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Reliability and Maintainability Symposium, 2009. RAMS 2009. Annual

Date 26-29 Jan. 2009

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Displaying Results 1 - 25 of 114
  • [Front cover]

    Page(s): c1
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    Freely Available from IEEE
  • [Front inside cover]

    Page(s): c2
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    Freely Available from IEEE
  • Annual reliability and maintainability symposium

    Page(s): i
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    Freely Available from IEEE
  • [Copyright notice - reprint permission]

    Page(s): ii
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    Freely Available from IEEE
  • Symposium management committee

    Page(s): iii
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    Freely Available from IEEE
  • Past symposia & general chairs

    Page(s): iv
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    Freely Available from IEEE
  • The R. A. Evans - P. K. McElroy Award for the 2008 Best Paper

    Page(s): v - vi
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    Freely Available from IEEE
  • Previous recipients [The R. A. Evans ?? P. K. McElroy Award]

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    Freely Available from IEEE
  • The R.A. Evans - P.K. McElroy Award for 2008 Best Paper [biography of Ralph A. Evans]

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    Freely Available from IEEE
  • R. A. Evans - P. K. McElroy Award 2007 Best Paper

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    Freely Available from IEEE
  • Tutorial sessions

    Page(s): x
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (118 KB)  

    Provides an abstract for each of the tutorial presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Technical program sessions and moderators

    Page(s): xi - xii
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    Freely Available from IEEE
  • Technical papers

    Page(s): xiii - xix
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    Freely Available from IEEE
  • Author index

    Page(s): xx - xxii
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    Freely Available from IEEE
  • Reliability analysis of dynamic fiber bundle models

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (283 KB) |  | HTML iconHTML  

    Fiber bundle models are useful tools for explaining dynamic failure behavior in heterogeneous materials. Such models shed light on diverse phenomena such as fatigue in structural materials and earthquakes in geophysical settings. Building good theoretical models has proven straightforward, but analyzing them has required delving into statistical details of the interaction of various flaw features and failure configurations, which has proven to be deceptively difficult. In this paper, we present a new method for reliability analysis of dynamic fiber bundle models. As in previous works, we assume that a fiber has a failure rate following a power law in its load level. However, unlike the exponential distribution used in the previous works, we consider that the remaining lifetimes of the surviving fibers follow Weibull distributions according to either the cumulative exposure model or tampered failure rate model. We develop both exact asymptotic reliability formulas and easy to compute bounds. Further, we also describe a fast simulation algorithm that greatly increases the bundle sizes that can be analyzed. View full abstract»

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  • PMF series for availability and reliability probabilistic assessments

    Page(s): 7 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1768 KB) |  | HTML iconHTML  

    PMF series (a contiguous family of probability mass functions) is a new method for probabilistic assessment of availability and reliability of both repairable and non- repairable systems. The same general methodology is applied to both availability and reliability. This allows availability assessment of complex systems with both repairable and non- repairable subsystems. Binominal and Weibull can be used in narrow circumstances, and in those cases results match those of PMF series, thus validating the more general PMF series. The method uses small quantities of system data that is always available for important operating systems, whereas Monte Carlo requires substantial data or assumptions. The probability tables that are developed are analogous to the capacity outage tables of the electric power industry. However, PMF series is more broadly applicable as it also applies to single plants, the more common situation in manufacturing. Probability tables are embedded in specific user application software for efficient day-to-day business and operating decisions. These may include applications such as product inventory optimization and the absolute and relative risk difference between operating strategies. This practical methodology using available data promises to greatly expand the use of probabilistic assessments. In the future, probabilistic risk may routinely be integrated into business and operating decisions. Practical methods have not heretofore been available for these decisions; therefore, decision makers are unaware that probabilistic risk can be used in their applications. Educating the decision makers in this technology is necessary. The ability to measure and analyze risk that previously was unmeasured and unrecognized leads to surprise. Management and Reliability Engineers are generally unaware of the actual risk in meeting the production goals for their plants. When the risks are recognized, they are invariably larger than supposed. As a consequence, the - reliability performance gap in any manufacturing company is perhaps significantly larger than single-valued measurement of mean availability, reliability and capacity would suggest. View full abstract»

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  • System of systems reliability for multi-state systems

    Page(s): 13 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (364 KB) |  | HTML iconHTML  

    The proliferation of networking technologies in commercial, consumer, and industrial applications has been mirrored in the defense industry. The US Department of Defense, along with comparable organizations abroad, have adopted a network centric paradigm for emerging and future systems. This migration to an information based approach is akin to the move to just-in-time manufacturing. That is, both approaches seek to achieve synergies from Information rather than from bulk of material. In the JIT case, this material is the component parts or materials, and in the military SoS the material may be armor and/or ammunition. Specifically, the US military is attempting to leverage the synergies of net centricity in order to obtain a more mobile and lighter fighting fleet that retains the same lethality and survivability by shedding armor and ammo in favor of information (radios and computers). In previous years, this author has presented papers on network reliability and multi-state reliability to the RAMS. In this year's paper, the notion of multi-state reliability is combined with the ad-hoc network reliability methods. This integrated model provides the ability to measure, in capacity terms the reliability of a SoS level by considering the network's state (connectivity) temporally and the interactions and contributions of its elements. Specifically, this paper describes a mathematical construct for simulating the capacity of general functions including lethality, mobility, survivability, reconnaissance, and supportability based upon traditional probabilistic measures of reliability. Next, the paper will describe a new method to combine those platform capabilities to an aggregate SoS level capability that is dependent on the connectivity between those nodes and the resultant synergies realized due to said connectivity. View full abstract»

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  • Reliability and quality of service in weighted probabilistic networks using Algebraic Decision Diagrams

    Page(s): 19 - 24
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (483 KB) |  | HTML iconHTML  

    In network reliability studies, nodes and links are usually represented as binary entities (either up or down). In many cases the analysis of the performance of the system requires a richer representation by associating to each arc a weight representing a specific attribute of the arc (e.g. capacity, resistance, cost, length). For example, the amount of traffic characterizing the connections in communication or transport systems or the distance between nodes in a highway network are fundamental for a full description of these networks. The paper explores the problem of the quantitative evaluation of reward functions in stochastic weighted networks, where the weights assigned to the arcs my have different physical interpretations. We discuss two types of interpretation of weights: weights as distances and weights as capacities. Correspondingly, two different algorithms based on a data structure called algebraic decision diagram (ADD), are discussed and presented. The first evaluates the probability that the terminal node can be reached from the source within a determinate distance or cost. The second computes the probability that a flow greater than a threshold can be transmitted between the source and the sink. The algorithms have been tested with several examples and with some benchmark network taken from the literature. View full abstract»

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  • Improving the 1-parameter Weibull: A Bayesian approach

    Page(s): 25 - 30
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (336 KB) |  | HTML iconHTML  

    Using maximum likelihood estimation (MLE) to estimate the parameters in a Weibull distribution will lead to a biased estimation of the shape parameter when the sample size is small or too few failures are observed. This bias may lead to inaccurate reliability point estimates. In addition, with few data points available in the calculation, the uncertainty of the estimated parameters is high, which again leads to high uncertainty in the predicted reliability (i.e. wide confidence bounds). To overcome these two issues, the 1-parameter Weibull distribution has been widely used, provided that the shape parameter is known beforehand. This approach, however, does not account for any uncertainty in the assumed value of the shape parameter and can therefore yield optimistic results in the form of tight confidence bounds. It can be improved with better information about the variability of the shaper parameter. In this paper, a Bayesian model, which is an improved approach for the 1-parameter Weibull, is discussed. Recommendations for establishing variability models for the Weibull shape parameter are presented. View full abstract»

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  • Bayesian reliability demonstration test in a Design for Reliability process

    Page(s): 31 - 36
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (439 KB) |  | HTML iconHTML  

    This paper illustrates how a Bayesian reliability demonstration test (BRDT) approach can be, and sometimes must be, integrated into a Design for Reliability (DFR) process. A simplified and effective BRDT algorithm is given, based on the prior distribution characteristics of reliability in the DFR process. The BRDT can significantly reduce sample size in reliability demonstration test, and serve as a powerful validation tool in DFR. View full abstract»

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  • Weibayes testing: What is the impact if assumed beta is incorrect?

    Page(s): 37 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1136 KB) |  | HTML iconHTML  

    This paper evaluates Weibayes small sample, zero and sudden death failure test and data analysis techniques in order to mathematically and graphically quantify the risk associated with assuming an incorrect value of the Weibull shape parameter, beta (beta). Specifically, the risk associated with assuming a value of beta that is higher than the ldquotruerdquo value of beta is investigated, as this is the scenario that can cause users of the Weibayes approach to (1) understate the test time needed to correctly define a reliability requirement and (2) interpret the test results incorrectly and overstate the reliability actually demonstrated. The work product of this effort is a MS Excelreg spreadsheet that automates the process of quantifying the risk associated with the Weibayes approach. View full abstract»

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  • Analysis of field performance using interval-censored incident data

    Page(s): 43 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (348 KB) |  | HTML iconHTML  

    Statistical time-to-failure analysis is a very powerful and versatile tool available to reliability engineers and statisticians for understanding and characterizing the failure risk and reliability of a component, device or system. Commonly applied methods of modeling time-to-failure involve fitting a parametric distribution, such as the Weibull probability function, using serial data on production or sales and incident data on units experiencing field failure since the launch of a product. When both the date of manufacture or sale and the date of incident are available from existing records, or can be easily ascertained by examination, the age of a failed unit can be determined exactly for purposes of analysis. Age censoring occurs, however, when one or both dates are missing-e.g., due to incomplete incident reporting or failure-induced physical damage to the unit. Excluding cases with incomplete date records involves a potentially significant loss of information in the time-to-failure analysis. We present several case studies to demonstrate how, in practice, such incidents can be treated as interval-censored observations in the time-to-failure analysis. Further, we evaluate the sensitivity of inferences to the inclusion of partially documented incidents to assess the value of this approach in practical applications. View full abstract»

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  • Six challenges in implementation of effective Accelerated Life Tests

    Page(s): 47 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (345 KB) |  | HTML iconHTML  

    Accelerated life testing (ALT) is an effective method of demonstrating and improving product reliability in applications where the products are expected to perform for a long period of time. Even though reliability professionals recognize the significance of high reliability, there are certain challenges which hinder an effective application of ALT principles in practical applications. This paper presents the six primary challenges that need to be considered during the planning and implementation of ALT and shows through means of a case study, how inadequate planning of ALT can result in expensive field failures. It also discusses the subsequent improvements that focus on the above principles to develop an improved plan. ALT plays a key role in the engineering development of products where high reliabilities are required. The case study discussed demonstrates the six challenges in effective implementation of such tests. The case study also showed the necessity for research in areas such as acceleration models for pressure and further investigation of aging effects of materials used. View full abstract»

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  • Bayesian parameter estimation with prior weighting in ALT model

    Page(s): 53 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (433 KB) |  | HTML iconHTML  

    This paper provides an overview of the application of Bayesian inference to accelerated life testing (ALT) models for the concrete case of estimation by Maximum of Aposteriori (MAP) method in the case of constant stress levels. It studies the Bayesian inference over the accelerated life model as presented in [1]. It suites, integrates and generalizes the particular cases presented in [2] and [3]. Towards the end, weighting of the prior information according to data is integrated. The paper also illustrates an experimental example. View full abstract»

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  • Life-prediction of the CSADT based on BP algorithm of ANN

    Page(s): 59 - 63
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (404 KB) |  | HTML iconHTML  

    One of the main problems encountered when we study the reliability of high-quality, long-life products by traditional means is that it is difficult to obtain adequate failure data in the life testing, for example, cost and time are always limited. For many of these products, an underlying degradation process is the root cause of failures. In order to get useful information in a short time, accelerated degradation testing (ADT) is frequently used. Using multi-stress in the ADT, we not only reduce time and cost of the testing, and increase efficiency, but we also simulate the actual environmental conditions more accurately and obtain more credible results. However, to achieve these results, the accelerated model must be established. Unfortunately, it is often quite difficult to give a certain physical or chemical model and quantify the degradation process, because the failure mechanisms of different stresses are various. In this paper, a new model is developed to predict the life of items in the constant stress accelerated degradation testing (CSADT) based on Back-Propagation (BP) Algorithm of Artificial Neural Network (BPANN). With this BPANN model, unlike other degradation analysis methods, this acceleration model avoids complicated calculations. It provides a new approach to the life-prediction of the ADT. View full abstract»

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