2021 3rd International Conference on System Reliability and Safety Engineering (SRSE)最新文献_第6页

An Efficient Robust Design Optimization Approach of Electromagnetic Relay Based on Surrogate Model and Evolutionary Algorithm 基于代理模型和进化算法的电磁继电器稳健设计优化方法

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00021

Hao Chen, X. Ye, Yigang Lin, G. Zhai

{"title":"An Efficient Robust Design Optimization Approach of Electromagnetic Relay Based on Surrogate Model and Evolutionary Algorithm","authors":"Hao Chen, X. Ye, Yigang Lin, G. Zhai","doi":"10.1109/SRSE54209.2021.00021","DOIUrl":"https://doi.org/10.1109/SRSE54209.2021.00021","url":null,"abstract":"The reliability and robustness of electromagnetic relay's performance characterize directly related to the performance, reliability and robustness of the aviation, aerospace, intelligent equipment industry, and its design and optimization have initiated boundless concern. The efficiency of the calculation of performance characteristics and the accuracy of the optimization process are two critical aspects affecting the design optimization of electromagnetic relay reliability and robustness. In response to the above issues, this study proposed an efficient computational burden reduction strategy for the multi-objective design and optimization aimed at the electromagnetic relay with substantial non-linearity and insufficient convergence characteristics. The dual response surface approach extracted the surrogate model of the mean value and variance for the critical static attractive force point. On the basis of the surrogate model, a particle swarm evolutionary algorithm for multi-objective design and optimization of electromagnetic relay has been modified. Then, the modified particle swarm evolutionary algorithm was used to optimize the dual response surface model, verifying the algorithm's feasibility. At last, the effectiveness of the proposed approach in this study was verified by a case study of the differential polarized magnetically maintained electromagnetic relay double permanent magnets.","PeriodicalId":168429,"journal":{"name":"2021 3rd International Conference on System Reliability and Safety Engineering (SRSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129800099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability Allocation Method of Comprehensive Weight Computer Numerical Control Machine Tool Based on Failure Correlation and Factor Correlation 基于故障相关性和因子相关性的综合权重数控机床可靠性分配方法

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00045

Gu Dongwei, Xu Zhen, Zhong Yuhong, Li Qihan, Long Zhe

{"title":"Reliability Allocation Method of Comprehensive Weight Computer Numerical Control Machine Tool Based on Failure Correlation and Factor Correlation","authors":"Gu Dongwei, Xu Zhen, Zhong Yuhong, Li Qihan, Long Zhe","doi":"10.1109/SRSE54209.2021.00045","DOIUrl":"https://doi.org/10.1109/SRSE54209.2021.00045","url":null,"abstract":"According to the shortage of independent hypothesis allocation models between influence factors and subsystems, a reliability allocation method considering influence factor correlation and subsystem failure correlation is proposed. The CNC (Computer Numerical Control) machine tool is taken as the research object. Using Copula function to analyze failure correlation analysis between subsystems, establish subsystem complete reliability model and integrated importance measure model under failure correlation. The four factors of influencing reliability allocation are subsystem complexity, integrated importance measure of subsystem based on failure correlation, failure frequency and specific gravity ratio of failure downtime. The correlation analysis among the influencing factors was carried out by using grey correlation theory, and the subjective weight of the subsystem was determined. The IEW-VIKOR(Information Entropy Weight-Vlsekriterijumska Optimizacija I Kompromisno Resenje) method is used to determine the objective weights of subsystems. The comprehensive weight of the subsystem is also obtained through combinatorial empowerment. The reliability allocation of the CNC machine tool subsystem was carried out according to the failure correlation reliability model, and the results of failure independence and failure correlation hypothesis allocation were compared and analyzed. When the reliability index is the same, the reliability allocation method proposed in this paper can assign lower reliability to each subsystem, which is of great significance to improve the reliability of CNC machine tools and reduce the difficulty of reliability design.","PeriodicalId":168429,"journal":{"name":"2021 3rd International Conference on System Reliability and Safety Engineering (SRSE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131097859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

State of Health Estimation of Lithium Ion Battery with Uncertainty Quantification Based on Bayesian Deep Learning 基于贝叶斯深度学习的不确定性锂离子电池健康状态评估

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00009

Yuqi Ke, Ruomei Zhou, Rong Zhu, W. Peng

{"title":"State of Health Estimation of Lithium Ion Battery with Uncertainty Quantification Based on Bayesian Deep Learning","authors":"Yuqi Ke, Ruomei Zhou, Rong Zhu, W. Peng","doi":"10.1109/SRSE54209.2021.00009","DOIUrl":"https://doi.org/10.1109/SRSE54209.2021.00009","url":null,"abstract":"Lithium Ion (Li-ion) batteries have been widely used in the field of electric vehicles (EVs). The safety of Li-ion battery is what people concern most. Accurately predicting the state of health (SOH) of Li-ion battery is a crucial problem. Previous studies have obtained high precision in SOH estimation. However, the prediction results are always point estimates which cannot obtain the confidence interval. SOH estimation without uncertainty quantification for Li-ion battery maintenance decision is risky. The work described in this paper is an attempt to quantify the aleatoric uncertainty and epistemic uncertainty of SOH estimation for Li-ion battery. We propose a new method for SOH estimation based on Bayesian neural network (BNN) using variational inference (VI) and Monte Carlo dropout (MC dropout) approximate inference methods. The Li-ion battery dataset published by National Aeronautics and Space Administration (NASA) is applied to validate the feasibility of the proposed method. Under the condition that the precision of SOH estimation is almost constant or even better comparing with non-Bayesian probabilistic models, we also obtain the uncertainty of the estimations, which makes the results more robust.","PeriodicalId":168429,"journal":{"name":"2021 3rd International Conference on System Reliability and Safety Engineering (SRSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130049629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Performance-based Reliability Prediction of Power Supply Considering Degradation Uncertainties 考虑退化不确定性的电源可靠性性能预测

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00011

Hao Niu, Shaohua Yang, Wenyuan Liao, Canxiong Lai, Wen Sun

引用次数: 0

Research On Simulation Trade-off Analysis Method Of Reliability Index of Component-based Software 基于构件的软件可靠性指标仿真权衡分析方法研究

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00063

Lihao Yang, Jingbo Liu, Hongqi Yang, G. Nie, Ning Hu, Xiangwei Wu

引用次数: 0

Research on Risk Prediction of Major Hazards Installation Enterprises Based on Random Forest 基于随机森林的重大灾害安装企业风险预测研究

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00056

Zhu Jing-cong, M. Dong, Zhao Ying, Guangbo Lei

引用次数: 0

A Rolling Bearing Fault Early Warning and Diagnosis Technology Based on Spectrum Analysis and Improved MSET 基于频谱分析和改进MSET的滚动轴承故障预警诊断技术

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00008

Li Yazhou, Dai Wei, Han Xi

{"title":"A Rolling Bearing Fault Early Warning and Diagnosis Technology Based on Spectrum Analysis and Improved MSET","authors":"Li Yazhou, Dai Wei, Han Xi","doi":"10.1109/SRSE54209.2021.00008","DOIUrl":"https://doi.org/10.1109/SRSE54209.2021.00008","url":null,"abstract":"Timely fault early warning and accurate fault location diagnosis in rolling bearing are significant to improve the reliable operation of rotating machinery. In this paper, a method for monitoring rolling bearing condition is proposed based on spectrum analysis and improved Multivariate State Estimation Technology (MSET). First, the envelope spectrum of original signal is obtained through Fast Kurtogram (FK). The fixed rotation frequency and fault characteristic frequency of bearing are obtained according to the empirical equation, and the corresponding amplitude of these frequencies in the envelope spectrum is used as the monitoring parameter. Secondly, a nonparametric model of the bearing under normal operating conditions is established via improved MSET, and similarity is introduced to quantitatively measure the similarity degree between the observed state and the normal state. Thirdly, the fault contribution rates of different fault frequencies are calculated for diagnosis of bearing fault types. Finally, the actual operating data of a certain bearing is used as an example for verification. The result shows that the proposed method can provide early warning of bearing faults and accurately identify fault types in the early stage.","PeriodicalId":168429,"journal":{"name":"2021 3rd International Conference on System Reliability and Safety Engineering (SRSE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121553523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability Optimization Deploymentof Complex Electromechanical Equipment Based on Multi-objective Optimization Algorithm 基于多目标优化算法的复杂机电设备可靠性优化部署

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00044

Deng Jianhui, Liu Yanyan, Liu Pengpeng, Huang Jine

引用次数: 0

A Hybrid Compute Engine Implemented for Dynamic Reliability and Risk Analysis 用于动态可靠性和风险分析的混合计算引擎

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00055

Jun Yang, Fengjun Li, Chenyu Jiang, Lichen Zheng, Y. Deng, Jieheng Liang, Ming Yang

{"title":"A Hybrid Compute Engine Implemented for Dynamic Reliability and Risk Analysis","authors":"Jun Yang, Fengjun Li, Chenyu Jiang, Lichen Zheng, Y. Deng, Jieheng Liang, Ming Yang","doi":"10.1109/SRSE54209.2021.00055","DOIUrl":"https://doi.org/10.1109/SRSE54209.2021.00055","url":null,"abstract":"In the paper, we present a hybrid compute engine integrating Boolean and analytical models for dynamic reliability and risk analysis of complex safety-critical industrial systems with dynamic interactions and multiphase mission consideration. The hybrid compute engine focuses on three aspects: i) Discrete-time Dynamic Event Tree (DDET) models generation and analysis; ii) risk-based reliability modeling and failure analysis of complex digital industrial process systems under uncertainties using Markov/CCMT approach; iii) dynamic mission reliability analysis of safety-critical systems and emergency response planning for mission success and safety management by GO-FLOW method. The DDET models implemented based on graph-based search and sequence diagram refactoring can be consistently linked to Markov/CCMT and GO-FLOW modules for branch probability estimation. The versatile multi-way search solver for computationally efficient Markov/CCMT analysis and supplementary success-oriented path tracing and planning are briefly illustrated with simplified case studies. It shows that the system integration solutions can provide the comprehensive probabilistic modeling toolkit with the connectivity to overcome drawbacks of any single methodologies when facing the challenges for dynamic reliability and risk analysis.","PeriodicalId":168429,"journal":{"name":"2021 3rd International Conference on System Reliability and Safety Engineering (SRSE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133876332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Imperfect Maintenance Optimization of Multi-State Rolling Stocks Based on Deep Reinforcement Learning 基于深度强化学习的多状态机车车辆不完全维修优化

2021 3rd International Conference on System Reliability and Safety Engineering (SRSE) Pub Date : 2021-11-01 DOI: 10.1109/SRSE54209.2021.00051

Chen Zhang, Yan-Fu Li

{"title":"Imperfect Maintenance Optimization of Multi-State Rolling Stocks Based on Deep Reinforcement Learning","authors":"Chen Zhang, Yan-Fu Li","doi":"10.1109/SRSE54209.2021.00051","DOIUrl":"https://doi.org/10.1109/SRSE54209.2021.00051","url":null,"abstract":"Developing an effective maintenance schedule for the rolling stocks has always been a critical issue of the railway companies. Currently, the Chinese railway companies still schedule the maintenance activities periodically according to the miles the rolling stocks traveled, which cause serious over-maintenance for the purpose of satisfying high reliability requirement. In this paper, we consider an imperfect maintenance optimization problem for multiple rolling stocks with stochastic maintenance time and operating conditions. The rolling stocks are modeled as the multi-state systems to characterize the degradation process. Meanwhile, the operating condition and the current location of the rolling stocks are also taken into consideration. The transition dynamics of the degradation process depends on the operating conditions. The optimization problem is formulated as a continuous-time Markov decision process and the objective is to maximize the total discounted reward related to the operating profit and the cost due to the maintenance, replacement and transportation in an infinite planning horizon. A deep reinforcement learning algorithm is developed to obtaining the optimal maintenance policy of the rolling stocks. A numerical experiment is given to demonstrate the advantage of the algorithm to improve the maintenance schedule of the rolling stocks.","PeriodicalId":168429,"journal":{"name":"2021 3rd International Conference on System Reliability and Safety Engineering (SRSE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114299078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0