Pub Date : 2008-01-28DOI: 10.1109/RAMS.2008.4925794
K. Mohan, B. Cline, J. Akers
The use of warranty claims data to determine the failure characteristics of a product is well documented. Typically, the failure distribution and its parameters are determined using product manufacturing data for each month of production and the corresponding monthly failure counts derived from the warranty claims. If the data is collected systematically, the product ages at the times of failure can be derived. Classical methods are then used to determine the failure time distribution and parameters. However, our experience shows that, in many cases, it may not be possible to know the failure ages of components. The information available each month might be limited to the volume of shipments and total claims or product returns. In such cases, the data hides the component age at the time of failure. In this paper, we show that when the failure history information is incomplete, the failure distribution of the product can be determined using Bayesian analysis techniques applicable for handling incomplete data. We apply the popular Expectation-Maximization (EM) algorithm to find the Maximum Likelihood Estimates (MLE) of the failure distribution parameters using incomplete data. The effectiveness of the EM algorithm is compared using several sets of incomplete warranty data generated using simulation. We observed that the EM algorithm is powerful in capturing the hidden failure patterns from the incomplete warranty data.
{"title":"A practical method for failure analysis using incomplete warranty data","authors":"K. Mohan, B. Cline, J. Akers","doi":"10.1109/RAMS.2008.4925794","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925794","url":null,"abstract":"The use of warranty claims data to determine the failure characteristics of a product is well documented. Typically, the failure distribution and its parameters are determined using product manufacturing data for each month of production and the corresponding monthly failure counts derived from the warranty claims. If the data is collected systematically, the product ages at the times of failure can be derived. Classical methods are then used to determine the failure time distribution and parameters. However, our experience shows that, in many cases, it may not be possible to know the failure ages of components. The information available each month might be limited to the volume of shipments and total claims or product returns. In such cases, the data hides the component age at the time of failure. In this paper, we show that when the failure history information is incomplete, the failure distribution of the product can be determined using Bayesian analysis techniques applicable for handling incomplete data. We apply the popular Expectation-Maximization (EM) algorithm to find the Maximum Likelihood Estimates (MLE) of the failure distribution parameters using incomplete data. The effectiveness of the EM algorithm is compared using several sets of incomplete warranty data generated using simulation. We observed that the EM algorithm is powerful in capturing the hidden failure patterns from the incomplete warranty data.","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":"122885182","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.2008.4925775
Ming Li, P. Pruessner
Probabilistic Risk Assessment (PRA) is performed to assess the probability of failure or success of a system's operation. Results provided by the risk assessment methodology are used to make decisions concerning choice of improvements to the design. PRA has been applied or recommended to NASA space applications to identify and mitigate their risks. The complexity of these tasks and varied information sources required for space applications makes solving them manually infeasible. Software tools are mandated. To date, numerous software tools have been developed and claimed as PRA solutions. It is always a concern which software best fits a particular PRA. The authors conducted a limited scope PRA on a NASA application using four different Reliability/PRA software tools (Relex, QRAS, SAPHIRE, GoldSim), which were readily available. The strength and weakness for each tool are identified and discussed. Recommendations on how to improve each tool to better satisfy NASA PRA needs are discussed.
{"title":"Software tools for PRA","authors":"Ming Li, P. Pruessner","doi":"10.1109/RAMS.2008.4925775","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925775","url":null,"abstract":"Probabilistic Risk Assessment (PRA) is performed to assess the probability of failure or success of a system's operation. Results provided by the risk assessment methodology are used to make decisions concerning choice of improvements to the design. PRA has been applied or recommended to NASA space applications to identify and mitigate their risks. The complexity of these tasks and varied information sources required for space applications makes solving them manually infeasible. Software tools are mandated. To date, numerous software tools have been developed and claimed as PRA solutions. It is always a concern which software best fits a particular PRA. The authors conducted a limited scope PRA on a NASA application using four different Reliability/PRA software tools (Relex, QRAS, SAPHIRE, GoldSim), which were readily available. The strength and weakness for each tool are identified and discussed. Recommendations on how to improve each tool to better satisfy NASA PRA needs are discussed.","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":"114273919","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.2008.4925825
M. Silverman, F. Schenkelberg, C. Hillman
The subject matter we consider in this paper are the significant reliability uncertainties around lead-free solder and how to best consider these risks and mitigate them so as not to take a hit in the area of reliability during the lead-free transition. Like the rest of the electronics industry, your products will transition to restriction of hazardous substances (RoHS) compliance. This includes the transition to lead-free solder, and at this time, there are significant reliability uncertainties around lead-free solder. Even if your product does not need to be compliant, the materials and processes that make up your product are changing. During this time of rapid transition, there is a significant new body of knowledge to understand to determine the areas of greatest risk to the reliability of your product. In this paper, we will highlight a few of these significant risk areas and how to best mitigate these risks during the transition.
{"title":"How to develop a qualification test plan for RoHS products","authors":"M. Silverman, F. Schenkelberg, C. Hillman","doi":"10.1109/RAMS.2008.4925825","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925825","url":null,"abstract":"The subject matter we consider in this paper are the significant reliability uncertainties around lead-free solder and how to best consider these risks and mitigate them so as not to take a hit in the area of reliability during the lead-free transition. Like the rest of the electronics industry, your products will transition to restriction of hazardous substances (RoHS) compliance. This includes the transition to lead-free solder, and at this time, there are significant reliability uncertainties around lead-free solder. Even if your product does not need to be compliant, the materials and processes that make up your product are changing. During this time of rapid transition, there is a significant new body of knowledge to understand to determine the areas of greatest risk to the reliability of your product. In this paper, we will highlight a few of these significant risk areas and how to best mitigate these risks during the transition.","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":"128998387","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.2008.4925843
Jie Gu, M. Pecht
This paper presents a physics-of-failure based prognostics and health management approach for effective reliability prediction. This method permits in-situ assessment of system reliability under actual application conditions. The method uses sensor data with models that enable in-situ assessment of the deviation or degradation of a product from an expected normal operating condition (i.e., the system's ldquohealthrdquo) and the prediction of the future state of reliability. The implementation procedure of this approach includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life-cycle loads, damage accumulation, and assessment of uncertainty.
{"title":"Prognostics and health management using physics-of-failure","authors":"Jie Gu, M. Pecht","doi":"10.1109/RAMS.2008.4925843","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925843","url":null,"abstract":"This paper presents a physics-of-failure based prognostics and health management approach for effective reliability prediction. This method permits in-situ assessment of system reliability under actual application conditions. The method uses sensor data with models that enable in-situ assessment of the deviation or degradation of a product from an expected normal operating condition (i.e., the system's ldquohealthrdquo) and the prediction of the future state of reliability. The implementation procedure of this approach includes failure modes, mechanisms, and effects analysis, data reduction and feature extraction from the life-cycle loads, damage accumulation, and assessment of uncertainty.","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":"130415306","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.2008.4925844
G.J. Zukowski, D. Dzedzy
This paper will present a closed loop systems engineering methodology for integrating multi-disciplined, cross-functional activities (Reliability & Maintainability, Embedded Test, Supportability Engineering, System Safety, etc.), which are often ldquostove-pipedrdquo in order to ensure the total readiness capability of a system. This process and methodology is called the Maintainability Readiness Assessment (MRA).
{"title":"Maintainability Readiness Assessment (MRA)","authors":"G.J. Zukowski, D. Dzedzy","doi":"10.1109/RAMS.2008.4925844","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925844","url":null,"abstract":"This paper will present a closed loop systems engineering methodology for integrating multi-disciplined, cross-functional activities (Reliability & Maintainability, Embedded Test, Supportability Engineering, System Safety, etc.), which are often ldquostove-pipedrdquo in order to ensure the total readiness capability of a system. This process and methodology is called the Maintainability Readiness Assessment (MRA).","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":"123879310","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.2008.4925790
D. Farel, M. Silverman
When introducing a product into a new market, determining the current market players' reliability performance may lead to a competitive advantage. Or, if your competition is using reliability as a marketing lead, does your product match their performance or do they have the advantage? Using Competitive Analysis, we can determine areas of strength as well as areas of weakness so that we can develop a plan for reliability improvement. Competitive Analysis often uses tools such as Reliability Predictions, Failure Modes and Effects Analyses (FMEAs) and Highly Accelerated Life Tests (HALTs) to compare your product with that of your competition.
{"title":"Using Competitive Analysis to get the competitive advantage","authors":"D. Farel, M. Silverman","doi":"10.1109/RAMS.2008.4925790","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925790","url":null,"abstract":"When introducing a product into a new market, determining the current market players' reliability performance may lead to a competitive advantage. Or, if your competition is using reliability as a marketing lead, does your product match their performance or do they have the advantage? Using Competitive Analysis, we can determine areas of strength as well as areas of weakness so that we can develop a plan for reliability improvement. Competitive Analysis often uses tools such as Reliability Predictions, Failure Modes and Effects Analyses (FMEAs) and Highly Accelerated Life Tests (HALTs) to compare your product with that of your competition.","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":"121413219","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.2008.4925776
Jinsuk Lee, R. Pan
In this paper we present a Bayes inference model for a simple step-stress accelerated life test (SSALT) using type-II censored samples. We assume that the failure times at each stress are exponentially distributed with a mean that is a log-linear function of the natural stress level, and derive a likelihood function for the SSALT model under type-II censoring. We integrate the engineering knowledge into the prior distribution of the parameters in log-linear function, and through a Siegel-gamma distribution conjugation we can derive the posterior distribution for the parameters of interest. Applying Bayes approach to SSALT, the statistical precision of parameter inference is improved and the required number of samples is reduced.
{"title":"Bayesian inference model for step-stress accelerated life testing with type-II censoring","authors":"Jinsuk Lee, R. Pan","doi":"10.1109/RAMS.2008.4925776","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925776","url":null,"abstract":"In this paper we present a Bayes inference model for a simple step-stress accelerated life test (SSALT) using type-II censored samples. We assume that the failure times at each stress are exponentially distributed with a mean that is a log-linear function of the natural stress level, and derive a likelihood function for the SSALT model under type-II censoring. We integrate the engineering knowledge into the prior distribution of the parameters in log-linear function, and through a Siegel-gamma distribution conjugation we can derive the posterior distribution for the parameters of interest. Applying Bayes approach to SSALT, the statistical precision of parameter inference is improved and the required number of samples is reduced.","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":"116509363","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.2008.4925808
Zhonglai Wang, Li Du, Hongzhong Huang
In practical engineering applications, many factors of systems themselves and of random environments cause systems to suffer from degradation and shocks. Degradations, such as wear and erosion, occur in many systems, especially mechanical systems. Shocking is also a significant cause of system failure and hence has been paid more attention to. Shocks caused by the factors of the systems themselves usually have regular periods; especially for rotating devices, shocks have approximately fixed periods. Shocks caused by the factors of random environment usually follow Poisson process. In this paper, a new system reliability model is proposed for systems that involve dependent and competitive degradations and shocks. This model will have wide application in many fields. A numerical example is given to illustrate the model.
{"title":"Reliability modeling for dependent competitive failure processes","authors":"Zhonglai Wang, Li Du, Hongzhong Huang","doi":"10.1109/RAMS.2008.4925808","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925808","url":null,"abstract":"In practical engineering applications, many factors of systems themselves and of random environments cause systems to suffer from degradation and shocks. Degradations, such as wear and erosion, occur in many systems, especially mechanical systems. Shocking is also a significant cause of system failure and hence has been paid more attention to. Shocks caused by the factors of the systems themselves usually have regular periods; especially for rotating devices, shocks have approximately fixed periods. Shocks caused by the factors of random environment usually follow Poisson process. In this paper, a new system reliability model is proposed for systems that involve dependent and competitive degradations and shocks. This model will have wide application in many fields. A numerical example is given to illustrate the model.","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":"132419079","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.2008.4925774
S. Lawrence, D. Mathias
This paper describes recent enhancements to the engineering-level analysis tools used by the simulation assisted risk assessment (SARA) project (Ref. 1) at NASA Ames Research Center in evaluating the blast overpressure risk to the crew. The primary enhancements to the model include incorporation of vapor cloud explosion (VCE) curve fits for propellant explosions, development of an improved model for the effects of vehicle velocity on blast propagation, improvement in the representation of blast/vehicle interaction effects, and incorporation of pressure vs. impulse (P-I) failure criteria to better represent structural failure modes. High-fidelity computational fluid dynamics (CFD) simulations, using the Overflow2 (Ref. 2) code, played a crucial role in the development of some of these enhancements. A subset of the high-fidelity results is presented.
{"title":"Blast overpressure modeling enhancements for application to risk-informed design of human space flight launch abort systems","authors":"S. Lawrence, D. Mathias","doi":"10.1109/RAMS.2008.4925774","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925774","url":null,"abstract":"This paper describes recent enhancements to the engineering-level analysis tools used by the simulation assisted risk assessment (SARA) project (Ref. 1) at NASA Ames Research Center in evaluating the blast overpressure risk to the crew. The primary enhancements to the model include incorporation of vapor cloud explosion (VCE) curve fits for propellant explosions, development of an improved model for the effects of vehicle velocity on blast propagation, improvement in the representation of blast/vehicle interaction effects, and incorporation of pressure vs. impulse (P-I) failure criteria to better represent structural failure modes. High-fidelity computational fluid dynamics (CFD) simulations, using the Overflow2 (Ref. 2) code, played a crucial role in the development of some of these enhancements. A subset of the high-fidelity results 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":"132157225","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.2008.4925814
R. Gross, S. Harris
Proof test results were analyzed and compared with a proposed life cycle curve or hazard function and the limit of useful life. Relief valve proof testing procedures, statistical modeling, data collection processes, and time-in-service trends are presented. The resulting analysis of test data allows for the estimation of a probability of failure on demand (PFD). Extending maintenance intervals to the limit of useful life as well as methodologies and practices for improving relief valve performance and reliability are discussed. A generic cost-benefit analysis and an expected life cycle cost reduction concludes that $90 million maintenance dollars might be avoided for a population of 3000 valves over 20 years.
{"title":"Analysis of safety relief valve proof test data to optimize lifecycle maintenance costs","authors":"R. Gross, S. Harris","doi":"10.1109/RAMS.2008.4925814","DOIUrl":"https://doi.org/10.1109/RAMS.2008.4925814","url":null,"abstract":"Proof test results were analyzed and compared with a proposed life cycle curve or hazard function and the limit of useful life. Relief valve proof testing procedures, statistical modeling, data collection processes, and time-in-service trends are presented. The resulting analysis of test data allows for the estimation of a probability of failure on demand (PFD). Extending maintenance intervals to the limit of useful life as well as methodologies and practices for improving relief valve performance and reliability are discussed. A generic cost-benefit analysis and an expected life cycle cost reduction concludes that $90 million maintenance dollars might be avoided for a population of 3000 valves over 20 years.","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":"131274909","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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