Pub Date : 1900-01-01DOI: 10.1109/RAM.2017.7889756
Bentolhoda Jafary, L. Fiondella
Checkpointing is a technique to backup work at periodic intervals so that if computation fails it will not be necessary to restart from the beginning but will instead be able to restart from the latest checkpoint. Performing checkpointing operations requires time. Therefore, it is necessary to consider the tradeoff between the time to perform checkpointing operations and the time saved when computation restarts at a checkpoint. This paper presents a method to model the impact of correlated failures on a system that performs checkpointing. We map the checkpointing process to a state space model and superimpose a correlated life distribution. Examples illustrate that the model identifies the optimal number of checkpoints despite the negative impact of correlation on system reliability.
{"title":"Optimal checkpointing of fault tolerant systems subject to correlated failure","authors":"Bentolhoda Jafary, L. Fiondella","doi":"10.1109/RAM.2017.7889756","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889756","url":null,"abstract":"Checkpointing is a technique to backup work at periodic intervals so that if computation fails it will not be necessary to restart from the beginning but will instead be able to restart from the latest checkpoint. Performing checkpointing operations requires time. Therefore, it is necessary to consider the tradeoff between the time to perform checkpointing operations and the time saved when computation restarts at a checkpoint. This paper presents a method to model the impact of correlated failures on a system that performs checkpointing. We map the checkpointing process to a state space model and superimpose a correlated life distribution. Examples illustrate that the model identifies the optimal number of checkpoints despite the negative impact of correlation on system reliability.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"1 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":"131308012","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/RAM.2017.7889754
A. Syamsundar, D. E. Vijay Kumar
A failed component / system brought back to its functioning state after repair exhibits different failure intensity than before its failure. This happens because the system in which the component is functioning experiences deterioration with age or the component / system is a repaired one which is aged compared to a new component / system. These factors affect the failure intensity of the component / system. To model the failure behaviour of such a component / system a simple model, termed the geometric failure rate reduction model by Finkelstein, is proposed. This model effectively models the changed failure behaviour of the component / system under the above circumstances. The model, and its inference are described and its application to a repairable systems demonstrated.
{"title":"Geometric failure rate reduction model for the analysis of repairable systems","authors":"A. Syamsundar, D. E. Vijay Kumar","doi":"10.1109/RAM.2017.7889754","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889754","url":null,"abstract":"A failed component / system brought back to its functioning state after repair exhibits different failure intensity than before its failure. This happens because the system in which the component is functioning experiences deterioration with age or the component / system is a repaired one which is aged compared to a new component / system. These factors affect the failure intensity of the component / system. To model the failure behaviour of such a component / system a simple model, termed the geometric failure rate reduction model by Finkelstein, is proposed. This model effectively models the changed failure behaviour of the component / system under the above circumstances. The model, and its inference are described and its application to a repairable systems demonstrated.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"2016 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":"121449730","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/RAM.2017.7889771
J. Hewitt, Gary D. Braman
Quantitative Risk Assessment (QRA) is an effective element of the Safety Risk Management process that augments qualitative methods used in the past. QRA is based on Life Data Analysis [1] and can accurately predict future risk by analyzing the risk without corrective action (uncorrected risk), and then analyzing the risk with specific mitigating actions implemented (corrected risk). Before 2013, there was no uniformity among QRA limits or guidelines in the rotorcraft industry. A benchmarking activity was initiated to document the basis of established QRA risk guidelines in use or recommended by helicopter and engine manufacturers, regulatory authorities, and academia. Benchmarking is a focused process that enables changes that can lead to improvements in products, processes, and services. The anticipated outcome is a comparison of the performance level and best practices of numerous organizations in managing processes and procedures, which can improve the standard of operational excellence. The benchmarking process resulted in the significant milestone of industry/government agreement on uniform definitions and risk guidelines (numerical parameters) for all rotorcraft and for engines installed on multi engine rotorcraft. The new guidelines were adopted by consensus of the group and have been promulgated by incorporation into the FAA Rotorcraft Risk Analysis Handbook for application in the rotary wing aircraft industry. This work can serve as a model for other safety critical fields where standardized Quantitative Risk definitions and guidelines are needed.
定量风险评估(QRA)是安全风险管理过程的一个有效元素，它补充了过去使用的定性方法。QRA基于Life Data Analysis[1]，通过分析未采取纠正措施的风险(未纠正风险)，再分析采取了特定缓解措施的风险(已纠正风险)，可以准确预测未来风险。在2013年之前，旋翼飞机行业的QRA限制或指导方针没有统一。开展了一项基准测试活动，以记录直升机和发动机制造商、监管机构和学术界使用或推荐的已建立的QRA风险指南的基础。基准测试是一个有重点的过程，它支持能够导致产品、流程和服务改进的更改。预期的结果是对许多组织在管理过程和程序方面的性能水平和最佳实践进行比较，这可以提高卓越运营的标准。基准测试过程导致行业/政府就所有旋翼飞机和安装在多引擎旋翼飞机上的发动机的统一定义和风险指南(数值参数)达成重要里程碑。新的指导方针经小组协商一致通过，并已纳入美国联邦航空局旋翼飞机风险分析手册，以适用于旋翼飞机工业。这项工作可以作为需要标准化定量风险定义和指导方针的其他安全关键领域的模型。
{"title":"Uniform rotorcraft guidelines for quantitative risk assessment","authors":"J. Hewitt, Gary D. Braman","doi":"10.1109/RAM.2017.7889771","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889771","url":null,"abstract":"Quantitative Risk Assessment (QRA) is an effective element of the Safety Risk Management process that augments qualitative methods used in the past. QRA is based on Life Data Analysis [1] and can accurately predict future risk by analyzing the risk without corrective action (uncorrected risk), and then analyzing the risk with specific mitigating actions implemented (corrected risk). Before 2013, there was no uniformity among QRA limits or guidelines in the rotorcraft industry. A benchmarking activity was initiated to document the basis of established QRA risk guidelines in use or recommended by helicopter and engine manufacturers, regulatory authorities, and academia. Benchmarking is a focused process that enables changes that can lead to improvements in products, processes, and services. The anticipated outcome is a comparison of the performance level and best practices of numerous organizations in managing processes and procedures, which can improve the standard of operational excellence. The benchmarking process resulted in the significant milestone of industry/government agreement on uniform definitions and risk guidelines (numerical parameters) for all rotorcraft and for engines installed on multi engine rotorcraft. The new guidelines were adopted by consensus of the group and have been promulgated by incorporation into the FAA Rotorcraft Risk Analysis Handbook for application in the rotary wing aircraft industry. This work can serve as a model for other safety critical fields where standardized Quantitative Risk definitions and guidelines are needed.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"6 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":"127720405","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/RAM.2017.7889723
T. Dukes, Blair M. Schmidt, Yangyang Yu
The paper focuses on the current engineering practice on SMART (System Safety, Maintainability, Availability, Reliability, and Testability) engineering using Failure Modes, Effects and Criticality Analysis (FMECA) as the fundamental SMART knowledge base. The paper demonstrates General Atomics Aeronautical Systems' adaptation to the Department Of Defense's (DOD's) new Reliability and Maintainability (RAM) engineering trend, such as quantitative hazard analysis, Reliability-Centered Maintenance (RCM) analysis, and fault coverage analysis, using the traditional RAM tool — FMECA.
{"title":"FMECA-based analyses: A SMART foundation","authors":"T. Dukes, Blair M. Schmidt, Yangyang Yu","doi":"10.1109/RAM.2017.7889723","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889723","url":null,"abstract":"The paper focuses on the current engineering practice on SMART (System Safety, Maintainability, Availability, Reliability, and Testability) engineering using Failure Modes, Effects and Criticality Analysis (FMECA) as the fundamental SMART knowledge base. The paper demonstrates General Atomics Aeronautical Systems' adaptation to the Department Of Defense's (DOD's) new Reliability and Maintainability (RAM) engineering trend, such as quantitative hazard analysis, Reliability-Centered Maintenance (RCM) analysis, and fault coverage analysis, using the traditional RAM tool — FMECA.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"4 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":"130987804","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/RAM.2017.7889655
C. Jackson, Sana U. Qasisar, M. Ryan
The Value Driven Tradespace Exploration (VDTSE) framework developed in this paper is a new and sophisticated approach to optimizing reliability throughout design, and in so doing dynamically apportion reliability goals to all system elements. The VDTSE framework is an extension of existing approaches that have been successfully used to optimize many design dimensions. This allows (for example) reliability, cost and other design characteristics (such as weight, volume and speed) to be automatically and continually optimized throughout the design process. This represents a substantial improvement on ‘conventional’ approaches to reliability goal setting that involve ‘fixed’ reliability requirements that reflect a single scenario of ‘satisfactory’ performance. This results in a short-sited ‘binary’ approach to reliability value — the system is ‘satisfactory’ if it exceeds the requirement. No value is placed on exceeding requirements, nor is value assigned systems that do not meet strict requirements but may offer more ‘business’ value through other benefits such as reduced cost, weight or volume. For ‘conventional’ approaches that involve specifying reliability requirements, to result in optimal systems the customer needs to have exhaustively analyzed all plausible design configurations, accurately modeled all trends in emerging technology, and put forth a demonstrable requirement that aligns with their analysis. In short, the customer needs to enact the design process before the producer does — a process which is impractical and inefficient. The VDTSE framework outlined herein avoids all these issues. It uses component design characteristics (that include cost and reliability) to establish a tradespace of potential system design solutions. By establishing the concept of ‘value’ to be a function of design characteristics, a Pareto frontier can be identified which contains the set of al locally optimized design solutions. The VDTSE involves an algorithm that rapidly identifies the Pareto frontier from a large number of candidate designs. Finally, this allows the optimum design to be determined (in terms of organizational value) that also involves reliability goals apportioned to individual components and sub-systems.
{"title":"Value driven tradespace exploration: A new approach to optimize reliability specification and allocation","authors":"C. Jackson, Sana U. Qasisar, M. Ryan","doi":"10.1109/RAM.2017.7889655","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889655","url":null,"abstract":"The Value Driven Tradespace Exploration (VDTSE) framework developed in this paper is a new and sophisticated approach to optimizing reliability throughout design, and in so doing dynamically apportion reliability goals to all system elements. The VDTSE framework is an extension of existing approaches that have been successfully used to optimize many design dimensions. This allows (for example) reliability, cost and other design characteristics (such as weight, volume and speed) to be automatically and continually optimized throughout the design process. This represents a substantial improvement on ‘conventional’ approaches to reliability goal setting that involve ‘fixed’ reliability requirements that reflect a single scenario of ‘satisfactory’ performance. This results in a short-sited ‘binary’ approach to reliability value — the system is ‘satisfactory’ if it exceeds the requirement. No value is placed on exceeding requirements, nor is value assigned systems that do not meet strict requirements but may offer more ‘business’ value through other benefits such as reduced cost, weight or volume. For ‘conventional’ approaches that involve specifying reliability requirements, to result in optimal systems the customer needs to have exhaustively analyzed all plausible design configurations, accurately modeled all trends in emerging technology, and put forth a demonstrable requirement that aligns with their analysis. In short, the customer needs to enact the design process before the producer does — a process which is impractical and inefficient. The VDTSE framework outlined herein avoids all these issues. It uses component design characteristics (that include cost and reliability) to establish a tradespace of potential system design solutions. By establishing the concept of ‘value’ to be a function of design characteristics, a Pareto frontier can be identified which contains the set of al locally optimized design solutions. The VDTSE involves an algorithm that rapidly identifies the Pareto frontier from a large number of candidate designs. Finally, this allows the optimum design to be determined (in terms of organizational value) that also involves reliability goals apportioned to individual components and sub-systems.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"1 17 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":"127982607","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/RAM.2017.7889668
E. Rabiei, E. Droguett, M. Modarres
This study presents a new structural health monitoring framework for complex degradation processes such as degradation of composites under fatigue loading. Since early detection and measurement of an observable damage marker in composite is very difficult, the proposed framework is established based on identifying and then monitoring “indirect damage indicators”. Dynamic Bayesian Network is utilized to integrate relevant damage models with any available monitoring data as well as other influential parameters. As the damage evolution process in composites is not fully explored, a technique consisting of extended Particle Filtering and Support Vector Regression is implemented to simultaneously estimate the damage model parameters as well as damage states in the presence of multiple measurements. The method is then applied to predict the time to failure of the component.
{"title":"Damage monitoring and prognostics in composites via dynamic Bayesian networks","authors":"E. Rabiei, E. Droguett, M. Modarres","doi":"10.1109/RAM.2017.7889668","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889668","url":null,"abstract":"This study presents a new structural health monitoring framework for complex degradation processes such as degradation of composites under fatigue loading. Since early detection and measurement of an observable damage marker in composite is very difficult, the proposed framework is established based on identifying and then monitoring “indirect damage indicators”. Dynamic Bayesian Network is utilized to integrate relevant damage models with any available monitoring data as well as other influential parameters. As the damage evolution process in composites is not fully explored, a technique consisting of extended Particle Filtering and Support Vector Regression is implemented to simultaneously estimate the damage model parameters as well as damage states in the presence of multiple measurements. The method is then applied to predict the time to failure of the component.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"1 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":"128974941","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/RAM.2017.7889755
D. McLellan, K. Schneider
This paper details a maintenance model for the Silver Fox Small Unmanned Aerial System. The system is comprised of a number of mission critical, modular components as well as a number of mission enhancing components. A typical system is deployed for 180 days and consists of one aircraft, a spares kit, and a hangar queen aircraft. A discrete event simulation model is developed to evaluate the effects of maintenance planning decisions and spare parts provisioning on aircraft availability. The model explores using both the parts from the spares kit and cannibalized parts from the hangar queen in order to keep the aircraft availability high. When a part fails, it is sent back to the United States for refurbishment which takes a certain amount of time. We investigate the effects of spare parts provisioning, allowable maintenance time, and refurbishment lead time on several metrics. We show that changes in these policies have a significant impact on aircraft availability and capability.
{"title":"Maintenance planning and spare parts provisioning for a small unmanned aerial system","authors":"D. McLellan, K. Schneider","doi":"10.1109/RAM.2017.7889755","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889755","url":null,"abstract":"This paper details a maintenance model for the Silver Fox Small Unmanned Aerial System. The system is comprised of a number of mission critical, modular components as well as a number of mission enhancing components. A typical system is deployed for 180 days and consists of one aircraft, a spares kit, and a hangar queen aircraft. A discrete event simulation model is developed to evaluate the effects of maintenance planning decisions and spare parts provisioning on aircraft availability. The model explores using both the parts from the spares kit and cannibalized parts from the hangar queen in order to keep the aircraft availability high. When a part fails, it is sent back to the United States for refurbishment which takes a certain amount of time. We investigate the effects of spare parts provisioning, allowable maintenance time, and refurbishment lead time on several metrics. We show that changes in these policies have a significant impact on aircraft availability and capability.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"31 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":"116721000","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/RAM.2017.7889664
L. Hund, Daniel L. Campbell, Justin T. Newcomer
This document outlines a data-driven probabilistic approach to setting product acceptance testing limits. Product Specification (PS) limits are testing requirements for assuring that the product meets the product requirements. After identifying key manufacturing and performance parameters for acceptance testing, PS limits should be specified for these parameters, with the limits selected to assure that the unit will have a very high likelihood of meeting product requirements (barring any quality defects that would not be detected in acceptance testing). Because the settings for which the product requirements must be met is typically broader than the production acceptance testing space, PS limits should account for the difference between the acceptance testing setting relative to the worst-case setting. We propose an approach to setting PS limits that is based on demonstrating margin to the product requirement in the worst-case setting in which the requirement must be met. PS limits are then determined by considering the overall margin and uncertainty associated with a component requirement and then balancing this margin and uncertainty between the designer and producer. Specifically, after identifying parameters critical to component performance, we propose setting PS limits using a three step procedure: 1. Specify the acceptance testing and worst-case use-settings, the performance characteristic distributions in these two settings, and the mapping between these distributions. 2. Determine the PS limit in the worst-case use-setting by considering margin to the requirement and additional (epistemic) uncertainties. This step controls designer risk, namely the risk of producing product that violates requirements. 3. Define the PS limit for product acceptance testing by transforming the PS limit from the worst-case setting to the acceptance testing setting using the mapping between these distributions. Following this step, the producer risk is quantified by estimating the product scrap rate based on the projected acceptance testing distribution. The approach proposed here provides a framework for documenting the procedure and assumptions used to determine PS limits. This transparency in procedure will help inform what actions should occur when a unit violates a PS limit and how limits should change over time.
{"title":"Statistical guidance for setting product specification limits","authors":"L. Hund, Daniel L. Campbell, Justin T. Newcomer","doi":"10.1109/RAM.2017.7889664","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889664","url":null,"abstract":"This document outlines a data-driven probabilistic approach to setting product acceptance testing limits. Product Specification (PS) limits are testing requirements for assuring that the product meets the product requirements. After identifying key manufacturing and performance parameters for acceptance testing, PS limits should be specified for these parameters, with the limits selected to assure that the unit will have a very high likelihood of meeting product requirements (barring any quality defects that would not be detected in acceptance testing). Because the settings for which the product requirements must be met is typically broader than the production acceptance testing space, PS limits should account for the difference between the acceptance testing setting relative to the worst-case setting. We propose an approach to setting PS limits that is based on demonstrating margin to the product requirement in the worst-case setting in which the requirement must be met. PS limits are then determined by considering the overall margin and uncertainty associated with a component requirement and then balancing this margin and uncertainty between the designer and producer. Specifically, after identifying parameters critical to component performance, we propose setting PS limits using a three step procedure: 1. Specify the acceptance testing and worst-case use-settings, the performance characteristic distributions in these two settings, and the mapping between these distributions. 2. Determine the PS limit in the worst-case use-setting by considering margin to the requirement and additional (epistemic) uncertainties. This step controls designer risk, namely the risk of producing product that violates requirements. 3. Define the PS limit for product acceptance testing by transforming the PS limit from the worst-case setting to the acceptance testing setting using the mapping between these distributions. Following this step, the producer risk is quantified by estimating the product scrap rate based on the projected acceptance testing distribution. The approach proposed here provides a framework for documenting the procedure and assumptions used to determine PS limits. This transparency in procedure will help inform what actions should occur when a unit violates a PS limit and how limits should change over time.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"1 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":"115515070","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/RAM.2017.7889761
M. Krasich
Software reliability, its predictions and data analyses are mostly based on the number of faults; therefore fault mitigation and reliability growth achieved by mitigation of number of faults. The usual results are the final failure frequency of the delivered mature software. The early reliability prediction is needed at the beginning of the development phase to estimate reliability of the software and its effect of the product it is a part of. Since the discrete number of faults are expected to be observed and mitigated, the non-homogenous Poisson probability distribution comes as the preferred mathematical tool. In the case where during development process no reliability growth was achieved, the same mathematics would just yield parameters which would indicate no reliability changes or, in the worst case, reliability degradation (the growth parameter equal or greater than one). Krasich-Peterson model (patent pending) and Musa original model when used for early predictions are very similar except the first assumes power law fitting of the mitigated faults, whilst the latter model assumes constant rate of failure mitigation. Since the early reliability predictions use assumptions for function parameters derived from quality level of the software inspection, testing, and improvement process and also on the software size, complexity, its use profile, the single way of validating those assumptions and the parameters derived from them is to apply the same mathematics to the reliability estimation of software for early predictions covering its lifecycle. Regardless of what mathematical model is applied, for continuity and for meaningful conclusions and decisions regarding software reliability as well as the future use of such information on other projects, one method type of counting (discrete) distribution should be applied in the same organization throughout the software lifecycle. An additional benefit of such consistency is the ability to compare not only software development and use phases but to compare the different software development and quality and test practices.
{"title":"Mathematical models and software reliability can different mathematics fit all phases of SW lifecycle?","authors":"M. Krasich","doi":"10.1109/RAM.2017.7889761","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889761","url":null,"abstract":"Software reliability, its predictions and data analyses are mostly based on the number of faults; therefore fault mitigation and reliability growth achieved by mitigation of number of faults. The usual results are the final failure frequency of the delivered mature software. The early reliability prediction is needed at the beginning of the development phase to estimate reliability of the software and its effect of the product it is a part of. Since the discrete number of faults are expected to be observed and mitigated, the non-homogenous Poisson probability distribution comes as the preferred mathematical tool. In the case where during development process no reliability growth was achieved, the same mathematics would just yield parameters which would indicate no reliability changes or, in the worst case, reliability degradation (the growth parameter equal or greater than one). Krasich-Peterson model (patent pending) and Musa original model when used for early predictions are very similar except the first assumes power law fitting of the mitigated faults, whilst the latter model assumes constant rate of failure mitigation. Since the early reliability predictions use assumptions for function parameters derived from quality level of the software inspection, testing, and improvement process and also on the software size, complexity, its use profile, the single way of validating those assumptions and the parameters derived from them is to apply the same mathematics to the reliability estimation of software for early predictions covering its lifecycle. Regardless of what mathematical model is applied, for continuity and for meaningful conclusions and decisions regarding software reliability as well as the future use of such information on other projects, one method type of counting (discrete) distribution should be applied in the same organization throughout the software lifecycle. An additional benefit of such consistency is the ability to compare not only software development and use phases but to compare the different software development and quality and test practices.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"1112 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":"116061397","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/RAM.2017.7889730
P. Arrowsmith
Candidate test plans with 3 stress levels (L, M, H) were identified using the probability of zero failures at one or more stress levels Pr{ZFP1} as a target parameter. For a given sample size n, the allocations nL and nM are input variables. The optimization involves finding a minimum for the lower stress level, on the premise that plans with wider spread of the stress levels have smaller error of the time-to-failure (TTF) extrapolated to the use stress condition. The only constraint is equal spacing of the stress levels, in terms of the standardized stress (ξ). The proposed method does not require computation of the large sample approximate variance (Avar). The optimization can be conveniently done using a spreadsheet and is quite flexible, enabling different censor times to be used for each stress level and can be readily extended to 4 or more stress levels. Monte Carlo simulation of the candidate test plans was used to verify the assumption that the variance of the extrapolated TTF is proportional to the lower stress ξL, for a given allocation. The optimized test plans and variance of the estimated time to 10% failure are similar to those previously published, using the same planning values. Although the optimization method identifies acceptable candidate test plans, there may be other allocations (with slightly higher ξL) that give lower variance of the estimated TTF. However, the difference is typically within the resolution of the stress factor (e.g. ∆T <1 °C) and the uncertainty of the estimated parameter. Monte Carlo simulation can be used to fine tune candidate test plans found by the optimization method.
{"title":"Improved method for ALT plan optimization","authors":"P. Arrowsmith","doi":"10.1109/RAM.2017.7889730","DOIUrl":"https://doi.org/10.1109/RAM.2017.7889730","url":null,"abstract":"Candidate test plans with 3 stress levels (L, M, H) were identified using the probability of zero failures at one or more stress levels Pr{ZFP1} as a target parameter. For a given sample size n, the allocations nL and nM are input variables. The optimization involves finding a minimum for the lower stress level, on the premise that plans with wider spread of the stress levels have smaller error of the time-to-failure (TTF) extrapolated to the use stress condition. The only constraint is equal spacing of the stress levels, in terms of the standardized stress (ξ). The proposed method does not require computation of the large sample approximate variance (Avar). The optimization can be conveniently done using a spreadsheet and is quite flexible, enabling different censor times to be used for each stress level and can be readily extended to 4 or more stress levels. Monte Carlo simulation of the candidate test plans was used to verify the assumption that the variance of the extrapolated TTF is proportional to the lower stress ξL, for a given allocation. The optimized test plans and variance of the estimated time to 10% failure are similar to those previously published, using the same planning values. Although the optimization method identifies acceptable candidate test plans, there may be other allocations (with slightly higher ξL) that give lower variance of the estimated TTF. However, the difference is typically within the resolution of the stress factor (e.g. ∆T <1 °C) and the uncertainty of the estimated parameter. Monte Carlo simulation can be used to fine tune candidate test plans found by the optimization method.","PeriodicalId":138871,"journal":{"name":"2017 Annual Reliability and Maintainability Symposium (RAMS)","volume":"29 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":"125465785","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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