Pub Date : 2020-01-01DOI: 10.1109/RAMS48030.2020.9153712
J. Weglian, J. Riley, M. Presley
Large industrial facilities, such as commercial nuclear power plants, still require human operators to respond to abnormal conditions. Failures of these operators to perform the appropriate actions can lead to significant consequences. Human failure events (HFEs) are modeled in probabilistic risk assessment (PRA) models for these plants to consider the consequences of these failed actions. These PRA models explicitly consider various types of failures, including failures to align equipment prior to an event, which leaves that equipment unavailable to respond, and failures of human actions after the abnormal event that are designed to mitigate the event.
{"title":"Contribution of Risk from Human Failures in PRA Models","authors":"J. Weglian, J. Riley, M. Presley","doi":"10.1109/RAMS48030.2020.9153712","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153712","url":null,"abstract":"Large industrial facilities, such as commercial nuclear power plants, still require human operators to respond to abnormal conditions. Failures of these operators to perform the appropriate actions can lead to significant consequences. Human failure events (HFEs) are modeled in probabilistic risk assessment (PRA) models for these plants to consider the consequences of these failed actions. These PRA models explicitly consider various types of failures, including failures to align equipment prior to an event, which leaves that equipment unavailable to respond, and failures of human actions after the abnormal event that are designed to mitigate the event.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134405247","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 : 2020-01-01DOI: 10.1109/RAMS48030.2020.9153675
Wei Huang, Roy Andrada, D. Borja
This paper presents a reliability analysis for a 2-for-1 warm standby redundant RLG configuration subject to time varying thermal stress (temperature). Starting from reliability analysis at the component level on ring laser assemblies and support electronics box, a system level reliability model is developed for the configuration. To account for temperature’s time variation, the cumulative effect of exposure (or damage) models are used, along with the distribution parameter’s temperature dependency based on the Arrhenius model. An example is presented to demonstrate how the temperature’s time variation would affect the reliability at both component and system (2-for-l configuration) level. In conclusion, a proposed path-forward is outlined.
{"title":"Ring Laser Gyroscope Warm Standby Redundancy Subject to Wearout Failure and Time Varying Thermal Stresses","authors":"Wei Huang, Roy Andrada, D. Borja","doi":"10.1109/RAMS48030.2020.9153675","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153675","url":null,"abstract":"This paper presents a reliability analysis for a 2-for-1 warm standby redundant RLG configuration subject to time varying thermal stress (temperature). Starting from reliability analysis at the component level on ring laser assemblies and support electronics box, a system level reliability model is developed for the configuration. To account for temperature’s time variation, the cumulative effect of exposure (or damage) models are used, along with the distribution parameter’s temperature dependency based on the Arrhenius model. An example is presented to demonstrate how the temperature’s time variation would affect the reliability at both component and system (2-for-l configuration) level. In conclusion, a proposed path-forward is outlined.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117311785","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 : 2020-01-01DOI: 10.1109/RAMS48030.2020.9153606
Zekun Song, Yichen Wang, P. Zong, Lin Wang, G. Feng, Wenqian Kang
How to evaluate software reliability based on historical data of embedded software projects is one of the problems we have to face in practical engineering. Therefore, we establish a software reliability evaluation model based on code metrics. The model uses code metrics to score software reliability. This evaluation technique requires the aggregation of software code metrics into project metrics. What are the differences among different aggregation methods in the software reliability evaluation process, and which methods can improve the accuracy of the reliability evaluation model we have established are our concerns. In view of the above problems, we conduct an empirical study on the application of software code metric aggregation methods based on actual projects.
{"title":"An Empirical Study of Comparison of Code Metric Aggregation Methods and Software Reliability Evaluation","authors":"Zekun Song, Yichen Wang, P. Zong, Lin Wang, G. Feng, Wenqian Kang","doi":"10.1109/RAMS48030.2020.9153606","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153606","url":null,"abstract":"How to evaluate software reliability based on historical data of embedded software projects is one of the problems we have to face in practical engineering. Therefore, we establish a software reliability evaluation model based on code metrics. The model uses code metrics to score software reliability. This evaluation technique requires the aggregation of software code metrics into project metrics. What are the differences among different aggregation methods in the software reliability evaluation process, and which methods can improve the accuracy of the reliability evaluation model we have established are our concerns. In view of the above problems, we conduct an empirical study on the application of software code metric aggregation methods based on actual projects.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"526 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116220366","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 : 2020-01-01DOI: 10.1109/RAMS48030.2020.9153714
Xiangyu Zheng, N. Huang, Yanan Bai, Shuo Zhang
Research has shown that spatial patterns of congestion is neither compact as expected by typical model of cascade dynamics nor purely random as in percolation theory. Analyzing spatial patterns of congestion is critical for mining spatial-temporal characteristics of congestion evolution. Spatial patterns of congestion are the result of congestion interaction, which appears as the dependency relationship of the adjacent edges and the dependency relationship of the non-adjacent edges with a certain range in the network. Previous models which analyze spatial patterns of congestion mainly considers the dependency relationship of the directly connected edges, but lack the consideration of the dependency relationship of the indirectly connected edges. Therefore, this paper presents a fractal-cluster-based analytical model considering the dependency relationship of the indirectly connected edges to describe the dominant mechanism governing the formation and evolution of spatial pattern of congestion. First, we introduce the edge dependency coefficient to quantitatively describe the dependency strength of the adjacent edges. Next, we regard the basic fractal element of the network as a cluster and introduce the cluster dependency coefficient to quantitatively describe the dependency relationship of the non-adjacent edges with a certain range in the network. Finally, we construct a weighted network in which the weight of edges represents the congestion level of edges and introduce a novel load transfer mechanism to describe the results of congestion interaction. Based on this, a fractal-cluster-based congestion evolution model is established to analyze spatial patterns of congestion. To quantify spatial pattern, we use the fractal dimension of the weighted network dB (a measurement of objects’ irregularity). The simulation comparison results have verified the feasibility of this indicator. Furthermore, simulation results have shown that our proposed model is more in line with the observed congestion propagation process, which verifies the effectiveness of our proposed model. This work can give precious hints on which step of the process is responsible for the congestion duo to the its mechanistic analysis of spatial patterns.
{"title":"A Fractal-Cluster-Based Analytical Model for Spatial Pattern of Congestion","authors":"Xiangyu Zheng, N. Huang, Yanan Bai, Shuo Zhang","doi":"10.1109/RAMS48030.2020.9153714","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153714","url":null,"abstract":"Research has shown that spatial patterns of congestion is neither compact as expected by typical model of cascade dynamics nor purely random as in percolation theory. Analyzing spatial patterns of congestion is critical for mining spatial-temporal characteristics of congestion evolution. Spatial patterns of congestion are the result of congestion interaction, which appears as the dependency relationship of the adjacent edges and the dependency relationship of the non-adjacent edges with a certain range in the network. Previous models which analyze spatial patterns of congestion mainly considers the dependency relationship of the directly connected edges, but lack the consideration of the dependency relationship of the indirectly connected edges. Therefore, this paper presents a fractal-cluster-based analytical model considering the dependency relationship of the indirectly connected edges to describe the dominant mechanism governing the formation and evolution of spatial pattern of congestion. First, we introduce the edge dependency coefficient to quantitatively describe the dependency strength of the adjacent edges. Next, we regard the basic fractal element of the network as a cluster and introduce the cluster dependency coefficient to quantitatively describe the dependency relationship of the non-adjacent edges with a certain range in the network. Finally, we construct a weighted network in which the weight of edges represents the congestion level of edges and introduce a novel load transfer mechanism to describe the results of congestion interaction. Based on this, a fractal-cluster-based congestion evolution model is established to analyze spatial patterns of congestion. To quantify spatial pattern, we use the fractal dimension of the weighted network dB (a measurement of objects’ irregularity). The simulation comparison results have verified the feasibility of this indicator. Furthermore, simulation results have shown that our proposed model is more in line with the observed congestion propagation process, which verifies the effectiveness of our proposed model. This work can give precious hints on which step of the process is responsible for the congestion duo to the its mechanistic analysis of spatial patterns.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116881759","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 : 2020-01-01DOI: 10.1109/RAMS48030.2020.9153611
E. Bediako, Yisha Xiang, Susan Alaswad, Liao Ying, L. Xing
Assuring the reliability of crude unit pipelines in the downstream oil and gas industry is highly essential since unexpected failures of these pipelines can result in a number of negative impacts to the business, including safety, environmental, and economic impacts. The objective of this work is to understand the degradation behavior of the piping system so we can know in advance when the degraded pipeline will reach the minimum thickness threshold.
{"title":"Reliability Analysis of Crude Unit Overhead Piping Based on Wall Thickness Degradation Process","authors":"E. Bediako, Yisha Xiang, Susan Alaswad, Liao Ying, L. Xing","doi":"10.1109/RAMS48030.2020.9153611","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153611","url":null,"abstract":"Assuring the reliability of crude unit pipelines in the downstream oil and gas industry is highly essential since unexpected failures of these pipelines can result in a number of negative impacts to the business, including safety, environmental, and economic impacts. The objective of this work is to understand the degradation behavior of the piping system so we can know in advance when the degraded pipeline will reach the minimum thickness threshold.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116853655","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 : 2020-01-01DOI: 10.1109/RAMS48030.2020.9153718
Maskura Nafreen, Saikath Bhattacharya, L. Fiondella
With the increased interest to incorporate machine learning into software and systems, methods to characterize the impact of the reliability of machine learning are needed to ensure the reliability of the software and systems in which these algorithms reside. Towards this end, we build upon the architecture-based approach to software reliability modeling, which represents application reliability in terms of the component reliabilities and the probabilistic transitions between the components. Traditional architecture-based software reliability models consider all components to be deterministic software. We therefore extend this modeling approach to the case, where some components represent learning enabled components. Here, the reliability of a machine learning component is interpreted as the accuracy of its decisions, which is a common measure of classification algorithms. Moreover, we allow these machine learning components to be fault-tolerant in the sense that multiple diverse classifier algorithms are trained to guide decisions and the majority decision taken. We demonstrate the utility of the approach to assess the impact of machine learning on software reliability as well as illustrate the concept of reliability growth in machine learning. Finally, we validate past analytical results for a fault tolerant system composed of correlated components with real machine learning algorithms and data, demonstrating the analytical expression’s ability to accurately estimate the reliability of the fault tolerant machine learning component and subsequently the architecture-based software within which it resides.
{"title":"Architecture-based Software Reliability Incorporating Fault Tolerant Machine Learning","authors":"Maskura Nafreen, Saikath Bhattacharya, L. Fiondella","doi":"10.1109/RAMS48030.2020.9153718","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153718","url":null,"abstract":"With the increased interest to incorporate machine learning into software and systems, methods to characterize the impact of the reliability of machine learning are needed to ensure the reliability of the software and systems in which these algorithms reside. Towards this end, we build upon the architecture-based approach to software reliability modeling, which represents application reliability in terms of the component reliabilities and the probabilistic transitions between the components. Traditional architecture-based software reliability models consider all components to be deterministic software. We therefore extend this modeling approach to the case, where some components represent learning enabled components. Here, the reliability of a machine learning component is interpreted as the accuracy of its decisions, which is a common measure of classification algorithms. Moreover, we allow these machine learning components to be fault-tolerant in the sense that multiple diverse classifier algorithms are trained to guide decisions and the majority decision taken. We demonstrate the utility of the approach to assess the impact of machine learning on software reliability as well as illustrate the concept of reliability growth in machine learning. Finally, we validate past analytical results for a fault tolerant system composed of correlated components with real machine learning algorithms and data, demonstrating the analytical expression’s ability to accurately estimate the reliability of the fault tolerant machine learning component and subsequently the architecture-based software within which it resides.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116202533","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 : 2020-01-01DOI: 10.1109/RAMS48030.2020.9153653
S. Jayatilleka
The time spent from the conceptual stage to the final product design, development and deployment needs to be competitively small in order to be successful in today’s market place. Working with fewer samples within fewer numbers of design iterations, reducing the time between two design iterations, and achieving higher reliability among such iterations are some of the main challenges of the new product development (NPD) process. In this process, strategies of both systems and reliability engineering can be utilized for speedier goal achievement at different NDP stages. Examples from the appliance industry are used to demonstrate the utility of these strategies.
{"title":"Intersection of Systems and Reliability Engineering during New Product Development Process","authors":"S. Jayatilleka","doi":"10.1109/RAMS48030.2020.9153653","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153653","url":null,"abstract":"The time spent from the conceptual stage to the final product design, development and deployment needs to be competitively small in order to be successful in today’s market place. Working with fewer samples within fewer numbers of design iterations, reducing the time between two design iterations, and achieving higher reliability among such iterations are some of the main challenges of the new product development (NPD) process. In this process, strategies of both systems and reliability engineering can be utilized for speedier goal achievement at different NDP stages. Examples from the appliance industry are used to demonstrate the utility of these strategies.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117045207","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}
Diesel engine is the power source of warship and the core part of power system. Diesel engine not only has complex fuselage structure and many moving parts, but also is in a worse operating environment than other parts.
{"title":"Fault Diagnosis and Prediction Method for Valve Clearance of Diesel Engine Based on Linear Regression","authors":"Yinglai Liu, Wenbing Chang, Siyue Zhang, Shenghan Zhou","doi":"10.1109/RAMS48030.2020.9153697","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153697","url":null,"abstract":"Diesel engine is the power source of warship and the core part of power system. Diesel engine not only has complex fuselage structure and many moving parts, but also is in a worse operating environment than other parts.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123720413","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 : 2020-01-01DOI: 10.1109/RAMS48030.2020.9153625
J. Weglian, J. Riley, F. Ferrante
SUMMARY & CONCLUSIONSCommercial nuclear power plants use probabilistic risk assessment (PRA) models to gain insights into the risks associated with operating the plants. PRA models can be used to assess a variety of hazards such as internal events (transients and loss of coolant accidents), internal flooding, fire, seismic, and other hazards. Each model can provide risk insights, identify vulnerabilities, and identify significant equipment or operator actions. This information can be used to improve plant performance and safety via equipment or operational changes. It is often convenient, and for some risk-informed regulations it may be required, to combine all hazard PRA models into a single calculational model. This “one-top” model provides a single PRA fault tree that can be solved to generate the risk for all hazards. Combining these models can be a challenge, if they were built with different revisions to the internal events model at their core. The one-top model provides a convenient platform for assessing the risk from all of the modeled hazards in a single quantification. Certain software tools, such as the EPRI FRANX software, simplify the process of creating a one-top model. However, quantifying a one-top model has challenges, because each hazard model is built with different assumptions, data, biases, and uncertainty. Furthermore, when one hazard generates a risk value much larger than the risk value from another hazard, combining the results runs the risk of masking risk insights from the hazard with the smaller risk value. In addition to quantifying the one-top model for risk-informed applications that require or benefit from it, hazard models should still be quantified separately to get the risk insights the individual models provide.
{"title":"Combining Hazards into a Single-Top Fault Tree","authors":"J. Weglian, J. Riley, F. Ferrante","doi":"10.1109/RAMS48030.2020.9153625","DOIUrl":"https://doi.org/10.1109/RAMS48030.2020.9153625","url":null,"abstract":"SUMMARY & CONCLUSIONSCommercial nuclear power plants use probabilistic risk assessment (PRA) models to gain insights into the risks associated with operating the plants. PRA models can be used to assess a variety of hazards such as internal events (transients and loss of coolant accidents), internal flooding, fire, seismic, and other hazards. Each model can provide risk insights, identify vulnerabilities, and identify significant equipment or operator actions. This information can be used to improve plant performance and safety via equipment or operational changes. It is often convenient, and for some risk-informed regulations it may be required, to combine all hazard PRA models into a single calculational model. This “one-top” model provides a single PRA fault tree that can be solved to generate the risk for all hazards. Combining these models can be a challenge, if they were built with different revisions to the internal events model at their core. The one-top model provides a convenient platform for assessing the risk from all of the modeled hazards in a single quantification. Certain software tools, such as the EPRI FRANX software, simplify the process of creating a one-top model. However, quantifying a one-top model has challenges, because each hazard model is built with different assumptions, data, biases, and uncertainty. Furthermore, when one hazard generates a risk value much larger than the risk value from another hazard, combining the results runs the risk of masking risk insights from the hazard with the smaller risk value. In addition to quantifying the one-top model for risk-informed applications that require or benefit from it, hazard models should still be quantified separately to get the risk insights the individual models provide.","PeriodicalId":360096,"journal":{"name":"2020 Annual Reliability and Maintainability Symposium (RAMS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121660015","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：