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Reliability, IEEE Transactions on

Issue 4 • Date Dec. 2012

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Displaying Results 1 - 25 of 29
  • Table of Contents

    Page(s): C1
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  • IEEE Transactions on Reliability publication information

    Page(s): C2
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  • Editorial: Reduce Time-to-Market by Considering Reliability Tradeoffs

    Page(s): 837 - 845
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  • Methodology and Framework for Predicting Helicopter Rolling Element Bearing Failure

    Page(s): 846 - 857
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1171 KB) |  | HTML iconHTML  

    The enhanced ability to predict the remaining useful life of helicopter drive train components offers potential improvement in the safety, maintainability, and reliability of a helicopter fleet. Current existing helicopter health and usage monitoring systems provide diagnostic information that indicates when the condition of a drive train component is degraded; however, prediction techniques are not currently used. Although various algorithms exist for providing remaining life predictions, prognostic techniques have not fully matured. This particular study addresses remaining useful life predictions for the helicopter oil-cooler bearings. The paper proposes a general methodology of how to perform rolling element bearing prognostics, and presents the results using a robust regression curve fitting approach. The proposed methodology includes a series of processing steps prior to the prediction routine, including feature extraction, feature selection, and health assessment. This approach provides a framework for including prediction algorithms into existing health and usage monitoring systems. A case study with the data collected by Impact Technology, LLC. is analysed using the proposed methodology. Future work would consider using the same methodology, but comparing the accuracy of this prediction method with Bayesian filtering techniques, usage based methods, and other time series prediction methods. View full abstract»

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  • Extended Kalman Filter Models and Resistance Spectroscopy for Prognostication and Health Monitoring of Leadfree Electronics Under Vibration

    Page(s): 858 - 871
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1811 KB) |  | HTML iconHTML  

    A technique has been developed for monitoring the structural damage accrued in ball grid array interconnects during operation in vibration environments. The technique uses resistance spectroscopy based state space vectors, rate of change of the state variable, and acceleration of the state variable in conjunction with extended Kalman filter, and is intended for the pre-failure time-history of the component. Condition monitoring using the presented technique can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to bear. The methodology has been demonstrated on 96.5%Sn3.0%Ag0.5%Cu (SAC305) lead-free area-array electronic assemblies subjected to vibration. The future state of the system has been estimated based on a second order extended Kalman filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage using plastic strain. Performance of the prognostication health management algorithm during the vibration test has been quantified using performance evaluation metrics. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including α-λ metric, beta, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness. View full abstract»

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  • An Options Approach for Decision Support of Systems With Prognostic Capabilities

    Page(s): 872 - 883
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1385 KB) |  | HTML iconHTML  

    Safety, mission, and infrastructure critical systems are adopting prognostics and health management, a discipline consisting of technologies and methods to assess the reliability of a product in its actual life-cycle conditions to determine the advent of failure and mitigate system risks. The output from a prognostic system is the remaining useful life of the system; it gives the decision-maker lead-time and flexibility to manage the health of the system. This paper develops a decision support model based on options theory, a financial derivative tool extended to real assets, to valuate maintenance decisions after a remaining useful life prediction. We introduce maintenance options, and develop a hybrid methodology based on Monte Carlo simulations and decision trees for a cost-benefit-risk analysis of prognostics and health management. We extend the model, and combine it with least squares Monte Carlo methods to valuate one type of maintenance options, the waiting options; their value represents the cost avoidance opportunities and revenue obtained from running the system through its remaining useful life. The methodologies in this paper address the fundamental objective of system maintenance with prognostics: to maximize the use of the remaining useful life while concurrently minimizing the risk of failure. We demonstrate the methodologies on decision support for sustaining wind turbines by showing the value of having a prognostics system for gearboxes, and determining the value of waiting to perform maintenance. The value of the waiting option indicates that having the system available throughout the predicted remaining useful life is more beneficial than having downtime for maintenance, even if there is a high risk of failure. View full abstract»

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  • Decorrelated Feature Space and Neural Nets Based Framework for Failure Modes Clustering in Electronics Subjected to Mechanical Shock

    Page(s): 884 - 900
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4313 KB) |  | HTML iconHTML  

    Electronic systems under extreme shock and vibration environments may sustain several failure modes simultaneously. Previous experience indicates that the dominant failure modes experienced by packages in a drop and shock framework are in the solder interconnects including cracks at the package and the board interface, pad cratering, copper trace fatigue, and bulk-failure in the solder joint. In this paper, a method has been presented for failure mode classification using a combination of Karhunen Loéve transform with parity-based stepwise supervised training of a perceptrons. New is the early classification of multiple failure modes in the pre-failure space using supervised neural networks in conjunction with a Karhunen Loéve transform. The feature space has been formed by joint time frequency analysis. Because the cumulative damage may be accrued under repetitive loading with exposure to multiple shock events, the area array assemblies have been exposed to shock and feature vectors constructed to track damage initiation and progression. The error back propagation learning algorithm has been used for stepwise parity of each particular failure mode. The classified failure modes and failure regions belonging to each particular failure mode in the feature space are also validated by simulation of the designed neural network used for parity of feature space. Statistical similarity and validation of different classified dominant failure modes is performed by multivariate analysis of variance and Hotelling's T-square. The results of different classified dominant failure modes are also correlated with the experimental cross sections of the failed test assemblies. The methodology adopted in this paper can perform real-time fault monitoring with identification of a specific dominant failure mode, and is scalable to system level reliability. View full abstract»

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  • Magneto-Optic Imaging for Aircraft Skins Inspection: A Probability of Detection Study of Simulated and Experimental Image Data

    Page(s): 901 - 908
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    The increasing fleet of aging aircrafts has resulted in an increasing demand for cost effective nondestructive evaluation (NDE) techniques that are accurate, reliable, and easy to use. Magneto-Optic Imaging (MOI) is such a technique, which has gained wide acceptance for detection of both surface and subsurface defects in multi-layer aircraft structures. The main advantage of MOI is rapid inspection and ease of interpreting image data in contrast to complex impedance signals from conventional eddy current instruments. One missing piece of the puzzle for advanced MOI systems is how to quantitatively analyse the MO images, and understand the detectability limits when image data are acquired under varying operational conditions. This paper presents a probability of detection (POD) study that is conducted using both simulation model-predicted and experimental MO image data. Simulated panels from a 3-D FEM model and experimental panels with machined defects are used to generate data for interpretation by human inspectors or automated systems, and subsequently for POD studies. The POD curves demonstrate the merits in optimizing inspection parameters that maximized the performance of current MOI systems. Parameters quantifying the detectability of MO image data using skewness functions are also presented and discussed. View full abstract»

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  • Connexionist-Systems-Based Long Term Prediction Approaches for Prognostics

    Page(s): 909 - 920
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    Prognostics and Health Management aims at estimating the remaining useful life of a system (RUL) , i.e. the remaining time before a failure occurs. It benefits thereby from an increasing interest: prognostic estimates (and related decision-making processes) enable increasing availability and safety of industrial equipment while reducing costs. However, prognostics is generally based on a prediction step which, in the context of data-driven approaches as considered in this paper, can be hard to achieve because future outcomes are in essence difficult to estimate. Also, a prognostic system must perform sufficient long term estimates, whereas many works focus on short term predictions. Following that, the aim of this paper is to formalize and discuss the connexionist-systems-based approaches to ensure multi-step ahead predictions for prognostics. Five approaches are pointed out: the Iterative, Direct, DirRec, Parallel, and MISMO approaches. Conclusions of the paper are based, on one side, on a literature review; and on the other side, on simulations among 111 time series prediction problems, and among a real engine fault prognostics application. These experiments are performed using the exTS (evolving extended Takagi-Sugeno system). As for comparison purpose, three types of performances measures are used: prediction accuracy, complexity (computational time), and implementation requirements. Results show that all three criteria are never optimized at the same time (same experiment), and best practices for prognostics application are finally pointed out. View full abstract»

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  • A Multistate Physics Model of Component Degradation Based on Stochastic Petri Nets and Simulation

    Page(s): 921 - 931
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2101 KB) |  | HTML iconHTML  

    Multistate physics modeling (MSPM) of degradation processes is an approach proposed for estimating the failure probability of components and systems. This approach integrates multistate modeling, which describes the degradation process through transitions among discrete states (e.g., initial, microcrack, rupture, etc.), and physics modeling by (physics) equations that describe the degradation process within the states. In reality, the degradation process is non-Markovian, its transition rates are time-dependent, and the degradation is possibly influenced by uncertain external factors such as temperature and stress. Under these conditions, it is in general difficult to derive the state probabilities analytically. In this paper, we overcome this difficulty by building a simulation model supported by a stochastic Petri net representing the multistate degradation process. The proposed modeling approach is applied to the problem of a nuclear component undergoing stress corrosion cracking. The results are compared with those derived from the state-space enrichment Markov chain approximation method applied in a previous work of literature. View full abstract»

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  • Reliability and Maintenance Modeling for Dependent Competing Failure Processes With Shifting Failure Thresholds

    Page(s): 932 - 948
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3479 KB) |  | HTML iconHTML  

    We present reliability and maintenance models for systems subject to multiple s-dependent competing failure processes with a changing, dependent failure threshold. In our model, two failure processes are considered: soft failure caused by continuous degradation together with additional abrupt degradation due to a shock process, and hard failure caused by the instantaneous stress from the same shock process. These two failure processes are correlated or s-dependent in two respects: 1) the arrival of each shock load affects both failure processes, and 2) the shock process impacts the hard failure threshold level. In previous research, the failure thresholds are fixed constants, which is appropriate for most design and reliability problems. However, the nature of the failure threshold has become a critical issue for certain classes of complex devices. When withstanding shocks, the system is deteriorating, and its resistance to failure is weakening. In this case, it becomes more sensitive to hard failure. In this paper, three cases of dependency between the shock process and the hard failure threshold level are studied. The first case is that the hard failure threshold value changes to a lower level when the first shock is recorded above a critical value, or a generalized extreme shock model. The second case is that the hard failure threshold value decreases to a lower level when the time lag between two sequential shocks is less than a threshold δ, or a generalized δ-shock model. The third case is that the hard failure threshold value reduces to a lower level right after m shocks whose magnitudes are larger than a critical value, or a generalized m-shock model. Based on degradation and random shock modeling, reliability models are developed for these two s-dependent failure processes with a shifting failure threshold. Two preventive maintenance policies are also applied and compared to decide which one is more benefic- al. Then a Micro-Electro-Mechanical System example is given to demonstrate the reliability models and maintenance polices. View full abstract»

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  • Search for All Minimal Paths in a General Large Flow Network

    Page(s): 949 - 956
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1308 KB) |  | HTML iconHTML  

    An active research field is the evaluation of the reliability of a complex network. The most popular methods for such evaluation often use Minimal Paths (MP) or Minimal Cuts (MC) of the network. Although there are many algorithms developed to search for MP or MC, most of them are inefficient for searching a large network due to the combinatorial explosion problem. Another disadvantage is that the existing algorithms are applicable to specific counts of source and sink nodes (e.g., one-to-one, one-to-many, and so on). This article proposes a novel approach to search for all MP in a general flow network. The term “general” means that the approach can be used to search for all MP with multi-sources, multi-sinks in the network. The edges can be directed, undirected, or hybrid (mixed with directed and undirected arcs). Some benchmarks from the well-known algorithms in the literature are examined and compared. Moreover, the comprehensive tests are also performed with the grid networks, as well as the well-known networks in the literature to show the efficiency of the approach. A sample code is provided in the article for quick validation. View full abstract»

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  • The Robust Deviation Redundancy Allocation Problem With Interval Component Reliabilities

    Page(s): 957 - 965
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    We propose a robust deviation framework to deal with uncertain component reliabilities in the constrained redundancy optimization problem (CROP) in series-parallel reliability systems. The proposed model is based on a linearized binary version of standard nonlinear integer programming formulations of this problem. We extend the linearized model to address uncertainty by assuming that the component reliabilities belong to an interval uncertainty set, where only upper and lower bounds are known for each component reliability, and develop a Min-Max regret model to handle data uncertainty. A key challenge is that, because the deterministic model involves nonlinear functions of the uncertain data, classical robust deviation approaches cannot be applied directly to find robust solutions. We exploit problem structures to develop four exact solution methods, and present computational results demonstrating their performance. View full abstract»

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  • A Kalman Filter-Based Ensemble Approach With Application to Turbine Creep Prognostics

    Page(s): 966 - 977
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    The safety of nuclear power plants can be enhanced, and the costs of operation and maintenance reduced, by means of prognostic and health management systems which enable detecting, diagnosing, predicting, and proactively managing the equipment degradation toward failure. We propose a prognostic method which predicts the Remaining Useful Life (RUL) of a degrading system by means of an ensemble of empirical models. The RUL predictions of the individual models are aggregated through a Kalman Filter (KF)-based algorithm. The method is applied to the prediction of the RUL of turbine blades affected by a developing creep. View full abstract»

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  • An Efficient Implementation of the Rainflow Counting Algorithm for Life Consumption Estimation

    Page(s): 978 - 986
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    In many reliability design and model-based health management applications where load profiles are variable and unpredictable, it is desirable to have efficient cycle counting methods to identify equivalent full and half cycles within the irregular load profile. Conventional cycle-based lifetime models can then be applied directly to provide information about the life consumption of the products. The use of an off-line rainflow algorithm is a common solution for arbitrary loads, but it cannot be applied in real time in its original form. This paper presents an in-line coding algorithm which uses a stack-based implementation, and a recursive algorithm to pick out the equivalent full and half cycles of the irregular load profile. The method can be integrated easily within time-domain or serial data applications to generate equivalent full and half cycles as they occur. Thus it is of particular significance for life estimation in real-time applications where use of the traditional implementations of the counting algorithm is impractical. In comparison with the off-line traditional rainflow method, the on-line method doesn't require any knowledge of the time history of the load profile because it processes each minimum or maximum when it occurs. Therefore, it provides a more efficient cycle counting method using less memory storage, and making more efficient use of computational resources within the real-time environment. View full abstract»

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  • Bivariate Equilibrium Distribution and Association Measure

    Page(s): 987 - 993
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    In this paper, we study the joint distribution of the backward and forward recurrence times in a renewal process, from the reliability point of view. The survival function, the failure rate, and the mean residual life function, are derived, and their monotonicity is discussed for the marginals, as well as the conditional models. We derive a time dependent association measure in terms of the hazard gradient, and its value, in the case of the model under consideration, is obtained. A real-life application is included to illustrate the importance of the model. View full abstract»

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  • Sensitivity Analysis of Server Virtualized System Availability

    Page(s): 994 - 1006
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1761 KB) |  | HTML iconHTML  

    Server virtualization is a technology used in many enterprise systems to reduce operation and acquisition costs, and increase the availability of their critical services. Virtualized systems may be even more complex than traditional nonvirtualized systems; thus, the quantitative assessment of system availability is even more difficult. In this paper, we propose a sensitivity analysis approach to find the parameters that deserve more attention for improving the availability of systems. Our analysis is based on Markov reward models, and suggests that host failure rate is the most important parameter when the measure of interest is the system mean time to failure. For capacity oriented availability, the failure rate of applications was found to be another major concern. The results of both analyses were cross-validated by varying each parameter in isolation, and checking the corresponding change in the measure of interest. A cost-based optimization method helps to highlight the parameter that should have higher priority in system enhancement. View full abstract»

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  • Reliability Characteristics of k -out-of- n Warm Standby Systems

    Page(s): 1007 - 1018
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    We study reliability characteristics of the k-out-of- n warm standby system with identical components subject to exponential lifetime distributions. We derive state probabilities of the warm standby system in a form that is similar to the state probabilities of the active redundancy system. Subsequently, the system reliability is expressed in several forms that can provide new insights into the system reliability characteristics. We also show that all properties and computational procedures that are applicable for active redundancy are also applicable for the warm standby redundancy. As a result, it is shown that the system reliability can be evaluated using robust algorithms within O(n-k+1) computational time. In addition, we provide closed-form expressions for the hazard rate, probability density function, and mean residual life function. We show that the time-to-failure distribution of the k-out-of-n warm standby system is equal to the beta exponential distribution. Subsequently, we derive closed-form expressions for the higher order moments of the system failure time. Further, we show that the reliability of the warm standby system can be calculated using well-established numerical procedures that are available for the beta distribution. We prove that the improvement in system reliability with an additional redundant component follows a negative binomial (Pólya) distribution, and it is log-concave in n. Similarly, we prove that the system reliability function is log-concave in n. Because the k -out-of-n system with active redundancy can be considered as a special case of the k-out-of-n warm standby system, we indirectly provide some new results for the active redundancy case as well. View full abstract»

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  • Sequential Test for Reliability Under Allowance for Target Uncertainty

    Page(s): 1019 - 1029
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    This paper deals with development of a sequential test for comparison of the mean times between failures (MTBF) of two items: a new one (subscript n) and another serving as a reference (subscript ref). In the test, the hypothesis is checked that MTBFn is not less than a given multiple of MTBFref. Prior of the test, information is available on a known quantity r of the time between failures (TBF) for the reference item, which is not involved in the test, and only the new item is tested. It is a common occurrence in industrial practice that a new, modernized item has to be checked versus a reference counterpart currently in use. We assume that the TBF of both items is distributed exponentially. The sequential probability ratio test (SPRT) is widely used for checking that an MTBFn is not less than a specified (target) value. The innovation here is that in the proposed sequential test the target is the MTBFref which is available not as an exact value but only as an estimate whose uncertainty depend on r . Accordingly, a search algorithm was developed for the decision boundaries in the proposed test, as well as for calculating the operating characteristic (OC), and the statistical parameters of the test duration and number of failures to stopping the test. Approximative relationships were obtained for the above, depending on the target OC. These straightforward relationships drastically simplify the planning process. Moreover, they will enable the reliability engineer to appreciate the need to take the above uncertainty into account, and the expediency of resorting to the test at an early stage of its planning. Its superior efficacy is demonstrated. View full abstract»

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  • Optimum Multi-Objective Ramp-Stress Accelerated Life Test with Stress Upper Bound for Burr Type-XII Distribution

    Page(s): 1030 - 1038
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2491 KB) |  | HTML iconHTML  

    This paper deals with the formulation of optimum multi-objective ramp-stress accelerated life test (ALT) plans with stress upper bounds for the Burr type-XII distribution under type-I censoring. It is impractical to estimate only one objective parameter after conducting such costly ALT tests. Therefore, an optimum ramp-stress ALT plan with multiple estimating objectives has been designed. The Burr type-XII life distribution has been found appropriate in accelerated life testing models. The commonly used Weibull and exponential distributions are its limiting cases, and the log-logistic distribution is its particular case. The inverse power law, and a cumulative exposure model are assumed. The optimal test plan consists of determining the ramp rate by minimizing the weighted sum of the asymptotic variances of the maximum likelihood estimator of quantile lifetimes at design constant stress. The method developed has been illustrated using an example, and the effects of the pre estimates of design parameters were investigated. Comparative study has also been done to highlight the merits of the proposed model. View full abstract»

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  • 2013 Annual Reliability and Maintainability Symposium (RAMS)

    Page(s): 1039
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  • Call for papers for a special section on fuzzy reliability

    Page(s): 1040
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  • Trustworthy computing [advertisement]

    Page(s): 1041
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  • Battery reliability and safety [advertisement]

    Page(s): 1042
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  • Open Access [advertisement]

    Page(s): 1043
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Aims & Scope

IEEE Transactions on Reliability is concerned with the problems involved in attaining reliability, maintaining it through the life of the system or device, and measuring it.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Way Kuo
City University of Hong Kong