IEEE Transactions on Reliability (IEEE T RELIAB)

Publisher: Institute of Electrical and Electronics Engineers. Professional Technical Group on Reliability; IEEE Reliability Group; IEEE Reliability Society; American Society for Quality Control. Electronics Division, Institute of Electrical and Electronics Engineers

Journal description

The principles and practices of reliability, maintainability, and product liability pertaining to electrical and electronic equipment.

Current impact factor: 1.93

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 1.934
2013 Impact Factor 1.657
2012 Impact Factor 2.293
2011 Impact Factor 1.285
2010 Impact Factor 1.288
2009 Impact Factor 1.331
2008 Impact Factor 1.315
2007 Impact Factor 1.303
2006 Impact Factor 0.8
2005 Impact Factor 0.715
2004 Impact Factor 0.828
2003 Impact Factor 0.444
2002 Impact Factor 0.522
2001 Impact Factor 0.477
2000 Impact Factor 0.358
1999 Impact Factor 0.341
1998 Impact Factor 0.255
1997 Impact Factor 0.355
1996 Impact Factor 0.369
1995 Impact Factor 0.304
1994 Impact Factor 0.45
1993 Impact Factor 0.332
1992 Impact Factor 0.407

Impact factor over time

Impact factor

Additional details

5-year impact 2.19
Cited half-life >10.0
Immediacy index 0.19
Eigenfactor 0.01
Article influence 0.85
Website IEEE Transactions on Reliability website
Other titles IEEE transactions on reliability, Institute of Electrical and Electronics Engineers transactions on reliability, Transactions on reliability, Reliability
ISSN 0018-9529
OCLC 1752560
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Institute of Electrical and Electronics Engineers

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
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  • Classification

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a new replacement policy for a deteriorating item from a mixed population is suggested. The item is randomly selected from a mixed population composed of two stochastically ordered subpopulations and the corresponding subpopulation from which the item is selected is unknown. As the operational history contains the information about the subpopulation from which the item is selected, it is reasonable to employ this information in determining the replacement policy. In the new replacement policy, the age at which the item will be replaced is not determined before the operation of the item, but it is determined based on the failure/repair history of the item observed during the initial operation. It is shown that the proposed replacement policy outperforms the ordinary periodic replacement policy. The properties of the optimal replacement policy will be studied in detail.
    No preview · Article · Jan 2016 · IEEE Transactions on Reliability
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    ABSTRACT: Malicious Input through Buffer Overflow (MiBO) vulnerabilities play important roles in cyber security. To identify MiBO vulnerabilities, white-box testing approaches analyze instructions in all possible execution paths. Black-box testing approaches try to trigger MiBO vulnerabilities using different inputs. However, only limited coverage can be achieved: the identified MiBO vulnerabilities, when being “hit” by a test input, must cause exceptions (e.g., crashes). Type information could help to catch the non-crash MiBO vulnerabilities, but such information is not contained in binary code. In this paper, we present a white-box fuzzing method to detect non-crash MiBO vulnerabilities. Without source code, we dynamically discover likely memory layouts to help the fuzzing process. This is very challenging since memory addresses and layouts keep changing with the running of software. In different executions with different inputs, the layouts may also change. To address these challenges, we selectively analyze memory operations to identify memory layouts. If a buffer border identified from the memory layout is exceeded, an error will be reported. The fuzzing results will be compared with the layout for future input generation, which greatly increases the opportunity to expose MiBO vulnerabilities. We implemented a prototype called ArtFuzz and performed several evaluations. ArtFuzz discovered 23 real MiBO vulnerabilities (including 8 zero-day MiBO vulnerabilities) in nine applications.
    No preview · Article · Jan 2016 · IEEE Transactions on Reliability
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    ABSTRACT: Fault tree analysis (FTA) is a method of analyzing and visualizing the causes of a fault using a fault tree diagram (FT diagram), which has a tree structure with logical steps. Design engineers developing a new product generally use FTA to analyze many fault events, calculate their probability, and include redundancy systems in the design process. Furthermore, FTA has been used to analyze problems with products and to prevent the occurrence of problems in the design phase. In particular, it is necessary for design engineers to analyze the events after a failure to determine the root causes of the failure of the redundancy systems. However, it is not easy for design engineers to produce an accurate FT diagram in the actual design process. We have developed a computer-aided knowledge management system for creating FT diagrams (FTAid) as part of a collaborative group (The University of Tokyo, National Institute of Advanced Industrial Science and Technology (AIST), and Jatco Ltd.). This system has been verified by the design engineers of Jatco Ltd. in actual product development. We report its effectiveness for predicting mechanical, electrical, and heat transfer failure, the verification of the system, and its validation in an actual design process. We conclude that the system can help design engineers to effectively and efficiently create FT diagrams in reliability engineering, although some existing ability in FTA and engineering is required. We also describe some outstanding issues regarding the improvement of FTAid, engineering education, and ensuring reliability.
    No preview · Article · Jan 2016 · IEEE Transactions on Reliability
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    ABSTRACT: Remaining useful life (RUL) estimation plays a vital role in the prognostics and health management of degrading systems. For complicated degrading systems, the associated degradation processes are not only subjected to the nonlinearity in the degradation evolving paths but are also influenced by three important sources of variability, i.e., temporal variability, unit-to-unit variability, and measurement variability. However, current studies do not consider the above key factors jointly. Toward this end, this paper presents a general nonlinear degradation model to characterize the degradation nonlinearity and the three-source variability simultaneously. By constructing a state-space model and applying the Kalman filtering technique, we present the method of the RUL estimate with three-source variability and derive the analytical form of the probability density function of the RUL with three-source variability and the degradation nonlinearity approximately, which can be real-time updated with the available observations. As such, the effects of the degradation nonlinearity and three-source variability are propagated into the RUL estimate. In addition, the unknown parameters of the presented nonlinear model are estimated using the maximum likelihood estimation approach. For demonstrating the presented approach, comparative studies are conducted. The results verify that the proposed approach improves the model fitting and the accuracy of the RUL estimate.
    No preview · Article · Jan 2016 · IEEE Transactions on Reliability
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    ABSTRACT: In this paper, how to estimate residual useful lifetime (RUL) is discussed based on a model-based method when fluctuations exist in the degradation process around its average behavior. The main idea is to model the degradation process as a time-dependent Ornstein-Uhlenbeck (OU) process, where the first passage failure is adopted to consider corresponding RUL estimation. The time-dependent OU process is proved good by its statistical properties on the controllable mean, variance, and correlation. Its mean-reverting property is introduced to interpret temporary correlated fluctuations from an overall degrading trend in degradation records. The corresponding parameter estimation is proposed based on the maximum likelihood estimation method, and a Volterra integral equation of second kind with a non-singular kernel is then considered to calculate the probability density function (pdf) of failure time. Proposed methods are tested in a case study, where results are compared with a nonlinear-drift, linear-diffusion process.
    No preview · Article · Jan 2016 · IEEE Transactions on Reliability

  • No preview · Article · Jan 2016 · IEEE Transactions on Reliability

  • No preview · Article · Jan 2016 · IEEE Transactions on Reliability

  • No preview · Article · Jan 2016 · IEEE Transactions on Reliability

  • No preview · Article · Jan 2016 · IEEE Transactions on Reliability

  • No preview · Article · Jan 2016 · IEEE Transactions on Reliability
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    ABSTRACT: Univariate Birnbaum-Saunders models have been widely applied to fatigue studies. Calculation of fatigue life is of great importance in determining the reliability of materials. We propose and derive new multivariate generalized Birnbaum-Saunders regression models. We use the maximum likelihood method and the EM algorithm to estimate their parameters. We carry out a simulation study to evaluate the performance of the corresponding maximum likelihood estimators. We illustrate the new models with real-world multivariate fatigue data.
    No preview · Article · Dec 2015 · IEEE Transactions on Reliability
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    ABSTRACT: For a hybrid flow shop (HFS), due to the maintenance, partial failure, and possibility of failure, the number of machines at a workstation presents multiple levels, meaning that capacity of each workstation is stochastic. This study considers the stochastic capacity of each workstation and proposes a performance index based on system reliability to measure the probability that the HFS can complete demand d within time constraint T . The HFS is modeled as a stochastic-flow network, in which each arc is regarded as a workstation with stochastic capacity and each node as a buffer. An algorithm is then developed to find the lower capacity vectors that satisfy (d, T) and system reliability is evaluated. In addition, a practical example of an IC card manufacturing system is utilized to illustrate the proposed algorithm and show that such an index can provide enough information for managers to make decisions.
    No preview · Article · Dec 2015 · IEEE Transactions on Reliability
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    ABSTRACT: This paper models a hybrid standby system subject to periodic inspections and condition-based standby mode transfers during a mission. At the beginning of the mission only one element is online and operating. The second element waits in a hot standby mode being ready to replace the failed online element at any time. Other elements wait in less-stressful and less-costly warm standby mode. During the mission periodic inspections are performed for checking conditions of the online and hot standby elements and subsequently triggering necessary mode transfer(s) of available warm standby element(s) to replace the failed hot standby element and/or online element. We suggest an efficient numerical method to assess availability and expected total mission cost (including standby cost, operation cost and mode transfer cost of system elements, inspection cost, system interruption or idle cost) of the considered system. The algorithm is flexible and applicable to arbitrary type of time-to-failure distributions. Then we formulate and solve new optimization problems that identify the optimal combination of inter-inspection interval and element activation sequence to minimize expected total mission cost while satisfying a certain constraint on system availability. As illustrated through examples, the optimization results can facilitate cost-effective and availability-aware planning of system inspection and operation.
    No preview · Article · Dec 2015 · IEEE Transactions on Reliability
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    ABSTRACT: This paper models a real-time warm standby system that has to accomplish a specified amount of task by a hard deadline. The system is subject to corrective replacements (CRs) upon failure of its operating element. It can also be renewed according to a predetermined schedule through preventive replacements (PRs). To facilitate an effective recovery of system operation after replacements, periodic backups are performed so that warm standby elements, upon being activated, can take over the mission task from the last backup point instead of from scratch. This paper presents a novel integrated model that considers effects of periodic backups, CRs and PRs in analyzing and optimizing real-time warm standby systems. Mission success probability and expected mission completion time are evaluated. Impacts of different mission and element parameters on mission success probability, optimal backup and PR policies, and optimal element activation sequence are investigated. It is shown that in warm standby systems with periodic backups and tight deadlines, PRs can improve the mission success probability even when they take the same time as CRs. When the maximum allowed mission time exceeds a certain level, PRs become ineffective and the optimal policy can involve only periodic backups.
    No preview · Article · Dec 2015 · IEEE Transactions on Reliability
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    ABSTRACT: Power electronics are widely used in the transport and energy sectors. Hence, the reliability of these power electronic components is critical to reducing the maintenance cost of these assets. It is vital that the health of these components is monitored for increasing the safety and availability of a system. The aim of this paper is to develop a prognostic technique for estimating the remaining useful life (RUL) of power electronic components. There is a need for an efficient prognostic algorithm that is embeddable and able to support on-board real-time decision-making. A time delay neural network (TDNN) is used in the development of failure modes for an insulated gate bipolar transistor (IGBT). Initially, the time delay neural network is constructed from training IGBTs' ageing samples. A stochastic process is performed for the estimation results to compute the probability of the health state during the degradation process. The proposed TDNN fusion with a statistical approach benefits the probability distribution function by improving the accuracy of the results of the TDDN in RUL prediction. The RUL (i.e., mean and confidence bounds) is then calculated from the simulation of the estimated degradation states. The prognostic results are evaluated using root mean square error (RMSE) and relative accuracy (RA) prognostic evaluation metrics.
    No preview · Article · Dec 2015 · IEEE Transactions on Reliability
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    ABSTRACT: We consider a flow network with directed links and three types of nodes: inflow points, transit-only nodes, and outflow points. Its structure can be reduced to a single component by series-parallel aggregation. The components are repairable, they have constant failure and repair rates, and their states are mutually s-independent. Each operable component has an integer throughput; a failed one has zero throughput. The network's performance is measured by the total demand satisfied (TDS) at all the outflow points vs. the total demand required (TDR) at these points. TDS is a r.v. with values in the [0, TDR] interval, where TDR is a constant. The distribution function of TDS is thus the network's basic reliability characteristic. It is computed by an author-developed algorithm operating on integer numbers, its complexity being polynomial w.r.t. certain further defined quantities. A few other reliability parameters, characterizing the dynamically changing ability to satisfy the demands at the outflow points, are also defined. They are calculated using the distribution function of TDS and the d-importances of individual components (a generalization of the Birnbaum importance, defined further in the paper). The presented results can be applied in the reliability analysis of water supply networks, oil or gas pipeline systems, power transmission and/or distribution networks, etc.
    No preview · Article · Dec 2015 · IEEE Transactions on Reliability
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    ABSTRACT: The performance of the system is often influenced by the performance of a particular component. Hence, it is important to identify the state changing of which component dominates the system performance changing in a maintenance process. Motivated by the Griffith importance measure (GIM) model, this paper proposed the generalized GIM, which extends the application of GIM to complex multi-state components, to evaluate the accurate contribution of the components in the changing of the system performance by considering the transition probabilities of the states of each component. Furthermore, the generalized GIM method is successfully applied in the continuous-state systems by extending the system structure function in GIM model. As a result, the expression of the performance of continuous system and the generalized GIM of the continuous-state components are proposed. A numerical example and an application to an oil transportation system are presented to illustrate how the proposed method works.
    No preview · Article · Dec 2015 · IEEE Transactions on Reliability