2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO)最新文献_第10页

Optimal Imperfect Maintenance in a Multi-state System with Two Failure Types 具有两种故障类型的多状态系统的最优不完全维护

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.47

S. Dietrich, W. Kahle

引用次数: 5

Approach for Inferring Fractiles of Future Time between Failures 一种推断故障间未来时间粒子的方法

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.55

R. Jiang

{"title":"Approach for Inferring Fractiles of Future Time between Failures","authors":"R. Jiang","doi":"10.1109/SMRLO.2016.55","DOIUrl":"https://doi.org/10.1109/SMRLO.2016.55","url":null,"abstract":"When the data on the time between failures (TBF) are available, a challenging issue is to infer the distribution of future TBFs. The existing approaches to address this issue include varying-parameter normal and Weibull distributions, where the distributional parameters are functions of the number of cumulative failures. Since the distributional parameters are extrapolated from the two fitted models of the distribution parameters, these approaches may be not robust. In this paper, we propose an improved approach. The proposed approach first estimates alpha-fractiles of time to failure from the observed data for multiple alpha values, and then fits the estimates associated with each alpha value to a three-parameter power-law model. The fitted power-law models are used to estimate the fractiles of a certain future TBF, which form an empirical distribution of the future TBF. The empirical distribution can be further fitted to a distribution model. Due to multiple fractiles are estimated, it is expected that the proposed approach is robust. The approach is illustrated by the well-known bus-motor failure data.","PeriodicalId":254910,"journal":{"name":"2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133116486","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: 1

About the Effectiveness of the Statistical Sequential Analysis in the Reliability Trials 论统计序列分析在信度试验中的有效性

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.83

Grodzenskiy Sergey, Grodzenskiy Yakov, Chesalin Alexander

引用次数: 5

Solving Control Problems with Linear State Dynamics - A Practical User Guide 解决控制问题与线性状态动力学-一个实用的用户指南

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.103

Juri Hinz, Jeremy Yee

引用次数: 6

Stochastic Network Reduction Technique for Calculations of Electrical Power System Structural Reliability 电力系统结构可靠度计算的随机网络约简技术

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.33

V. Oboskalov, S. Timashev, A. Bushinskaya, S. Gusev

{"title":"Stochastic Network Reduction Technique for Calculations of Electrical Power System Structural Reliability","authors":"V. Oboskalov, S. Timashev, A. Bushinskaya, S. Gusev","doi":"10.1109/SMRLO.2016.33","DOIUrl":"https://doi.org/10.1109/SMRLO.2016.33","url":null,"abstract":"This paper considers calculation of structural reliability indices (SRI) for electrical power system (EPS), when \"cutoff\" failures are taken into account. Calculations are based on \"exclusion-renewal\" technique. In this paper, the design scheme slightly differs from the electrical scheme. Stochastic network reduction (SNR) technique is considered as the main tool for network reduction. SNR also accounts for directional elements, which have different SRI in forward and backward direction of power flow. The paper shows that difference in resulting SRI of diverse software solutions (SS) arise from different approaches to SNR of series-connected elements. The article compares \"p\" and \"gamma\" network reduction techniques (NRT). Taking into account specific nature of EPS, the paper proposes the \"gamma\" NRT for practical calculations of the EPS structural reliability. The paper also considers implementation of the rhomb NRT at the stage of renewal of the rank 2 nodes. This method is based on parallel calculus of the SRI, which permits substantial speed-up of the design procedure.","PeriodicalId":254910,"journal":{"name":"2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117060189","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

Parallel Clustering of Protein Structures Generated via Stochastic Monte Carlo 基于随机蒙特卡罗生成的蛋白质结构并行聚类

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.71

S. Dexter, Gavriel Yarmish, Philip Listowsky

引用次数: 1

An Algorithms for Solving Extended Bin Packing Problem for Efficient Storage of Digital Information 面向数字信息高效存储的扩展装箱问题求解算法

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.107

Svetlana Daichman, B. Efros

引用次数: 0

Statistical Analysis in the DEA Context DEA背景下的统计分析

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.82

Z. Sinuany-Stern, Lea Friedman

{"title":"Statistical Analysis in the DEA Context","authors":"Z. Sinuany-Stern, Lea Friedman","doi":"10.1109/SMRLO.2016.82","DOIUrl":"https://doi.org/10.1109/SMRLO.2016.82","url":null,"abstract":"This paper deals with Data Envelopment Analysis (DEA), where we have several organizational units or Decision Making Units -- DMUs. Each DMU has multiple inputs and multiple outputs. DEA calculates the relative efficiencies of DMUs via linear programming. Various versions of DEA were developed. Although DEA is a deterministic model, during the last two decades statistical methods are used in three main dimensions: 1. In preparing the input and output data and DMUs, 2. As a stochastic alternative to derive DMUs efficiencies, 3. As a second stage after the efficiencies are derived to test the relationship between the efficiency and various environmental parameters. Our paper explores the use of the various statistical methods in the DEA context covering these three main dimensions. The major statistical methods we present are: comparisons including parametric and non-parametric tests, correlation and regression, analyses of variance, multivariate analyses, and bootstrapping. Examples from the literature, using various statistical methods in the DEA context, will be presented along the above three dimensions.","PeriodicalId":254910,"journal":{"name":"2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123479053","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

Availability and Unloading Capacity Assessment of Multi-state Material Handling System, Operate in a Stochastic Environment and Material Handling Stochastic Demand 多状态物料搬运系统的可用性和卸载能力评估，随机环境下的运行和物料搬运的随机需求

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.64

Sagi Finish, Marina Felshin, I. Frenkel, L. Khvatskin

{"title":"Availability and Unloading Capacity Assessment of Multi-state Material Handling System, Operate in a Stochastic Environment and Material Handling Stochastic Demand","authors":"Sagi Finish, Marina Felshin, I. Frenkel, L. Khvatskin","doi":"10.1109/SMRLO.2016.64","DOIUrl":"https://doi.org/10.1109/SMRLO.2016.64","url":null,"abstract":"We present availability and unloading capacity assessment of multi-state material handling system, operate in a stochastic environment and investigate an impact of stochastic demands for material handling. In order to determine the system availability and unloading capacity we constructed Markov models, representing the various unloading capacity levels of each element and sub-system in the Material Handling System. The entire system can be represented as Markov model with 96 different states expressing the different performance levels of the entire process. The stochastic demands for material handling is described as three level Markov model, typical for such environment. The entire Markov model is described as system with 288 differential equations, solution of which is complicated problem. To overcome this obstacle we propose an application of the Lz-transform method for availability and unloading capacity assessment of multi-state Material Handling System (MSMHS). We demonstrated that the suggested method can be implemented in engineering decision making and construction of various MSS systems related to requirements, unloading capacity and production processes.","PeriodicalId":254910,"journal":{"name":"2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO)","volume":"12 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124133612","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

Operating Reliability Analysis of Peaking Generating Units Considering Start-Up Failures and Degradation 考虑启动失效和退化的调峰机组运行可靠性分析

2016 Second International Symposium on Stochastic Models in Reliability Engineering, Life Science and Operations Management (SMRLO) Pub Date : 2016-02-15 DOI: 10.1109/SMRLO.2016.37

Yuan Yan, Yi Ding, Chuangxin Guo, R. Wang, Lin Cheng, Yuanzhan Sun

引用次数: 3