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: 1.66

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013/2014 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
Year

Additional details

5-year impact 2.07
Cited half-life 0.00
Immediacy index 0.21
Eigenfactor 0.00
Article influence 0.76
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
    • Author can archive a post-print version
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    • Author's pre-print must be accompanied with set-phrase, when accepted by IEEE for publication ("(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")
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    • Author's Post-print - Must link to publisher version with DOI
    • Publisher's version/PDF cannot be used
    • Publisher copyright and source must be acknowledged
  • Classification
    ‚Äč green

Publications in this journal

  • IEEE Transactions on Reliability 12/2014; 63(4):817-829.
  • IEEE Transactions on Reliability 12/2014; 63(4):850-857.
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    ABSTRACT: Performance deterioration monitoring is an essential part of the prognostics and health management (PHM) of gas turbine engines (GTEs). This paper proposes a physics-based modeling approach for performance deterioration monitoring with two model-based performance indicators, heat loss index and power deficit index, for GTE PHM applications. A comprehensive nonlinear thermodynamic model for a single shaft GTE is developed to establish the relation between the operating conditions and the cycle parameters. The model, once properly calibrated, is able to predict the GTE cycle parameters in a healthy condition as the baseline, while in reality, the measured parameters gradually deviate from the baseline, which reflects the performance deterioration of the GTE. To represent the degradation level, the heat loss index is defined as the normalized measure of the thermal power that is being wasted in the GTE compared to the healthy condition. Similarly, the power deficit index is defined as the deficiency ratio of the GTE output power due to the performance deterioration. The effectiveness of the performance indicators in monitoring performance deterioration and their robustness to the variations of the operating conditions are examined by using three years of typical operating data of an industrial GTE. The results clearly reveal the trends of both the short term recoverable deterioration due to fouling effects in the compressor, and the long term non-recoverable deterioration caused by structural degradation. The technique is especially advantageous for prognostic applications where there is no access to internal cycle parameters of a GTE, and only the operating data are available, hence no additional sensors are required.
    IEEE Transactions on Reliability 11/2014; PP(99):1-9.
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    ABSTRACT: Motivated by an industrial problem affecting a water utility, we develop a model for a load sharing system where an operator dispatches work load to components in a manner that manages their degradation. We assume degradation is the dominant failure type, and that the system will not be subject to sudden failure due to a shock. By deriving the time to degradation failure of the system, estimates of system probability of failure are generated, and optimal designs can be obtained to minimize the long run average cost of a future system. The model can be used to support asset maintenance and design decisions. Our model is developed under a common set of core assumptions. That is, the operator allocates work to balance the level of the degradation condition of all components to achieve system performance. A system is assumed to be replaced when the cumulative work load reaches some random threshold. We adopt cumulative work load as the measure of total usage because it represents the primary cause of component degradation. We model the cumulative work load of the system as a monotone increasing and stationary stochastic process. The cumulative work load to degradation failure of a component is assumed to be inverse Gaussian distributed. An example, informed by an industry problem, is presented to illustrate the application of the model under different operating scenarios.
    IEEE Transactions on Reliability 09/2014; 63(3):721-730.
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    ABSTRACT: Constant-stress procedures based on parametric lifetime distributions and models are often used for accelerated life testing in product reliability experiments. Maximum likelihood estimation (MLE) is the typical statistical inference method. This paper presents a new inference method, named the random variable transformation (RVT) method, for Weibull constant-stress accelerated life tests with progressively Type-II right censoring (including ordinary Type-II right censoring). A two-parameter Weibull life distribution with a scale parameter that is a log-linear function of stress is used. RVT inference life distribution parameters and the log-linear function coefficients are provided. Exact confidence intervals for these parameters are also explored. Numerical comparisons of RVT-based estimates to MLE show that the proposed RVT inference is promising, in particular for small sample sizes.
    IEEE Transactions on Reliability 09/2014; 63(3):807-815.
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    ABSTRACT: Competing failure is an important reliability topic. Thus, the study of the statistical inference of accelerated life testing (ALT) with competing failures is of great significance. In contrast to the previous related studies that assumed that all competing failure modes were cross statistically independent, we study the statistical inference method for ALT considering the statistical dependence of the competing failure modes based on copula theory. With the copula function, we construct the statistically dependent relationship between the margin distributions of the competing failure modes and their joint distribution, and find the maximum likelihood estimation (MLE) model for the parameter estimations. We also present a simple engineering-based multi-dimensional copula construction method applied in the statistical inference for ALT with statistically dependent competing failure modes. The results and analysis of the case studies indicate that the statistical inference models and the multi-dimensional copula construction method derived in this article are not only correct and feasible but have good accuracy and universality. We have provided an effective, universally applicable method for the statistical inference of ALT in situations involving competing failures, statistically independent or dependent, which is of great significance to evaluating a product's lifetime in ALT.
    IEEE Transactions on Reliability 09/2014; 63(3):764-780.
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    ABSTRACT: Bayesian analysis of the series system failure data under step-stress accelerating life testing is proposed when the cause of failure may not have been identified but has only been narrowed down to a subset of all potential risks. A general Bayesian formulation is investigated for the log-location-scale distribution family that includes most commonly used parametric lifetime distributions. Reparameterization is introduced for estimating the lifetime under the use condition stress and other parameters directly. The posterior analysis is done by Markov chain Monte Carlo sampling. The methodology is illustrated through the Weibull distributions, and a numerical example.
    IEEE Transactions on Reliability 09/2014; 63(3):798-806.
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    ABSTRACT: In this paper, we focus on the reliability and availability analysis of Web service (WS) compositions, orchestrated via the Business Process Execution Language (BPEL). Starting from the failure profiles of the services being composed, which take into account multiple possible failure modes, latent errors, and propagation effects, and from a BPEL process description, we provide an analytical technique for evaluating the composite process' reliability-availability metrics. This technique also takes into account BPEL's advanced composition features, including fault, compensation, termination, and event handling. The method is a design-time aid that can help users and third party providers reason, in the early stages of development, and in particular during WS selection, about a process' reliability and availability. A non-trivial case study in the area of travel management is used to illustrate the applicability and effectiveness of the proposed approach.
    IEEE Transactions on Reliability 09/2014; 63(3):689-705.
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    ABSTRACT: In this paper, the degradation based reliability demonstration test (RDT) plan design problems for long life products under a small sample circumstance are studied. Fixed sample method, sequential probability ratio test (SPRT) method, and sequential Bayesian decision method are provided based on univariate degradation testing. The simulation examples show the superiority of degradation based RDT methods compared with the traditional failure based methods, and the sequential-type methods have more test power than their fixed sample counterparts. The test power can be further improved by combining the test data of a reliability indicator with the data of its marker, based on which the bivariate fixed sample method and the sequential Bayesian decision method are defined. The simulation study shows the benefit from the combination. The degradation based RDT plan optimization model, and the corresponding searching-based solution algorithm using some heuristic rules discovered in the paper, are also presented. The case study of Rubidium Atomic Frequency Standard with a RDT plan design demonstrates the effectiveness of our methods on overcoming the difficulties of small samples in reliability demonstration of long life products.
    IEEE Transactions on Reliability 09/2014; 63(3):781-797.
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    ABSTRACT: Modeling statistical dependence between two systems or components is an important problem in reliability theory. Such a problem has been well studied for binary systems and components. In the present paper, we provide a way for modeling s-dependence between two multi-state components. Our method is based on the use of copulas which are very popular for modeling s-dependence. We obtain expressions for the joint state probabilities of the two components, and illustrate the results for the case when the degradation in both components follows a Markov process.
    IEEE Transactions on Reliability 09/2014; 63(3):715-720.
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    ABSTRACT: We model the load sharing phenomenon in a $k$-out-of- $m$ system through the accelerated failure time model. This model leads to multivariate families of distributions for ordered random variables, which are particular cases of the sequential order statistics. For illustrative purpose, we discuss the model, and the estimation problem for a two component parallel system under the setting of a linear failure rate distribution. In this set up, we discuss a test for the hypothesis that the failure times of components are statistically independent against the alternative that they show the load sharing phenomenon. We report simulation studies showing the performance of the estimators, and the test procedure. The test is also applied to two data sets for illustrative purpose.
    IEEE Transactions on Reliability 09/2014; 63(3):706-714.
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    ABSTRACT: The IG process models have been shown to be an important family in degradation analysis. In this paper, we are interested in optimal constant-stress accelerated degradation tests (ADTs) planning when the underlying degradation follows the inverse Gaussian (IG) process. We first consider ADT planning for the IG process without random effects. Asymptotic variance of the estimate of a lower quantile is derived, and the objective of the planning is to minimize this variance by properly choosing the testing stresses, and the number of samples allocated to each stress. Next, ADT planning for a random-effects IG process model is considered. We then applied the IG process to fit the stress relaxation data of a component, and use the developed methods to help with the optimal ADT design.
    IEEE Transactions on Reliability 09/2014; 63(3):750-763.
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    ABSTRACT: Software is currently a key part of many safety-critical and life-critical application systems. People always need easy- and instinctive-to-use software, but the biggest challenge for software engineers is how to develop software with high reliability in a timely manner. To assure quality, and to assess the reliability of software products, many software reliability growth models (SRGMs) have been proposed in the past three decades. The practical problem is that sometimes these selected SRGMs by companies or software practitioners disagree in their reliability predictions, while no single model can be trusted to provide consistently accurate results across various applications. Consequently, some researchers have proposed to use combinational models for improving the prediction capability of software reliability. In this paper, three enhanced weighted-combinations, namely weighted arithmetic, weighted geometric, and weighted harmonic combinations, are proposed. To solve the problem of determining proper weights for model combinations, we further study how to incorporate enhanced genetic algorithms (EGAs) with several efficient operators into weighted assignments. Experiments are performed based on real software failure data, and numerical results show that our proposed models are flexible enough to depict various software development environments. Finally, some management metrics are presented to both assure software quality and determine the optimal release strategy of software products under development.
    IEEE Transactions on Reliability 09/2014; 63(3):731-749.
  • IEEE Transactions on Reliability 08/2014; 99:1-15.