<![CDATA[ IEEE Transactions on Reliability - new TOC ]]>
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TOC Alert for Publication# 24 2017December 11<![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]]>Stochastic “Output $geq$ demand” Networks and their Generation). Based on all examples studied in this paper, the absolute difference ratios between the previous incorrect (as published) and the correct probabilities (from the Song rule) are all greater than 23%. The proposed Song rule analysis is verified to be correct using discrete-event simulation. In addition to providing rigorous analysis for the network problems under consideration, the Song rule is a useful tool for assessing the validity of any future proposed approach for other stochastic reliability problems.]]>664101210241189<![CDATA[Reliability Assessment of Multiprocessor System Based on $(n,k)$-Star Network]]>$(n,k)$ -star graph, as an empirical object. In order to measure the reliability of $(n,k)$-star graph, the analytical model introduces mean time to failure (MTTF) to show the time that the appearance of a certain number of faulty $S_{n-1,k-1}$ 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]]>

Two mixed-integer nonlinear programming models are introduced to address the problem described above. The first model focuses on determining the optimal inspection policy that maximizes the reliability of the system over its useful life subject to a minimal threshold value of this reliability term. The second model determines the optimal policy that minimizes the system downtime. The two models are solved using a branch-and-cut global optimization approach. Two separate numerical studies are conducted to demonstrate the performance of the models and validate it through benchmarking these results against a prior study in the literature.]]>
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]]>Z-score based Euclidean distance is adopted to estimate the difference between solutions. Finally, the above methods are evaluated and the experiments show several results. 1) The developed heuristics for constraint handling are better than the Existing Strategy in terms of the capacity and coverage values. 2) The Z-score operation obtains better diversity values and reduces repeated solutions. 3) The modified NSGA-II for OTRAPs (called NSGA-II-TRA) performs significantly better than the existing MOEAs in terms of capacity and coverage values, which suggests that NSGA-II-TRA could obtain more and higher quality testing-time-allocation schemes, especially for large, complex datasets. 4) NSGA-II-TRA is robust according to the sensitivity analysis results.]]>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]]>et al. is discovered and reported in this paper. The work of Gebraeel et al. is extended to a more general prognostic method. Simulation and experimental case studies are investigated to illustrate how the proposed model works. Comparisons with the statistical model proposed by Gebraeel et al. for bearing remaining useful life prediction are conducted to highlight the superiority of the proposed statistical model.]]>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]]>$T$ time unit, and is correctively repaired at failure. The corrective repair is a minimal repair that just restores the system to work, while the PM results in a GGP, i.e., the lifetime sequence of a system after PM constitute a decreasing GGP. The long-run average cost rate $C(N,T)$ of the system is derived, and the optimal bivariate policy $(N^*,T^*)$ is determined by minimizing $C(N,T)$, where $N$ is the number of PMs before replacement. Next, a sequential PM model is investigated for a system in which a sequential time interval to be determined for PM. An algorithm is given to seek the optimal replacement policy $N^*$ and the optimal time intervals $T_1^*,T_2^*,ldots, T_N^*$ 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[IEEE Transactions on Reliability institutional listings]]>664C3C32541<![CDATA[IEEE Transactions on Reliability institutional listings]]>664C4C4763