Pub Date : 2000-01-24DOI: 10.1109/RAMS.2000.816291
Meng-Lai Yin, C. L. Hyde, L. E. James
This paper addresses software reliability modeling issues at the early stage of a software development for large-scale software products. The hierarchical view of a software product provides the modeling framework at the system level. This paper shows how to take the hierarchical description and perform the system-level software reliability estimation using Petri net mechanisms. The Petri net modeling techniques are proposed for handling the dependency among software modules. Furthermore, this paper addresses issues of early-stage software reliability modeling when failure data is not available.
{"title":"A Petri-net approach for early-stage system-level software reliability estimation","authors":"Meng-Lai Yin, C. L. Hyde, L. E. James","doi":"10.1109/RAMS.2000.816291","DOIUrl":"https://doi.org/10.1109/RAMS.2000.816291","url":null,"abstract":"This paper addresses software reliability modeling issues at the early stage of a software development for large-scale software products. The hierarchical view of a software product provides the modeling framework at the system level. This paper shows how to take the hierarchical description and perform the system-level software reliability estimation using Petri net mechanisms. The Petri net modeling techniques are proposed for handling the dependency among software modules. Furthermore, this paper addresses issues of early-stage software reliability modeling when failure data is not available.","PeriodicalId":178321,"journal":{"name":"Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133708107","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 : 2000-01-24DOI: 10.1109/RAMS.2000.816336
I. Ruiz, E. Paniagua, J. Alberto, J. Sanabria
State analysis is an alternative functional approach to any other failure analysis technique that tries to face with current product and process design requirements. While other methodologies discover failures without a complete system understanding, state analysis focuses on finding system level failure modes by means of building a full functional model. State analysis grades failure modes in a more representative way by means of applying a customer reaction model, of combining factors, of following fuzzy rules and of including uncertainties. In this way, you are able to focus on important issues and produce field failure rate estimates with these results. Applying state analysis, a total of 635 issues have been identified in 12 sessions, of 2 hours, with an average of 7 people per session. An FMEA of a function was done with different people involved in order to compare. The results were: double time investment, 8 people involved and no critical subsystem interaction issues detected. State analysis has been found an intuitive, comprehensive and easy tool to analyze product functions, failures and design weaknesses within the IPD (integrated product design) environment.
{"title":"State analysis: an alternative approach to FMEA, FTA and Markov analysis","authors":"I. Ruiz, E. Paniagua, J. Alberto, J. Sanabria","doi":"10.1109/RAMS.2000.816336","DOIUrl":"https://doi.org/10.1109/RAMS.2000.816336","url":null,"abstract":"State analysis is an alternative functional approach to any other failure analysis technique that tries to face with current product and process design requirements. While other methodologies discover failures without a complete system understanding, state analysis focuses on finding system level failure modes by means of building a full functional model. State analysis grades failure modes in a more representative way by means of applying a customer reaction model, of combining factors, of following fuzzy rules and of including uncertainties. In this way, you are able to focus on important issues and produce field failure rate estimates with these results. Applying state analysis, a total of 635 issues have been identified in 12 sessions, of 2 hours, with an average of 7 people per session. An FMEA of a function was done with different people involved in order to compare. The results were: double time investment, 8 people involved and no critical subsystem interaction issues detected. State analysis has been found an intuitive, comprehensive and easy tool to analyze product functions, failures and design weaknesses within the IPD (integrated product design) environment.","PeriodicalId":178321,"journal":{"name":"Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131950255","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 : 2000-01-24DOI: 10.1109/RAMS.2000.816280
M. A. Morris
For many products, the application of reliability and maintainability (R&M) is mature. From communications to transportation, the people who work on improving the performance of products deserve much credit. Digital networks to improve communication clarity and performance, one hundred thousand miles between scheduled tune-ups, the list could go on and on. Unfortunately, the application of R&M in the world of machinery and equipment that produces these wonderful products is sometimes far from mature. The good news, there is still some low hanging fruit, some opportunities for real and significant improvement. The motivation for R&M is financial. The intent of R&M is to drive the cost of equipment performance to an economic minimum. To accomplish improved economic performance we must be concerned with the selection of appropriate figures of merit. There is a keen interest in studying and improving those things that are in the control space of the manufacturer of machinery or equipment. This may include the addition of blocking and starving events to current data collection strategies. To accomplish a real and significant reduction in the frequency of failure, we must also implement a system to support lessons learned. QS-9000 and the Tooling & Equipment Supplement provide a sensible structure for an effective R&M program.
{"title":"Improving performance of machinery and equipment","authors":"M. A. Morris","doi":"10.1109/RAMS.2000.816280","DOIUrl":"https://doi.org/10.1109/RAMS.2000.816280","url":null,"abstract":"For many products, the application of reliability and maintainability (R&M) is mature. From communications to transportation, the people who work on improving the performance of products deserve much credit. Digital networks to improve communication clarity and performance, one hundred thousand miles between scheduled tune-ups, the list could go on and on. Unfortunately, the application of R&M in the world of machinery and equipment that produces these wonderful products is sometimes far from mature. The good news, there is still some low hanging fruit, some opportunities for real and significant improvement. The motivation for R&M is financial. The intent of R&M is to drive the cost of equipment performance to an economic minimum. To accomplish improved economic performance we must be concerned with the selection of appropriate figures of merit. There is a keen interest in studying and improving those things that are in the control space of the manufacturer of machinery or equipment. This may include the addition of blocking and starving events to current data collection strategies. To accomplish a real and significant reduction in the frequency of failure, we must also implement a system to support lessons learned. QS-9000 and the Tooling & Equipment Supplement provide a sensible structure for an effective R&M program.","PeriodicalId":178321,"journal":{"name":"Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115184314","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 : 1900-01-01DOI: 10.1109/RAMS.2000.816303
D. Coit
A procedure is presented to prioritize system reliability prediction activities once a preliminary reliability prediction has been determined. Time and budgetary constraints impose limitations on the extent of analyses and testing needed to determine component reliability estimates. Therefore, it becomes necessary to allocate limited resources in accordance with their system-level impact. The variance of the system reliability estimate is decomposed so that the effect of individual components within the system can be compared and ranked. The reliability prediction prioritization index (RPPI) is defined to allow a comparison of components and to provide a relative ranking of components that can be used to separate the components into two priority groups. The separation is based on whether a decrease of the component reliability estimate variance meaningfully decreases the system reliability estimate variance. The procedure is demonstrated on an automatic train control example.
{"title":"System reliability prediction prioritization strategy","authors":"D. Coit","doi":"10.1109/RAMS.2000.816303","DOIUrl":"https://doi.org/10.1109/RAMS.2000.816303","url":null,"abstract":"A procedure is presented to prioritize system reliability prediction activities once a preliminary reliability prediction has been determined. Time and budgetary constraints impose limitations on the extent of analyses and testing needed to determine component reliability estimates. Therefore, it becomes necessary to allocate limited resources in accordance with their system-level impact. The variance of the system reliability estimate is decomposed so that the effect of individual components within the system can be compared and ranked. The reliability prediction prioritization index (RPPI) is defined to allow a comparison of components and to provide a relative ranking of components that can be used to separate the components into two priority groups. The separation is based on whether a decrease of the component reliability estimate variance meaningfully decreases the system reliability estimate variance. The procedure is demonstrated on an automatic train control example.","PeriodicalId":178321,"journal":{"name":"Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123355243","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 : 1900-01-01DOI: 10.1109/RAMS.2000.816323
F. Safie, R.L. Belyeu
As part of NASA's intensive effort to incorporate quantitative risk assessment (QRA) tools in the Agency's decision-making process concerning Space Shuttle risk, NASA has developed a powerful risk assessment tool called the quantitative risk assessment system (QRAS). The QRAS is a tool designed to estimate Space Shuttle risk and evaluate Space Shuttle upgrades. This paper presents an overview of the QRAS with focus on its application for evaluating the risk reduction due to proposed Space Shuttle upgrades. The application includes a case study from the Space Shuttle main engine (SSME). The QRAS overview section of the paper includes the QRAS development process, the technical approach to model development, the QRA quantification methods and techniques, and observations concerning the complex modeling involved in QRAS. The application section of the paper describes a practical case study using QRAS models for evaluating critical Space Shuttle Program upgrades, specifically a proposed SSME nozzle upgrade. This paper presents the method for evaluating the proposed upgrade by comparing the current nozzle (old design with well-established probabilistic models) to the channel wall nozzle (new design at the preliminary design level).
{"title":"NASA new approach for evaluating risk reduction due to Space Shuttle upgrades","authors":"F. Safie, R.L. Belyeu","doi":"10.1109/RAMS.2000.816323","DOIUrl":"https://doi.org/10.1109/RAMS.2000.816323","url":null,"abstract":"As part of NASA's intensive effort to incorporate quantitative risk assessment (QRA) tools in the Agency's decision-making process concerning Space Shuttle risk, NASA has developed a powerful risk assessment tool called the quantitative risk assessment system (QRAS). The QRAS is a tool designed to estimate Space Shuttle risk and evaluate Space Shuttle upgrades. This paper presents an overview of the QRAS with focus on its application for evaluating the risk reduction due to proposed Space Shuttle upgrades. The application includes a case study from the Space Shuttle main engine (SSME). The QRAS overview section of the paper includes the QRAS development process, the technical approach to model development, the QRA quantification methods and techniques, and observations concerning the complex modeling involved in QRAS. The application section of the paper describes a practical case study using QRAS models for evaluating critical Space Shuttle Program upgrades, specifically a proposed SSME nozzle upgrade. This paper presents the method for evaluating the proposed upgrade by comparing the current nozzle (old design with well-established probabilistic models) to the channel wall nozzle (new design at the preliminary design level).","PeriodicalId":178321,"journal":{"name":"Annual Reliability and Maintainability Symposium. 2000 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.00CH37055)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125548292","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}