2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)最新文献

{"title":"Software failure modes and effects analysis","authors":"J. Stadler, N. J. Seidl","doi":"10.1109/RAMS.2013.6517710","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517710","url":null,"abstract":"Failure modes and effects analysis (FMEA) is an effective way to identify and mitigate potential problems within the design of a system. By adapting the general process outlined in MIL-STD-1629A [1] to the design of software, a rigorous software FMEA (SFMEA) process has been developed to drive the identification of risks to safety, reliability, and customer satisfaction.","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132134069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Allocation analysis - challenges & solutions","authors":"N. Bidokhti","doi":"10.1109/RAMS.2013.6517724","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517724","url":null,"abstract":"This paper discusses the challenges and solutions for reliability allocation analysis. The sophistication of today's designs requires a re-evaluation of reliability allocation methodologies to account for complex system designs where a single board could have lots of powerful ICs such as ASICs, FPGAs, Processors, etc. how should the overall system reliability target be divided into various elements? As part of early engagement between reliability and design Engineers, reliability requirements are discussed which includes the design reliability allocation. The design team uses this data as a guideline to ensure the design will meet the required reliability and availability requirements. As product and circuit designs are becoming more and more complex, the ability to set the reliability and availability targets for each element of the system becomes more challenging as well. Products are no longer hardware only; the majority of functions are driven by software. As product features and functions are constantly increasing, the demand for complex integrated circuits is increasing as well. There are a number of component types such as ASICs, FPGAs, DSPs, Mirco-controller / processors and memories are susceptible to soft errors. Therefore, there is a need to connect this phenomenon to physical hardware and software failures to accurately define the required reliability and availability targets for the system components. The goal of this paper is to discuss the current methods of allocating reliability targets for system elements and highlight the shortcomings of the current methodologies. In addition, it will provide the best practices and methods to allocate / divide system reliability requirements by individual elements and takes various types failures such as hardware, software and soft errors into account.","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133142043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty management in model-based imputation for missing data","authors":"M. Azarkhail, P. Woytowitz","doi":"10.1109/RAMS.2013.6517697","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517697","url":null,"abstract":"In semiconductor industry like many other applications, the failure data is rarely available in complete form and is often flawed by missing records. When the missing process is random, the missing data can be safely ignored without major conceptual impact on the statistics of the experiment. The potential flaw with ignoring the missing data, however, is that the remaining complete observations may not carry enough statistical power, due to small sample size of the remaining population of complete failures. In some cases, the modeler may be able to describe the missing records as a function of other independent information available. Imputation of missing records from such empirical model is a typical way by which the lateral information about missing records can be leveraged. These models often carry considerable uncertainty that needs to be effectively incorporated into the data analysis process, in order to avoid false overconfidence in estimated reliability measures. In this article the uncertainty management during the model-based imputation process for missing data is discussed. The case study consists of Weibull analysis for a reliability critical component when a simple linear model is available for the missing records. Ignoring the missing records will result in relatively large uncertainty over the calculated reliability measures. The single imputation from correlation model will mark the other end of the spectrum due to an artificial boost in the statistical significance of the results as expected. The Multiple imputations and Bayesian likelihood averaging methods seem to be the most viable options when it comes to the uncertainty management in this problem. There seems to be some differences, however, that will be explained in detail.","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133233117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating optimal design for ALT experiment with time censoring","authors":"Moein Saleh, Rong Pan","doi":"10.1109/RAMS.2013.6517665","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517665","url":null,"abstract":"The computer generated optimal experimental designs have become popular as the computational capabilities of computers grow. For example, the optimal test plans of accelerated life tests (ALT) have received a lot of attentions from academia and practitioners in recent years. In this paper, we discuss a computational method for finding the D-optimal ALT plans through the generalized linear model (GLM) of accelerated life testing problem. A modified version of the point exchange algorithm (PEA) is developed. By comparing this algorithm with the quasi Newton method on an ALT planning problem, we demonstrate the superiority of our method in terms of speed and accuracy.","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132317385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Availability Estimation of a Photovoltaic System","authors":"A. Charki, D. Bigaud","doi":"10.1109/RAMS.2013.6517744","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517744","url":null,"abstract":"The availability estimation of photovoltaic (PV) systems has not been received great attention in the literature. Availability is important consideration in the life-cycle of such systems. This paper presents a methodology for estimating the availability of a photovoltaic system using Petri networks. Each component - module, wires and inverter - is detailed in Petri networks. In this paper, we simulate a photovoltaic system considering failure and repair distributions of its components. In this paper, we investigate and simulate a photovoltaic system using Petri nets considering failure and repair distributions of its components. Several types of failure modes of PV modules are implemented in the simulation. A study shows that the influence on the number of modules in series on the availability estimation is not significant. However, the mean time between failures decreases in increasing the number of series. The proposal methodology can be used for optimizing the performance of the installation of a PV system.","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133035101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-servicing failure rates: Optimizing preventive maintenance interval and quantifying maintenance induced failure in repairable systems","authors":"C. Jackson, B. Mailler","doi":"10.1109/RAMS.2013.6517681","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517681","url":null,"abstract":"The underlying aims of preventive maintenance (PM) include improving reliability, operational availability and life-cycle costs of systems by reducing the risk of potentially expensive and inopportune failure. Optimizing PM frequency maximizes these benefits. Historically, this has been difficult to achieve due to the uncertainty regarding how each system will perform in a particular role, configuration and environment, inability to model maintenance induced failure (or ignorance and skepticism of the concept) and the complexity of analyzing systems with multiple preventive failure modes.","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115369093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Competing failure modes modeling with limited wearout failures","authors":"Peng Liu, Peng-fei Wang","doi":"10.1109/RAMS.2013.6517738","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517738","url":null,"abstract":"This paper proposes a new modeling approach which can be applied to solve some practical reliability engineering problems when competing failure modes present, but only one or limited failures have occurred. This type of failure data cannot be modeled by traditional time-to-failure distributions. The new approach is derived from Weibayes method and provides a viable solution when reliability inference is needed during early product deployment phase. The most important value of the derivation is: it provides a distributional interpretation about the Weibayes result, and the assumptions that are made. The derivation not only reproduces the important result in the literature, but also gives insight into the sampling distribution of the parameter estimate. A parametric bootstrap method is used to illustrate how to incorporate the result in competing failure mode modeling.","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116070425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive analytics: Assessing failure rate accuracy & failure mode completeness","authors":"J. Bukowski, W. Goble","doi":"10.1109/RAMS.2013.6517619","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517619","url":null,"abstract":"This paper introduces a benchmarking technique we call predictive analytics (PA). The benchmark for the constant failure rate (λ) of a specific failure mode of an element (e.g., pressure transmitter, microprocessor, valve, etc.) used to implement a safety instrumented function (SIF) is predicted using the failure modes, effects and diagnostic analysis (FMEDA) technique supported by a database of constant failure rates and failure mode distributions for the components which comprise the element. This benchmark represents the λ of that failure mode inherent in the element during its useful life. The λ for the same failure mode of the element is estimated from field failure data (FFD) and compared to the benchmark. It is not uncommon for the benchmark λ and estimated λ to differ considerably. PA provides a procedure for exploring explanations of these differences and assessing the accuracy of the estimated element λ with respect to the benchmark λ of the element. PA can often determine the source of that portion of the estimated λ value not inherent to the element but likely due to random failures of infant mortality, wear out, or initial failures, to systematic failures, or to application or site specific issues. This site specific element λ is the portion of the estimated λ the end user needs to address to improve operational reliability and safety. PA can also assess the quality of FFD and can facilitate the discovery of previously unknown element failure modes.","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"1 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121005523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing effective reliability growth strategies for DoD programs","authors":"R. Kaminski","doi":"10.1109/RAMS.2013.6517675","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517675","url":null,"abstract":"Planning for reliability growth requires a disciplined approach rather than an “ad hoc” approach. The reliability growth program must be devised early so that adequate test phase lengths and test assets can be planned to optimize reliability growth opportunities. The primary goal of the Reliability Growth Test (RGT) is to improve the probability of demonstrating achievement of the reliability requirement during Operational Testing (OT); which should also translate to effective reliability performance when fielded. The first step in the planning process is to determine the length of test time needed to reach a planned reliability growth goal before entry into OT testing. The planned growth goal should be above the reliability requirement to ensure that the requirement can be demonstrated with at least an 80 percent statistical lower confidence bound (LCB) during the OT phase. The primary inputs to planning models in general are: the initial reliability value from which the growth will commence, the anticipated reliability “growth rate”, the target or goal reliability, and the planned test length (phases). The initial starting reliability value is a critical input variable as it tends to influence overall test length. The best method for selecting an initial reliability value is to have some early test results where reliability has been determined by actual observed performance. The alternate method of selecting the initial reliability value based on some percentage of the requirement would entail formulating a less effective and higher risk test strategy. The next value that is critical to planning models is the growth rate. Choosing growth parameters that are realistic is an important step so that adequate test times can be planned. The final two major contributors are the goal reliability and test length, which directly influence cost and schedule. For this reason, various reliability growth strategies should be explored in order to optimize the reliability growth; balancing technical risk with program schedule and budget. Formulating effective reliability growth strategies also requires that a vigorous root cause analysis and corrective action (RCA/CA) process be developed. Simply replacing failed components during the RGT; without removing the underlying failure mode, will not result in reliability growth. Understanding the root cause of surfaced failure modes and devising effective mitigation strategies is the only method that will achieve reliability growth. This is why total calendar time that includes actual test time and time to perform RCA/CA must be considered and planned into the RGT and program test schedule. Another aspect to consider in planning the RGT is to identify items that are not likely to undergo reliability growth. The use of Commercial-Off-The-Shelf (COTS) items in many DoD programs is a reality that cannot be ignored. These COTS items will have no effective growth avenues since COTS suppliers are unlikely to change produc","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124956128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selective preventive maintenance scheduling under imperfect repair","authors":"M. Pandey, M. Zuo","doi":"10.1109/RAMS.2013.6517618","DOIUrl":"https://doi.org/10.1109/RAMS.2013.6517618","url":null,"abstract":"The demand for a system is sometimes available only for a finite time horizon. It thus becomes necessary to schedule maintenance activities during a given planning horizon such that the desired system performance is maintained and the available resources are optimally allocated. In this paper, a mathematical model is proposed for periodically planning preventive maintenance activities for a system comprising multiple components. Due to resource limitations, it may not be possible to perform all desired maintenance options; hence, a selective maintenance approach is used to find the components to be maintained and maintenance actions to be performed on the selected components. An imperfect maintenance based hybrid model is considered here which includes age reduction as well as hazard adjustment after maintenance. Due to the high dimension of the solution domain, evolutionary approach is used to solve the problem. The optimal number of intervals is found under reliability and maintenance time constraints. During each maintenance break, the optimal maintenance option is selected for each component such that the overall cost of maintenance and possible failures for the entire planning horizon is minimized. It is also found that considering one interval at a time will incur higher cost.","PeriodicalId":189714,"journal":{"name":"2013 Proceedings Annual Reliability and Maintainability Symposium (RAMS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126142677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}