Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925838
J. Bennett
SR-332, reliability prediction procedure for electronic equipment, is a module of the Telcordia family of requirements (FR) known as FR-796, reliability and quality generic requirements (RQGR). The purpose of the Telcordia reliability prediction procedure (RPP) is to document a standard method for predicting device and unit hardware reliability as well as serial system hardware reliability. A prediction of reliability is an important element in the process of selecting equipment for use by telecommunications service providers and other buyers of electronic equipment. As used in the RPP, reliability is a measure of the frequency of equipment failures as a function of time. In 2006, the RPP was revised for the first time since 2001. Among many changes, the new issue 2 introduced a significant enhancement to the procedure: the ability to estimate arbitrary upper confidence levels (UCLs). This paper will provide a brief overview of the RPP, review modifications to the procedure in Issue 2, and in particular describe the motivation for defining these new UCL techniques, the addition of generic standard deviation values, and the methodologies for calculating upper confidence levels. In conclusion, the techniques introduced in issue 2 allow for the calculation of failure rate UCLs at any level of confidence, not just 90%. A side benefit is that the new techniques also allow for the estimation of the mean failure rate of electronic equipment.
{"title":"Telcordia Reliability Prediction Procedure: Upper confidence levels","authors":"J. Bennett","doi":"10.1109/RAMS.2008.4925838","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925838","url":null,"abstract":"SR-332, reliability prediction procedure for electronic equipment, is a module of the Telcordia family of requirements (FR) known as FR-796, reliability and quality generic requirements (RQGR). The purpose of the Telcordia reliability prediction procedure (RPP) is to document a standard method for predicting device and unit hardware reliability as well as serial system hardware reliability. A prediction of reliability is an important element in the process of selecting equipment for use by telecommunications service providers and other buyers of electronic equipment. As used in the RPP, reliability is a measure of the frequency of equipment failures as a function of time. In 2006, the RPP was revised for the first time since 2001. Among many changes, the new issue 2 introduced a significant enhancement to the procedure: the ability to estimate arbitrary upper confidence levels (UCLs). This paper will provide a brief overview of the RPP, review modifications to the procedure in Issue 2, and in particular describe the motivation for defining these new UCL techniques, the addition of generic standard deviation values, and the methodologies for calculating upper confidence levels. In conclusion, the techniques introduced in issue 2 allow for the calculation of failure rate UCLs at any level of confidence, not just 90%. A side benefit is that the new techniques also allow for the estimation of the mean failure rate of electronic equipment.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121416543","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}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925769
T. Halim, L. Tang
The conventional calendar-based mean cumulative function (MCF) plot is useful in monitoring the field reliability of a population of repairable systems deployed in a large quantity. It is simple and easily understood by management. However, it is age-confounded when the system population is heterogeneous with age. Assuming that the systems follow the well-known ldquoBathtubrdquo behavior, the population which consists of a higher proportion of aged systems will perform badly on the MCF plot compared to another population made up of mainly newer systems. Hence, direct comparisons between the two populations may not be fair. This paper illustrates a few simple steps that aid in mitigating such age heterogeneity issue prior to plotting the MCF. The age-adjusted MCF allows a fairer comparison of maintenance performance between the populations. The applicability of the proposed approach is demonstrated using actual field failure data. The case study shows that if differences in system age compositions are not accounted for, different conclusions could be drawn which could be detrimental to the maintenance personnel morale. Worse still, precious maintenance resources might be channeled to the wrong location. Other than age, the proposed approach can be easily extended to adjust for other system attributes.
{"title":"An age-adjusted comparison of field failure data for repairable systems","authors":"T. Halim, L. Tang","doi":"10.1109/RAMS.2008.4925769","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925769","url":null,"abstract":"The conventional calendar-based mean cumulative function (MCF) plot is useful in monitoring the field reliability of a population of repairable systems deployed in a large quantity. It is simple and easily understood by management. However, it is age-confounded when the system population is heterogeneous with age. Assuming that the systems follow the well-known ldquoBathtubrdquo behavior, the population which consists of a higher proportion of aged systems will perform badly on the MCF plot compared to another population made up of mainly newer systems. Hence, direct comparisons between the two populations may not be fair. This paper illustrates a few simple steps that aid in mitigating such age heterogeneity issue prior to plotting the MCF. The age-adjusted MCF allows a fairer comparison of maintenance performance between the populations. The applicability of the proposed approach is demonstrated using actual field failure data. The case study shows that if differences in system age compositions are not accounted for, different conclusions could be drawn which could be detrimental to the maintenance personnel morale. Worse still, precious maintenance resources might be channeled to the wrong location. Other than age, the proposed approach can be easily extended to adjust for other system attributes.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128744433","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}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925821
L. Xing, Wendai Wang
Common-cause failures (CCF) are simultaneous failures of multiple components within a system due to a common-cause or a shared root cause. CCF can contribute significantly to the overall system unreliability. Therefore, it is important to incorporate CCF into the system reliability analysis. Traditional CCF analyses have assumed that the occurrence of a common-cause results in the deterministic/guaranteed failure of components affected by that common-cause. In practical systems, however, the occurrence of a common-cause may result into failures of different components with different probabilities of occurrence. This behavior is termed as probabilistic CCF (PCCF). In this paper, we present a combinatorial method for the reliability analysis of systems subject to PCCF. The approach is represented in a dynamic fault tree model by a proposed probabilistic CCF gate. Basics of the proposed approach and effects of PCCF on the system reliability are illustrated through the detailed quantitative analysis of an example system.
{"title":"Probabilistic common-cause failures analysis","authors":"L. Xing, Wendai Wang","doi":"10.1109/RAMS.2008.4925821","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925821","url":null,"abstract":"Common-cause failures (CCF) are simultaneous failures of multiple components within a system due to a common-cause or a shared root cause. CCF can contribute significantly to the overall system unreliability. Therefore, it is important to incorporate CCF into the system reliability analysis. Traditional CCF analyses have assumed that the occurrence of a common-cause results in the deterministic/guaranteed failure of components affected by that common-cause. In practical systems, however, the occurrence of a common-cause may result into failures of different components with different probabilities of occurrence. This behavior is termed as probabilistic CCF (PCCF). In this paper, we present a combinatorial method for the reliability analysis of systems subject to PCCF. The approach is represented in a dynamic fault tree model by a proposed probabilistic CCF gate. Basics of the proposed approach and effects of PCCF on the system reliability are illustrated through the detailed quantitative analysis of an example system.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125420072","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}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925763
Dong Liu, Chunyuan Zhang, Weiyan Xing, Rui Li
The paper presents a Bayesian networks (BN) based method to analyze the reliability of phased-mission systems (PMS). The method includes three steps. Firstly, each phase of PMS is represented by a BN framework, named phase-BN. Then, in order to express the dependences across phases, all the phase-BN are combined by (1) connecting the root nodes that represent the same component but belong to different phases, and (2) connecting the leaf nodes of the phase-BN with a new node that represents the whole PMS mission. The new constructed BN is named PMS-BN. Lastly, the reliability analysis is performed by a discrete-time BN modeling acting on PMS-BN. Two examples are used to expatiate on the proposed approach. The PMS-BN based method provides a new way to analyze PMS, especially those with dynamic phases. Further, based on PMS-BN, fault diagnoses and sensitivity analysis can be performed easily.
{"title":"Reliability analysis of phased-mission systems using Bayesian networks","authors":"Dong Liu, Chunyuan Zhang, Weiyan Xing, Rui Li","doi":"10.1109/RAMS.2008.4925763","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925763","url":null,"abstract":"The paper presents a Bayesian networks (BN) based method to analyze the reliability of phased-mission systems (PMS). The method includes three steps. Firstly, each phase of PMS is represented by a BN framework, named phase-BN. Then, in order to express the dependences across phases, all the phase-BN are combined by (1) connecting the root nodes that represent the same component but belong to different phases, and (2) connecting the leaf nodes of the phase-BN with a new node that represents the whole PMS mission. The new constructed BN is named PMS-BN. Lastly, the reliability analysis is performed by a discrete-time BN modeling acting on PMS-BN. Two examples are used to expatiate on the proposed approach. The PMS-BN based method provides a new way to analyze PMS, especially those with dynamic phases. Further, based on PMS-BN, fault diagnoses and sensitivity analysis can be performed easily.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114763204","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}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925784
J. Bukowski
This paper proposes a new figure of merit (FOM) for evaluating safety integrity levels (SIL) for safety instrumented systems (SIS). Currently, SIL ratings are based on two separate tables - one for low process demands and one for high process demands. The proposed FOM, probability of an accident as a function of time, PAC(t), unifies the two separate tables into a single table and extends the concept of risk reduction factor (RRF), which is currently only defined for low demand applications, to high demand applications as well. Using PAC(t) as the new FOM explicitly includes the process demand rate in the model and therefore, permits the effects of different demand rates on the safety performance of a specific SIS to be quantified. The model also allows for the inclusion of diagnostic coverage and on-line repair so that the effects of these parameters can also be quantified. Finally, using PAC(t), the maximum time of periodic inspection (TI) permitted before the SIS moves to a lower SIL rating can be easily calculated. A number of examples illustrate the application and usefulness of PAC(t) as the defining FOM for SIL evaluation.
{"title":"A unified model for evaluating the safety integrity level of safety instrumented systems","authors":"J. Bukowski","doi":"10.1109/RAMS.2008.4925784","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925784","url":null,"abstract":"This paper proposes a new figure of merit (FOM) for evaluating safety integrity levels (SIL) for safety instrumented systems (SIS). Currently, SIL ratings are based on two separate tables - one for low process demands and one for high process demands. The proposed FOM, probability of an accident as a function of time, PAC(t), unifies the two separate tables into a single table and extends the concept of risk reduction factor (RRF), which is currently only defined for low demand applications, to high demand applications as well. Using PAC(t) as the new FOM explicitly includes the process demand rate in the model and therefore, permits the effects of different demand rates on the safety performance of a specific SIS to be quantified. The model also allows for the inclusion of diagnostic coverage and on-line repair so that the effects of these parameters can also be quantified. Finally, using PAC(t), the maximum time of periodic inspection (TI) permitted before the SIS moves to a lower SIL rating can be easily calculated. A number of examples illustrate the application and usefulness of PAC(t) as the defining FOM for SIL evaluation.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130631086","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}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925761
J. Andrews, D. Prescott, R. Remenyte-Prescott
This paper presents a decision making strategy for autonomous multi-platform systems, wherein a number of platforms perform phased missions in order to achieve an overall mission objective. Phased missions are defined for both single and multi-platform systems and a decision making strategy is outlined for such systems. The requirements for a tool performing such a strategy are discussed and methods and techniques, traditionally used for system reliability assessment, are identified to fulfill these requirements. Two examples are presented in order to demonstrate how a decision making tool would be employed in practice. Finally, a brief discussion of the efficient implementation of such a strategy is presented.
{"title":"A systems reliability approach to decision making in autonomous multi-platform systems operating a phased mission","authors":"J. Andrews, D. Prescott, R. Remenyte-Prescott","doi":"10.1109/RAMS.2008.4925761","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925761","url":null,"abstract":"This paper presents a decision making strategy for autonomous multi-platform systems, wherein a number of platforms perform phased missions in order to achieve an overall mission objective. Phased missions are defined for both single and multi-platform systems and a decision making strategy is outlined for such systems. The requirements for a tool performing such a strategy are discussed and methods and techniques, traditionally used for system reliability assessment, are identified to fulfill these requirements. Two examples are presented in order to demonstrate how a decision making tool would be employed in practice. Finally, a brief discussion of the efficient implementation of such a strategy is presented.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114212075","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}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925807
C. Maisch, B. Bertsche, B. Hohn, H. Otto
This paper deals with an approach for estimating the reliability of transmission oils. Forecasting oil degradation, especially oil oxidation allows to adjust the right oil change intervals in order to save costs and expand the transmission's lifetime. A brief overview about the machine component transmission oil imparts fundamental knowledge. Afterwards definitions of oil failures and combining them to an oil failure system are explained. The oil lifetime line describes the relationship between possible oil temperature and oil lifetime. Such a line is the keystone in order to describe the oil's failure behavior caused by oil oxidation. That's why two approaches are presented to derive an oil lifetime line. Both approaches use a certain oil failure criteria explained in the paper. The first approach is based on the pitting damage accumulation hypothesis. This hypothesis is expanded with the effect of oil degradation what enables to derive an oil lifetime line out of the results of a back-to-back spur gear rig test. Second a General Log Linear Model is used with available data to estimate the gears pitting lifetime depending on oil degradation. With the defined oil failure criteria it was possible to derive an oil lifetime line for mineral oil based fluids. Further work will include the estimation of an oil lifetime line with a probability and confidence intervals. Synthetic oil based fluids need to be investigated more intensive in order to create a suitable oil lifetime line for such fluids.
{"title":"An approach for estimating the reliability of transmission oils","authors":"C. Maisch, B. Bertsche, B. Hohn, H. Otto","doi":"10.1109/RAMS.2008.4925807","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925807","url":null,"abstract":"This paper deals with an approach for estimating the reliability of transmission oils. Forecasting oil degradation, especially oil oxidation allows to adjust the right oil change intervals in order to save costs and expand the transmission's lifetime. A brief overview about the machine component transmission oil imparts fundamental knowledge. Afterwards definitions of oil failures and combining them to an oil failure system are explained. The oil lifetime line describes the relationship between possible oil temperature and oil lifetime. Such a line is the keystone in order to describe the oil's failure behavior caused by oil oxidation. That's why two approaches are presented to derive an oil lifetime line. Both approaches use a certain oil failure criteria explained in the paper. The first approach is based on the pitting damage accumulation hypothesis. This hypothesis is expanded with the effect of oil degradation what enables to derive an oil lifetime line out of the results of a back-to-back spur gear rig test. Second a General Log Linear Model is used with available data to estimate the gears pitting lifetime depending on oil degradation. With the defined oil failure criteria it was possible to derive an oil lifetime line for mineral oil based fluids. Further work will include the estimation of an oil lifetime line with a probability and confidence intervals. Synthetic oil based fluids need to be investigated more intensive in order to create a suitable oil lifetime line for such fluids.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131434276","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}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925768
L. H. Crow
It is common practice for systems to be subjective to operational testing during their development program. The objective of this testing is to evaluate the performance, including reliability, of the system under conditions that represent actual use conditions. Because of expense, resources, schedule, and other considerations, these operational tests rarely represent exactly the actual use conditions. Rather, stated mission profile conditions are specific for the operational testing. These mission profiles conditions are typically general statements that guide the testing on an average basis during the testing. Because of practical constraints the elements that make up the mission profile conditions are typically tested under varying schedules with the intent that on average the mission profile conditions are met. It is also common practice that reliability corrective actions are incorporated into the system during this type of testing. That is, the test is often an operational mission profile reliability growth test. Under these conditions, we usually have a lack of structure for managing the elements that make up the mission profiles, which makes it very difficult to have an agreed-on methodology for estimating the system's reliability. This is especially true if reliability growth is occurring. Many systems fail operational testing because key assessments parameters can not be made in a straightforward clear manner so that management can take timely and appropriate action. This paper addresses this issue and presents a methodology currently being applied on major Department of Defense programs for operational reliability growth testing.
{"title":"A methodology for managing reliability growth during operational mission profile testing","authors":"L. H. Crow","doi":"10.1109/RAMS.2008.4925768","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925768","url":null,"abstract":"It is common practice for systems to be subjective to operational testing during their development program. The objective of this testing is to evaluate the performance, including reliability, of the system under conditions that represent actual use conditions. Because of expense, resources, schedule, and other considerations, these operational tests rarely represent exactly the actual use conditions. Rather, stated mission profile conditions are specific for the operational testing. These mission profiles conditions are typically general statements that guide the testing on an average basis during the testing. Because of practical constraints the elements that make up the mission profile conditions are typically tested under varying schedules with the intent that on average the mission profile conditions are met. It is also common practice that reliability corrective actions are incorporated into the system during this type of testing. That is, the test is often an operational mission profile reliability growth test. Under these conditions, we usually have a lack of structure for managing the elements that make up the mission profiles, which makes it very difficult to have an agreed-on methodology for estimating the system's reliability. This is especially true if reliability growth is occurring. Many systems fail operational testing because key assessments parameters can not be made in a straightforward clear manner so that management can take timely and appropriate action. This paper addresses this issue and presents a methodology currently being applied on major Department of Defense programs for operational reliability growth testing.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129682222","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}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925805
N. Wakai, Y. Kobira, H. Egawa
An effective procedure to determine the burn-in acceleration factors for latest system LSI (Large Scale Integration) with 90 nm and 65 nm technology are discussed in this paper. The relationship among yield, defect density, and reliability, is well known and well documented for defect mechanisms. In particular, it is important to determine the suitable acceleration factors for temperature and voltage to estimate the exact burn-in conditions needed to screen these defects. The approach in this paper is found to be useful for recent Cu-processes which are difficult to control from a defectivity standpoint. Performing an evaluation with test vehicles of from 130 nm to 65 nm technology, the following acceleration factors were obtained, Ea>0.9 ev and gamma(Gamma)>-5.85. In addition, it was determined that a lower defect density gave a lower Weibull shape parameter. As a result of failure analysis, it is found that the main failures in these technologies were caused by particles, and their Weibull shape parameter ldquobetardquo was changed depending of the related defect density. These factors can be applied for an immature time period where the process and products have failure mechanisms dominated by defects. Thus, an effective Burn-In is possible with classification from the standpoint of defect density, even from a period of technology immaturity.
{"title":"Consideration of Burn-In acceleration and effective screening procedure in latest System LSI","authors":"N. Wakai, Y. Kobira, H. Egawa","doi":"10.1109/RAMS.2008.4925805","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925805","url":null,"abstract":"An effective procedure to determine the burn-in acceleration factors for latest system LSI (Large Scale Integration) with 90 nm and 65 nm technology are discussed in this paper. The relationship among yield, defect density, and reliability, is well known and well documented for defect mechanisms. In particular, it is important to determine the suitable acceleration factors for temperature and voltage to estimate the exact burn-in conditions needed to screen these defects. The approach in this paper is found to be useful for recent Cu-processes which are difficult to control from a defectivity standpoint. Performing an evaluation with test vehicles of from 130 nm to 65 nm technology, the following acceleration factors were obtained, Ea>0.9 ev and gamma(Gamma)>-5.85. In addition, it was determined that a lower defect density gave a lower Weibull shape parameter. As a result of failure analysis, it is found that the main failures in these technologies were caused by particles, and their Weibull shape parameter ldquobetardquo was changed depending of the related defect density. These factors can be applied for an immature time period where the process and products have failure mechanisms dominated by defects. Thus, an effective Burn-In is possible with classification from the standpoint of defect density, even from a period of technology immaturity.","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130834360","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}
Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925767
A. P. Patra, P. Soderholm, U. Kumar
Life Cycle Cost (LCC) is used as a cost effective decision support for maintenance of railway track infrastructure. However, a fair degree of uncertainty associated with the estimation of LCC is due to the statistical characteristics of Reliability, Availability and Maintainability (RAM) parameters. This paper illustrates a methodology for estimation of uncertainty linked with LCC, by a combination of Design of Experiment (DoE) and Monte Carlo simulation. The paper also includes developed maintenance cost models for track and a case study of Banverket (Swedish National Rail Administration).
{"title":"Uncertainty in Life Cycle Cost of railway track","authors":"A. P. Patra, P. Soderholm, U. Kumar","doi":"10.1109/RAMS.2008.4925767","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925767","url":null,"abstract":"Life Cycle Cost (LCC) is used as a cost effective decision support for maintenance of railway track infrastructure. However, a fair degree of uncertainty associated with the estimation of LCC is due to the statistical characteristics of Reliability, Availability and Maintainability (RAM) parameters. This paper illustrates a methodology for estimation of uncertainty linked with LCC, by a combination of Design of Experiment (DoE) and Monte Carlo simulation. The paper also includes developed maintenance cost models for track and a case study of Banverket (Swedish National Rail Administration).","PeriodicalId":143940,"journal":{"name":"2008 Annual Reliability and Maintainability Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2008-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128242508","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}
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