Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457837
Cesar Ruiz, Seyyed Farid Hashemian, Haitao Liao
The reliability of a highly reliable product is often estimated through accelerated testing with one or multiple stressors and well-designed stress profiles. Since the parameters of the product's life-stress relationship will change in response to stress variation, it is desirable to precisely control the designed testing conditions. However, widely used testing equipment, such as an environmental chamber, may not always meet such expectations with respect to the required level of accuracy. This may result in changes in the life-stress relationship during the test and, if ignored, potentially diminish the accuracy of reliability extrapolation at the use condition. In this paper, we propose a physics-informed statistical learning framework for product reliability prediction via accelerated testing with imperfectly controlled testing conditions. The proposed stress profile representation method and statistical estimation procedure partially relax the requirements for stringent control of applied stresses during accelerated testing. A dataset from a capacitor accelerated test involving both voltage and temperature stressors is modified and used to illustrate the proposed methodology. The results show that the proposed methodology is a useful tool for reliability prediction and is robust to moderate and continuous changes in accelerated testing conditions while requiring minimal added knowledge from the end user's perspective.
{"title":"Reliability Prediction via Accelerated Testing with Imperfectly Controlled Conditions","authors":"Cesar Ruiz, Seyyed Farid Hashemian, Haitao Liao","doi":"10.1109/RAMS51492.2024.10457837","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457837","url":null,"abstract":"The reliability of a highly reliable product is often estimated through accelerated testing with one or multiple stressors and well-designed stress profiles. Since the parameters of the product's life-stress relationship will change in response to stress variation, it is desirable to precisely control the designed testing conditions. However, widely used testing equipment, such as an environmental chamber, may not always meet such expectations with respect to the required level of accuracy. This may result in changes in the life-stress relationship during the test and, if ignored, potentially diminish the accuracy of reliability extrapolation at the use condition. In this paper, we propose a physics-informed statistical learning framework for product reliability prediction via accelerated testing with imperfectly controlled testing conditions. The proposed stress profile representation method and statistical estimation procedure partially relax the requirements for stringent control of applied stresses during accelerated testing. A dataset from a capacitor accelerated test involving both voltage and temperature stressors is modified and used to illustrate the proposed methodology. The results show that the proposed methodology is a useful tool for reliability prediction and is robust to moderate and continuous changes in accelerated testing conditions while requiring minimal added knowledge from the end user's perspective.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"33 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531172","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 : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457642
Miklós Á. Szidarovszky
Traditionally, calculating the life cycle reliability of a system across multiple phases would be obtained by calculating the reliability of the components and the system in each phase independently, and then combining those individual phase reliabilities into an overall life cycle reliability. This paper showcases the usage of the duty cycle to combine all failure rate adjustments, e.g. environments, dormancy etc., and life cycle phases to remove the need to calculate individual failure rates for components and simplify the Reliability Block Diagram (RBD) for the system. This methodology allows an analytical reliability approach on a multi-phased life cycle using an analytical RBD as opposed to a phase-based simulation RBD. A comparison of the results between the methodologies, for a system that has a multi-phase life cycle, confirms that combining multiple factors into the duty cycle(s) allows for the use of an analytical RBD as an alternate to a phase-based simulation RBD, with the limitation that the failure rate is constant in each phase of the life cycle.
{"title":"Estimating Multi-Phase Life Cycle Reliability by Combining Failure Rate Adjustments into Duty Cycle","authors":"Miklós Á. Szidarovszky","doi":"10.1109/RAMS51492.2024.10457642","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457642","url":null,"abstract":"Traditionally, calculating the life cycle reliability of a system across multiple phases would be obtained by calculating the reliability of the components and the system in each phase independently, and then combining those individual phase reliabilities into an overall life cycle reliability. This paper showcases the usage of the duty cycle to combine all failure rate adjustments, e.g. environments, dormancy etc., and life cycle phases to remove the need to calculate individual failure rates for components and simplify the Reliability Block Diagram (RBD) for the system. This methodology allows an analytical reliability approach on a multi-phased life cycle using an analytical RBD as opposed to a phase-based simulation RBD. A comparison of the results between the methodologies, for a system that has a multi-phase life cycle, confirms that combining multiple factors into the duty cycle(s) allows for the use of an analytical RBD as an alternate to a phase-based simulation RBD, with the limitation that the failure rate is constant in each phase of the life cycle.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"12 7","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530657","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 : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457737
Wei Huang, Roy Andrada, Kristen Holman, D. Borja, Koki Ho
This paper presents a preliminary assessment of operational availability of a LEO satellite constellation with a single orbital plane. A new model is developed. Unlike traditional availability models developed from using Markov chains, this model derives expected service downtimes from probability distributions directly, due to the unrepairable nature of a failed satellite on orbit. Two types of service downtimes are considered: one attributed to redistributing remaining satellites to restore full coverage of the orbit if there are spare satellites still available, and the other attributed to a partial loss of the orbital coverage due to a permanent gap created by an additional satellite failure if there is no spare available. This model establishes a baseline for validation and verification of any numerical assessment tool to be developed in the future for complex LEO satellite constellations with multiple orbital planes, due to the fact that the single orbital plane constellation is a special case of complex constellations by setting the number of orbital planes equal to one and the analysis results obtained from the tool with such setting should be identical or close to the ones obtained from the analytical model presented here. An example is presented. And a proposed path-forward to develop a comprehensive LEO satellite availability assessment tool is outlined at the end of the paper.
{"title":"A Preliminary Availability Assessment of A LEO Satellite Constellation","authors":"Wei Huang, Roy Andrada, Kristen Holman, D. Borja, Koki Ho","doi":"10.1109/RAMS51492.2024.10457737","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457737","url":null,"abstract":"This paper presents a preliminary assessment of operational availability of a LEO satellite constellation with a single orbital plane. A new model is developed. Unlike traditional availability models developed from using Markov chains, this model derives expected service downtimes from probability distributions directly, due to the unrepairable nature of a failed satellite on orbit. Two types of service downtimes are considered: one attributed to redistributing remaining satellites to restore full coverage of the orbit if there are spare satellites still available, and the other attributed to a partial loss of the orbital coverage due to a permanent gap created by an additional satellite failure if there is no spare available. This model establishes a baseline for validation and verification of any numerical assessment tool to be developed in the future for complex LEO satellite constellations with multiple orbital planes, due to the fact that the single orbital plane constellation is a special case of complex constellations by setting the number of orbital planes equal to one and the analysis results obtained from the tool with such setting should be identical or close to the ones obtained from the analytical model presented here. An example is presented. And a proposed path-forward to develop a comprehensive LEO satellite availability assessment tool is outlined at the end of the paper.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"292 5","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530966","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 : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457690
Matthew Litton, D. Drusinsky, J. B. Michael
The proliferation of uncrewed aerial vehicles, also known as drones, raises concerns about their safe and reliable integration with conventional aircraft. Today, drones are used commercially for wide-ranging applications such as infrastructure inspection, agriculture, and logistics, necessitating their further integration into the airspace ecosystem. Airspace management currently depends on human operators to perform deconfliction and emergency management, but the removal of human operators requires drones to possess autonomous decision-making capabilities for real-time conflict resolution and collision detection. We analyze data from U.S. military-developed detect-and-avoid algorithms to demonstrate the ability to automatically learn lightweight and effective models of collision detection. Then, we discuss how such models can be easily and cheaply developed for deployment on drones as part of autonomous detect-and-avoid systems. Such systems can provide assurances to regulators and other stakeholders about the reliable integration of drones into the national airspace system. (This research is sponsored by the U.S. Department of the Navy.)
{"title":"Assuring Reliability of Autonomous Commercial Drones in the National Airspace","authors":"Matthew Litton, D. Drusinsky, J. B. Michael","doi":"10.1109/RAMS51492.2024.10457690","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457690","url":null,"abstract":"The proliferation of uncrewed aerial vehicles, also known as drones, raises concerns about their safe and reliable integration with conventional aircraft. Today, drones are used commercially for wide-ranging applications such as infrastructure inspection, agriculture, and logistics, necessitating their further integration into the airspace ecosystem. Airspace management currently depends on human operators to perform deconfliction and emergency management, but the removal of human operators requires drones to possess autonomous decision-making capabilities for real-time conflict resolution and collision detection. We analyze data from U.S. military-developed detect-and-avoid algorithms to demonstrate the ability to automatically learn lightweight and effective models of collision detection. Then, we discuss how such models can be easily and cheaply developed for deployment on drones as part of autonomous detect-and-avoid systems. Such systems can provide assurances to regulators and other stakeholders about the reliable integration of drones into the national airspace system. (This research is sponsored by the U.S. Department of the Navy.)","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"65 4","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531179","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 : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457672
Michael Z. Spivey
With exact lifetime data using maximum likelihood to estimate the scale and threshold parameters for a two-parameter exponential model is quite simple. However, in the presence of interval-censored data these calculations become much more difficult, especially when the censoring is random. In this paper we discuss the mathematics underlying the determination of the maximum likelihood estimators for both the scale and threshold parameters for the two-parameter exponential model in the presence of random interval-censored data. In addition, we prove a few theoretical results concerning the maximum likelihood estimators, results that greatly restrict the situations under which the log-likelihood function could have more than one local maximum. Finally, we present results concerning the speed and accuracy of two different methods for determining these maximum likelihood estimators numerically.
{"title":"Maximum Likelihood Estimation with the Two-Parameter Exponential Model and Interval-Censored Data","authors":"Michael Z. Spivey","doi":"10.1109/RAMS51492.2024.10457672","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457672","url":null,"abstract":"With exact lifetime data using maximum likelihood to estimate the scale and threshold parameters for a two-parameter exponential model is quite simple. However, in the presence of interval-censored data these calculations become much more difficult, especially when the censoring is random. In this paper we discuss the mathematics underlying the determination of the maximum likelihood estimators for both the scale and threshold parameters for the two-parameter exponential model in the presence of random interval-censored data. In addition, we prove a few theoretical results concerning the maximum likelihood estimators, results that greatly restrict the situations under which the log-likelihood function could have more than one local maximum. Finally, we present results concerning the speed and accuracy of two different methods for determining these maximum likelihood estimators numerically.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"63 7","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530665","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 : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457711
Cher Ming Tan, Steve Wang, Josh MP Xiong, Eloise CY Shih
This study focuses on the unexpected early reliability failure of an electronic network product for 5G transmission on the train system. The product began to experience communication failures after deployed in a European country for around 2 years. The investigation revealed that the electronic component inside the product had an open circuit, which was caused by corrosion from sulfur in the environment. Upon detailed analysis of the usage conditions at the customers' sites, it was discovered that the environment was located near a rail line in close proximity to a hot spring. This condition led to high levels of sulfur-containing gases. Detailed analyses were conducted to investigate whether this environment could be the primary cause of the premature failure, and the analysis yielded compelling evidence indicating that the hostile sulfur-containing gases are responsible for the rapid degradation of reliability and subsequent failure. Remarkably, the customers were not aware of such hostile environments, likely due to their lack of awareness regarding its impact on the products, and hence such environment was not made known to us. Aware of the possible hostile environment that the products will be exposed to, proactive steps are implemented including the specification of environmental conditions for usage, the selection of anti-sulphuration components, and the inclusion of protective coating on the circuit boards of the product.
{"title":"Pre-Mature Reliability Degradation of 5G Network Electronics in Unknown Hostile Environment","authors":"Cher Ming Tan, Steve Wang, Josh MP Xiong, Eloise CY Shih","doi":"10.1109/RAMS51492.2024.10457711","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457711","url":null,"abstract":"This study focuses on the unexpected early reliability failure of an electronic network product for 5G transmission on the train system. The product began to experience communication failures after deployed in a European country for around 2 years. The investigation revealed that the electronic component inside the product had an open circuit, which was caused by corrosion from sulfur in the environment. Upon detailed analysis of the usage conditions at the customers' sites, it was discovered that the environment was located near a rail line in close proximity to a hot spring. This condition led to high levels of sulfur-containing gases. Detailed analyses were conducted to investigate whether this environment could be the primary cause of the premature failure, and the analysis yielded compelling evidence indicating that the hostile sulfur-containing gases are responsible for the rapid degradation of reliability and subsequent failure. Remarkably, the customers were not aware of such hostile environments, likely due to their lack of awareness regarding its impact on the products, and hence such environment was not made known to us. Aware of the possible hostile environment that the products will be exposed to, proactive steps are implemented including the specification of environmental conditions for usage, the selection of anti-sulphuration components, and the inclusion of protective coating on the circuit boards of the product.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"266 9","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531015","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 : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457788
Ayswarya Rajagopalan, Bradley Guerke, Jimmy Jun-Min Yang
The 4-Corner testing suite (4C) validates the drive's ability to perform under stress by cycling the drive through four extreme corners of thermal and voltage stress (4C- TVS) and periodic sudden power loss at temperature (4C-SPL), all while stressing the drive with workload. The test conditions such as voltage fluctuations, temperature above the product specification are the accelerating factors that simulates the real-world failures that the drive may encounter during product life cycle. The early phases of testing firmware maturity rely on bench validation tests to evaluate the stability of the firmware. These tests are targeted tests that validate the system as a whole unit and may not cover the temperature and voltage ranges that the drives are specified for including margins. Running the 4C-tests during the early phase of the project helps to uncover issues that may remain unnoticed until later phases of the project. Reliability SSD validation cycle involves testing the stability and performance of the drive or DUT at various stages of product development. There are 4 stages identified in line with the product maturity that are Pre-Engineering verification Testing (Pre-EVT), Engineering Verification Testing (EVT), Design Verification Testing (DVT) and Reliability Demonstration Test (RDT). Various tests are run at each stage of product development to identify issues, debug failures, and validate the fix. This process is repeated until the product is stable. The time between the Pre-EVT and DVT phase is about six months. The benefit of the 4C testing suite during the early firmware development phases such as Pre- Evt has seen an increased improvement in product stability. Early discovery allows for more comprehensive firmware and hardware solutions and better validation. •Reduces the debug time in later stages of the product development where issues are more complex to fix. •Lesser surprise failures in later stages of the product. •Better stability and confidence in the product. The target tests during the pre- EVT phase are bench validation test that test the drive feature by feature on limited drive population. The 4C tests on the other hand test the drive as a whole entity on larger volume of drives. The increased drive count and stressful environment increases the possibility of catching the failures as compared to bench validation tests. In one of the product evaluations, 4C was run during the pre-EVT phase. The run detected many failures such as power failures, bad block count increase, firmware issues etc., that were not caught by the bench validation tests. The issues uncovered during the run helped in improving the bench validation tests and increased overall quality of the product.
{"title":"Early Detection of Failure in Solid State Devices Using 4 Corner Targeted Testing","authors":"Ayswarya Rajagopalan, Bradley Guerke, Jimmy Jun-Min Yang","doi":"10.1109/RAMS51492.2024.10457788","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457788","url":null,"abstract":"The 4-Corner testing suite (4C) validates the drive's ability to perform under stress by cycling the drive through four extreme corners of thermal and voltage stress (4C- TVS) and periodic sudden power loss at temperature (4C-SPL), all while stressing the drive with workload. The test conditions such as voltage fluctuations, temperature above the product specification are the accelerating factors that simulates the real-world failures that the drive may encounter during product life cycle. The early phases of testing firmware maturity rely on bench validation tests to evaluate the stability of the firmware. These tests are targeted tests that validate the system as a whole unit and may not cover the temperature and voltage ranges that the drives are specified for including margins. Running the 4C-tests during the early phase of the project helps to uncover issues that may remain unnoticed until later phases of the project. Reliability SSD validation cycle involves testing the stability and performance of the drive or DUT at various stages of product development. There are 4 stages identified in line with the product maturity that are Pre-Engineering verification Testing (Pre-EVT), Engineering Verification Testing (EVT), Design Verification Testing (DVT) and Reliability Demonstration Test (RDT). Various tests are run at each stage of product development to identify issues, debug failures, and validate the fix. This process is repeated until the product is stable. The time between the Pre-EVT and DVT phase is about six months. The benefit of the 4C testing suite during the early firmware development phases such as Pre- Evt has seen an increased improvement in product stability. Early discovery allows for more comprehensive firmware and hardware solutions and better validation. •Reduces the debug time in later stages of the product development where issues are more complex to fix. •Lesser surprise failures in later stages of the product. •Better stability and confidence in the product. The target tests during the pre- EVT phase are bench validation test that test the drive feature by feature on limited drive population. The 4C tests on the other hand test the drive as a whole entity on larger volume of drives. The increased drive count and stressful environment increases the possibility of catching the failures as compared to bench validation tests. In one of the product evaluations, 4C was run during the pre-EVT phase. The run detected many failures such as power failures, bad block count increase, firmware issues etc., that were not caught by the bench validation tests. The issues uncovered during the run helped in improving the bench validation tests and increased overall quality of the product.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"264 5","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531016","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 : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457719
Matthias Volk, Falak Sher, J. Katoen, M. Stoelinga
This paper presents SAFEST, a powerful tool for modelling and analyzing both static and dynamic fault trees. Dynamic fault trees (DFTs) extend standard fault trees by providing support for faithfully modelling spare management, functional dependencies, and order-dependent failures. The SAFEST tool provides efficient and powerful analysis of DFTs via probabilistic model checking – a rigorous, automated analysis technique for probabilistic systems. The backbone of the analysis is based on efficient state space generation. Several optimization techniques are incorporated, such as exploiting irrelevant failures, symmetries, and independent modules. Probabilistic model checking allows to analyze the resulting state space with respect to a wide range of measures of interest. In addition, an approximation approach is provided that builds only parts of the state space and allows to iteratively refine the computations up to the desired accuracy. The SAFEST tool provides a graphical user interface for creating, generating, simulating, and simplifying fault trees as well as visualizing the results from the fault tree analysis. SAFEST is state of the art for DFT analysis, as demonstrated by an experimental evaluation and comparison with existing tools. In addition, SAFEST and DFT models have been applied in a variety of case studies, including vehicle guidance systems, train operations in railway station areas, and energy systems such as (nuclear) power plants.
{"title":"SAFEST: Fault Tree Analysis Via Probabilistic Model Checking","authors":"Matthias Volk, Falak Sher, J. Katoen, M. Stoelinga","doi":"10.1109/RAMS51492.2024.10457719","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457719","url":null,"abstract":"This paper presents SAFEST, a powerful tool for modelling and analyzing both static and dynamic fault trees. Dynamic fault trees (DFTs) extend standard fault trees by providing support for faithfully modelling spare management, functional dependencies, and order-dependent failures. The SAFEST tool provides efficient and powerful analysis of DFTs via probabilistic model checking – a rigorous, automated analysis technique for probabilistic systems. The backbone of the analysis is based on efficient state space generation. Several optimization techniques are incorporated, such as exploiting irrelevant failures, symmetries, and independent modules. Probabilistic model checking allows to analyze the resulting state space with respect to a wide range of measures of interest. In addition, an approximation approach is provided that builds only parts of the state space and allows to iteratively refine the computations up to the desired accuracy. The SAFEST tool provides a graphical user interface for creating, generating, simulating, and simplifying fault trees as well as visualizing the results from the fault tree analysis. SAFEST is state of the art for DFT analysis, as demonstrated by an experimental evaluation and comparison with existing tools. In addition, SAFEST and DFT models have been applied in a variety of case studies, including vehicle guidance systems, train operations in railway station areas, and energy systems such as (nuclear) power plants.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"264 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531018","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 : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457726
Lukmon Rasaq, Korbin Ferguson, William Teasley, Max Xu, Kyle E. Blond, O. P. Yadav
This paper presents a framework to evaluate sensor solutions and maintenance strategies using performance metric and genetic algorithm to mitigate future Boeing 767 (B767) Airworthiness Directives (ADs). The results obtained can inform predictive maintenance models and reliability improvements to mitigate the costs of AD for commercial and military B767 operators.
{"title":"Sensor and Maintenance Strategy Evaluation for Boeing 767 Commercial Fleets","authors":"Lukmon Rasaq, Korbin Ferguson, William Teasley, Max Xu, Kyle E. Blond, O. P. Yadav","doi":"10.1109/RAMS51492.2024.10457726","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457726","url":null,"abstract":"This paper presents a framework to evaluate sensor solutions and maintenance strategies using performance metric and genetic algorithm to mitigate future Boeing 767 (B767) Airworthiness Directives (ADs). The results obtained can inform predictive maintenance models and reliability improvements to mitigate the costs of AD for commercial and military B767 operators.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"107 3","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531033","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 : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457807
Kenneth Lu, Margarita Hiett, Ernest Vincent Cross, Michael Reposa, Aron Kain, Erik Davis
Advances in Artificial Intelligence and Machine Learning AI/ML have demonstrated enormous potential in improving and optimizing condition-based maintenance processes. In this paper, we present novel research that leverages the power of probabilistic programming and hybrid-AI that combines domain knowledge with data to create an effective analytic capability that monitors (in real-time) the health and status of a Robotic Combat Vehicle, next generation prototype ground vehicle for the US Army.
{"title":"Hybrid-AI Approach to Health Monitoring of Vehicle Control System","authors":"Kenneth Lu, Margarita Hiett, Ernest Vincent Cross, Michael Reposa, Aron Kain, Erik Davis","doi":"10.1109/RAMS51492.2024.10457807","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457807","url":null,"abstract":"Advances in Artificial Intelligence and Machine Learning AI/ML have demonstrated enormous potential in improving and optimizing condition-based maintenance processes. In this paper, we present novel research that leverages the power of probabilistic programming and hybrid-AI that combines domain knowledge with data to create an effective analytic capability that monitors (in real-time) the health and status of a Robotic Combat Vehicle, next generation prototype ground vehicle for the US Army.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"52 3-4","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530932","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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