<![CDATA[ IEEE Transactions on Reliability - new TOC ]]>
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TOC Alert for Publication# 24 2018February 22<![CDATA[Table of Contents]]>664C196589<![CDATA[IEEE Transactions on Reliability publication information]]>664C2C2116<![CDATA[The Impact of Soft Error Event Topography on the Reliability of Computer Memories]]>664966979962<![CDATA[Optimization of Component Allocation/Distribution and Sequencing in Warm Standby Series-Parallel Systems]]>664980988375<![CDATA[Accelerated Life Testing With Semiparametric Modeling of Stress Effects]]>664989996422<![CDATA[Optimal Sequential ALT Plans for Systems With Mixture of One-Shot Units]]>6649971011452<![CDATA[The Song Rule as a Validator of Analytical Results—A Note Correcting System Reliability Results in a Review of the Literature]]>664101210241189<![CDATA[Reliability Assessment of Multiprocessor System Based on $(n,k)$-Star Network]]>n-1,k-1 costs. The higher the MTTF, the better the robustness. So, the way to evaluate the robustness of an (n, k)-star is to count how much the MTTF is. In fact, an (n, k)-star can be partitioned along any dimension (except the first one) with corresponding identification code. So, we will explore the reliability of (n, k)-star graph when it is partitioned along any dimension (except the first one) under node and/or link fault model. Comparisons among the simulation results under two partitioning models reveal that the MTTF is higher under liberal partition model, which better reflect the steady state of an interconnection network that can persist when the network is destroyed.]]>66410251035452<![CDATA[Reliability Assessment of Hierarchical Systems With Incomplete Mixed Data]]>66410361047673<![CDATA[Machine Learning Model for Event-Based Prognostics in Gas Circulator Condition Monitoring]]>2 gas through the reactor core. The ongoing maintenance and examination of these machines are important for operators in order to maintain safe and economic generation. GCs experience a dynamic duty cycle with periods of nonsteady state behavior at regular refueling intervals, posing a unique analysis problem for reliability engineers. In line with the increased data volumes and sophistication of available technologies, the investigation of predictive and prognostic measurements has become a central interest in rotating asset condition monitoring. However, many of the state-of-the-art approaches finding success deal with the extrapolation of stationary time series feeds, with little to no consideration of more complex but expected events in the data. In this paper, we demonstrate a novel modeling approach for examining refueling behaviors in GCs, with a focus on estimating their health state from vibration data. A machine learning model was constructed using the operational history of a unit experiencing an eventual inspection-based failure. This new approach to examining GC condition is shown to correspond well with explicit remaining useful life measurements of the case study, improving on the existing rudimentary extrapolation methods often employed in rotating machinery health monitoring.]]>66410481057892<![CDATA[Failure Mode and Effect Analysis Using Cloud Model Theory and PROMETHEE Method]]>66410581072540<![CDATA[A Delay Time Model With Multiple Defect Types and Multiple Inspection Methods]]>66410731084600<![CDATA[Pattern Analysis Framework With Graphical Indices for Condition-Based Monitoring]]>664108511007030<![CDATA[A State Transfer Scheduling Optimization Framework for Standby Systems]]>664110111091054<![CDATA[Impact of the Real-Time Thermal Loading on the Bulk Electric System Reliability]]>66411101119742<![CDATA[A Graphical Model Based on Performance Shaping Factors for Assessing Human Reliability]]>664112011431846<![CDATA[A Unified Framework for Evaluating Supply Chain Reliability and Resilience]]>66411441156603<![CDATA[A Comprehensive Evaluation of Software Rejuvenation Policies for Transaction Systems With Markovian Arrivals]]>664115711772509<![CDATA[Random Additive Signature Monitoring for Control Flow Error Detection]]>664117811922303<![CDATA[Constraint Handling in NSGA-II for Solving Optimal Testing Resource Allocation Problems]]>664119312121116<![CDATA[Code Coverage and Postrelease Defects: A Large-Scale Study on Open Source Projects]]>66412131228737<![CDATA[Void Formation and Their Effect on Reliability of Lead-Free Solder Joints on MID and PCB Substrates]]>664122912371726<![CDATA[Reliability Analysis of Ethernet Ring Mesh Networks]]>66412381252800<![CDATA[Dynamic Defense Resource Allocation for Minimizing Unsupplied Demand in Cyber-Physical Systems Against Uncertain Attacks]]>664125312652728<![CDATA[A Two-Stage Latent Variable Estimation Procedure for Time-Censored Accelerated Degradation Tests]]>66412661279354<![CDATA[Maintenance Scheduling for Multicomponent Systems with Hidden Failures]]>66412801292972<![CDATA[A Prognostic Model for Stochastic Degrading Systems With State Recovery: Application to Li-Ion Batteries]]>66412931308739<![CDATA[Maintenance Strategy Optimization for Complex Power Systems Susceptible to Maintenance Delays and Operational Dynamics]]>664130913302495<![CDATA[Statistical Modeling of Bearing Degradation Signals]]>664133113441343<![CDATA[Two-Phase Degradation Process Model With Abrupt Jump at Change Point Governed by Wiener Process]]>66413451360646<![CDATA[Online Estimation Methods for the Gamma Degradation Process]]>66413611367344<![CDATA[Remaining Useful Life Prediction for Degradation Processes With Long-Range Dependence]]>66413681379677<![CDATA[Preventive Maintenance Models Based on the Generalized Geometric Process]]>1*, T_{2}*,..., T_{N}* between PMs. By assuming that the lifetime of the system is Weibull distributed, the optimal policy is obtained explicitly. In both models, numerical examples are provided to verify the effectiveness of the approaches developed.]]>66413801388402<![CDATA[2017 Index IEEE Transactions on Reliability Vol. 66]]>66413891402181<![CDATA[IEEE Transactions on Reliability institutional listings]]>664C3C32541<![CDATA[IEEE Transactions on Reliability institutional listings]]>664C4C4763