving, as a prominent representative of the latest technological revolution, achieves precise control over driving tasks through collaboration between the driver and AT systems. In this context, the driver's trust in AI plays a pivotal role in influencing the efficiency of human-machine cooperation and overall driving safety in intelligent driving scenarios. Nonetheless, there remains a notable absence of methods for accurately assessing and monitoring the state of human-machine interaction, particularly with regard to trust levels during co-driving. To address this gap, this study established an experimental platform utilizing UC-win/road software and conducted experiments to capture and analyze the driver's EEG signals. The primary objective was to develop a neural network-based trust discrimination model. This model exhibits a remarkable level of accuracy, achieving a data analysis accuracy rate of 90%. Furthermore, the research findings indicate that electrodes positioned in the occipital region of the brain exhibit heightened sensitivity to variations in human-machine trust levels compared to other electrodes. Notably, a trust discrimination model constructed solely using these occipital electrodes achieved an impressive accuracy rate of 98.52%. The significance of this trust discrimination model lies in its potential to dynamically measure human-machine trust levels, enabling real-time predictions and early warnings regarding AI interaction risks in intelligent driving scenarios. © 2023 IEEE.

关键词： Neural networks

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An Improved Method for FRAM Functional Variation Identification and Analysis 3

An Improved Method for FRAM Functional Variation Identificat...

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2022 3rd International Conference on Physics and engineering Mathematics, ICPEM 2022

作者： Cong, Kun Jiao, Jian Zhao, Tingdi School of Reliability and Systems Engineering Beihang University Beijing100191 China

With the increasing importance of socio-Technical systems, the analysis method of FRAM has attracted more and more attention due to the nonlinear accident causality and system adaptive characteristics. Aiming at the problem that the identification of functional variations in FRAM analysis method is not systematic enough, this paper proposes a method to systematically and comprehensively identify functional variations from three dimensions of human, machine and environment, and then gives the improved FRAM analysis process. Finally, a case study of the "Herald of Free Enterprise"accident is conducted to verify the applicability, correctness and effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Ferroelectric RAM

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Research on the Prediction Technique for Battery Performance Degradation Trends Based on Hierarchical Federated Leaming 15

Research on the Prediction Technique for Battery Performance...

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15th IEEE Global reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Gong, Gantian Xu, Peiyang Jin, Xiangyu Ma, Jian School of Reliability and Systems Engineering Beihang University Beijing China Institute of Reliability Engineering Beihang University Beijing China International Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354010

Lithium-ion batteries have a wide range of applications as a new energy source, and it is of great significance to realize the prediction of the performance degradation trends of lithium-ion batteries. At present, data-driven prediction technology for performance degradation trends has become an effective method for the prediction of lithium batteries. However, in the actual prediction process, lithium batteries have less data available for training, resulting in poor training accuracy. In this paper, we propose a prediction algorithm for battery performance degradation trends based on hierarchical federated learning to improve the accuracy of lithium battery prediction. First, we propose a federal grouping technique based on improved K-medoids clustering to group multi-formulation batteries. Then, we perform hierarchical federated learning based on the grouping results. Com pared with traditional client-server federation learning, hierarchical federation learning reduces the server communication burden and improves the model convergence speed. In addition, we use a multiscale time convolutional network prediction method to predict a single cell. The method involves predicting trends at both large and small scales, followed by a refinement process to enhance the accuracy of the predictions. The method proposed in this paper has good performance on the dataset. © 2024 IEEE.

关键词： Federated learning

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Delay bounded routing with the maximum belief degree for dynamic uncertain networks

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Journal of systems engineering and Electronics 2025年第1期36卷 127-138页

作者： MA Ji KANG Rui LI Ruiying ZHANG Qingyuan LIU Liang WANG Xuewang School of Reliability and Systems Engineering Beihang UniversityBeijing 100191China Science and Technology on Reliability and Environmental Engineering Laboratory Beijing 100191China International Innovation Institute Beihang UniversityHangzhou 311115China Institute of Telecommunication and Navigation Satellites China Academy of Space TechnologyBeijing 100094China

Delay aware routing is now widely used to provide efficient network transmission. However, for newly developing or developed mobile communication networks(MCN), only limited delay data can be obtained. In such a network, the delay is with epistemic uncertainty, which makes the traditional routing scheme based on deterministic theory or probability theory not applicable. Motivated by this problem, the MCN with epistemic uncertainty is first summarized as a dynamic uncertain network based on uncertainty theory, which is widely applied to model epistemic uncertainties. Then by modeling the uncertain end-toend delay, a new delay bounded routing scheme is proposed to find the path with the maximum belief degree that satisfies the delay threshold for the dynamic uncertain network. Finally, a lowEarth-orbit satellite communication network(LEO-SCN) is used as a case to verify the effectiveness of our routing scheme. It is first modeled as a dynamic uncertain network, and then the delay bounded paths with the maximum belief degree are computed and compared under different delay thresholds.

关键词： dynamic uncertain network uncertainty theory epistemic uncertainty delay bounded routing maximum belief degree

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Deep reinforcement learning-based resilience optimization for infrastructure networks restoration with multiple crews

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Frontiers of engineering Management 2025年第1期12卷 141-153页

作者： Qiang FENG Qilong WU Xingshuo HAI Yi REN Changyun WEN Zili WANG School of Reliability and Systems Engineering Beihang UniversityBeijing 100191China Hangzhou International Innovation Institute Beihang UniversityHangzhou 311115China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore

Restoration of infrastructure networks(INs)following large disruptions has received much attention lately due to examples of massive localized *** this challenge are two complex but critical problems:repair route identification and optimizing the sequence of the repair actions for resilience *** approaches have not,however,given due consideration to globally optimal enhancement in resilience,especially with multiple repair crews that have uneven *** address this gap,this paper focuses on a resilience opti-mization(RO)strategy for coordinating multiple *** objective is to determine the optimal routes for each crew and the best sequence of repairs for damaged nodes and *** the two-layered decision-making required—coordinating between multiple crews and opti-mizing each crew's actions—this study develops a deep reinforcement learning(DRL)*** framework leverages an actor-critic neural network that processes IN damage data and guides Monte Carlo tree search(MCTS)to identify optimal repair routes and actions for each crew.A case study based on the 228-node power grid,simulated using Python,demonstrates that the proposed DRL approach effectively supports restoration decision-making.

关键词： Infrastructure network restoration Deep reinforcement learning Resilience optimization Repair routing Multiple crews

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Health assessment method for core components of switching quantity interface circuit based on active excitation test 14

Health assessment method for core components of switching qu...

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14th International Conference on reliability, Maintainability and Safety, ICRMS 2023

作者： Xu, Peiyang Jia, Long Ma, Jian Chen, Chang Chen, Wei Lan, Chunyun Zou, Xinyu Li, Tianhao Science & Technology on Reliability & Environmental Engineering Laboratory School of Reliability and Systems Engineering Beihang University Beijing China School of Automation Science and Electrical Engineering Beijing Aerospace Institute of Automatic Control Beihang University Beijing China Science & Technology on Reliability & Environmental Engineering Laboratory School of Reliability and Systems Engineering Institute of Reliability Engineering Beihang University Beijing China Beijing Aerospace Institute of Automatic Control Beijing China

ISBN: (纸本)9798350329988

As power electronics technology continues to advance, the prevalence of switching quantity interface circuits has grown in diverse domains, encompassing industrial production and everyday civilian applications. An analysis of operational data from these circuits, along with an assessment of their overall health, proves instrumental in the timely detection and resolution of operational anomalies. This holds paramount significance in upholding the reliability of these interface circuits. Nevertheless, tracking the degradation trajectory of the core components during their operational lifespan poses a challenge. Typically, only two states, normal and abnormal, are readily discernible, making direct health assessment elusive. To address this issue, this study introduces a health assessment methodology for the fundamental components of switch quantity interface circuits based on active excitation testing. Through the application of active excitation, the performance decay profile of the optical coupler is ascertained, solving the problem of capturing the degradation process within these circuits. This approach facilitates the stable and precise characterization of the health status of switching quantity interface circuits. The health assessment method presented in this paper is characterized by modest computational resource demands and a reduced reliance on expert knowledge. It is adept at quantitatively delineating the health status of switch-quantity interface circuits with precision, thereby offering guidance for the maintenance and replacement of pivotal electronic components within these circuits. In doing so, it contributes to the assurance of operational dependability and an extension of the service life of switch quantity interface circuits. © 2023 IEEE.

关键词： reliability analysis

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THE UAV RISK PREDICTION METHOD BASED ON CWGAN-GP AND CNN 51

THE UAV RISK PREDICTION METHOD BASED ON CWGAN-GP AND CNN

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： He, Zhao Zhou, Shenghan Chen, Xu Wen, Jingxiao Chang, Wenbing School of Reliability and Systems Engineering Beihang University Beijing China School of Economics and Management Beihang University Beijing China

The paper proposes a method based on CWGAN-GP and CNN for solving the problem of low model accuracy due to data imbalance in UAV risk prediction. Firstly, the conditional Wasserstein GAN with gradient penalty is used to generate the scarce abnormal samples in the original dataset. Secondly, the generated samples are combined with the original samples to obtain the expanded dataset. Finally, the expanded dataset is used to train the convolutional neural network model for risk prediction. The study conducts experiments on a real dataset called ALFA. The results show that the CWGAN-GP model can generate high-quality samples, which can be combined with CNN to implement a good risk prediction effect. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Prediction models

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A novel reliability analysis method for engineering problems:Expanded learning intelligent back propagation neural network

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Chinese Journal of Aeronautics 2024年第12期37卷 212-230页

作者： Ying HUANG Jianguo ZHANG Xiaoduo FAN Qi GONG Lukai SONG School of Reliability and Systems Engineering Beihang UniversityBeijing 100191China AVIC Aero Polytechnology Establishment Beijing 100028China Department of Mechanical Engineering The Hong Kong Polytechnic UniversityHong Kong 100872China Research Institute of Aero-Engine Beihang UniversityBeijing 102206China

Estimating the failure probability of highly reliable structures in practice engineering,such as aeronautical components,is challenging because of the strong-coupling and the small failure probability *** this paper,an Expanded Learning Intelligent Back Propagation(EL-IBP)neural network approach is developed:firstly,to accurately characterize the engineering response coupling relationships,a high-fidelity Intelligent-optimized Back Propagation(IBP)neural network metamodel is developed;furthermore,to elevate the analysis efficacy for small failure assessment,a novel expanded learning strategy for adaptive IBP metamodeling is *** numerical examples and one typical practice engineering case are analyzed,to validate the effectiveness and engineering application value of the proposed *** comparison shows that the ELIBP method holds significant efficiency and accuracy superiorities in engineering *** current study may shed a light on pushing the adaptive metamodeling technique deeply toward complex engineering reliability analysis.

关键词： reliability analysis Back propagation neural network Adaptive metamodel Variance expansion Small failure probability Strong-coupling

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A reliability Evaluation and Optimization Policy for Two-phase Industrial Plants Considering Time-varying Stochastic Environmental Effect 6

A Reliability Evaluation and Optimization Policy for Two-pha...

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

作者： Wei, Fanping Ma, Xiaobing Chen, Yi Qiu, Qingan Yang, Li Beihang University School of Reliability and Systems Engineering Beijing China Beijing Institute of Technology School of Management Beijing China

ISBN: (纸本)9798350356083

Environmental effects are key factors that affect the degradation accumulation over the lifetime of diverse industrial plants, especially for plants exposed to harsh environment. To evaluate the reliability of system and perform preventive in time, an assessment and decision-making policy considering plants exhibiting two-phase environmental degradation characteristics is proposed. The cumulative impact of environmental effects on degradation behavior is quantified through the establishment of a time-varying stochastic environment model. A two-stage nonlinear stochastic process with environmental covariates is to capture the long-term degradation evolution. Through detection, the accumulation of real-time degradation is found, and the occurrence of hidden defects can be sensed, and then replacement decisions are made according to a pre-determined threshold. Through the joint optimization of inspection intervals and threshold limits, the long-term operational cost is optimized. A numerical experiment is conducted to validate the advantages of the policy, particularly in terms of downtime and cost reduction. © 2024 IEEE.

关键词： Cost reduction

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