Pub Date : 2024-01-22DOI: 10.1109/RAMS51492.2024.10457592
Yavuz Goktas, Yunwei Hu, D. Yellamati
In this paper, we introduce a systematic approach for developing Process Failure Mode Effects Analysis (PFMEA) by incorporating p-diagrams. P-diagrams have proven to be successful in the development of Design Failure Mode Effects Analysis (DFMEA). PFMEA is a crucial tool for minimizing process risks, and we believe that leveraging p-diagrams can greatly enhance its effectiveness. There is a noticeable gap in the literature regarding the application of p-diagrams in PFMEA development. To address this gap, our proposed approach aims to provide a comprehensive guide for developing p-diagram driven robust PFMEA development. By utilizing p-diagrams, we aim to improve the accuracy and robustness of the PFMEA process. We start by analyzing the process through the creation of a process flow diagram. This diagram provides a visual representation of the interconnected steps involved in the process. Following that, we develop p-diagrams for each focus item (any potentially critical step or cell in the manufacturing process) we have identified as a potential risk using Change Point Analysis or any other risk assessment methodology. These p-diagrams help us gain a better understanding of the relationships between manufacturing parameters, manufacturing failure modes, noise factors or potential causes(6M), and required functions of the focus area. This understanding is essential for a structured transition into Process Failure Mode and Effects Analysis (PFMEA). To further enhance our analysis, we leverage the 6M (Ishikawa) approach as a proactive input into p-diagram as potential causes that drive any potential manufacturing failure modes. This categorizes potential sources of noise factors into six main categories (Man, Machine, Materials, Methods, Measurement, Mother Nature), allowing us to identify and address various noise factors that may impact the process requirements. Subsequently, the PFMEA team proceeds to complete the remaining columns, such as failure consequences, and evaluates the Risk Priority Numbers (RPNs). This systematic process thoroughly examines all functions and potential noise factors that could lead to deviations from ideal functionality. By doing so, it enhances efficiency and reduces the likelihood of overlooking important causes of failures.
在本文中，我们介绍了一种结合 P 型图开发过程失效模式影响分析 (PFMEA) 的系统方法。事实证明，P 型图在开发设计失效模式影响分析 (DFMEA) 过程中非常成功。PFMEA 是最大限度降低流程风险的重要工具，我们相信利用 P 型图可以大大提高其有效性。关于在 PFMEA 开发中应用 p 型图的文献存在明显的空白。为了弥补这一空白，我们提出的方法旨在为开发 p 型图驱动的稳健 PFMEA 开发提供全面指导。通过利用 p 型图，我们旨在提高 PFMEA 流程的准确性和稳健性。我们首先通过创建流程图来分析流程。流程图直观地展示了流程中相互关联的步骤。然后，我们为每个重点项目（生产流程中任何潜在的关键步骤或单元）绘制 p 型图，这些重点项目是我们利用变化点分析法或任何其他风险评估方法确定的潜在风险。这些 p 型图有助于我们更好地理解制造参数、制造故障模式、噪声因素或潜在原因 (6M) 以及重点领域所需功能之间的关系。这种理解对于有条不紊地过渡到过程失效模式和影响分析 (PFMEA) 至关重要。为了进一步加强分析，我们利用 6M（石川）方法，将其作为驱动任何潜在制造故障模式的潜在原因，主动输入到 P 图中。这将潜在的噪声源因素分为六大类（人、机器、材料、方法、测量、自然），使我们能够识别并解决可能影响工艺要求的各种噪声因素。随后，PFMEA 团队继续完成其余栏目，如故障后果，并评估风险优先级编号 (RPN)。这一系统化流程彻底检查了所有功能和可能导致偏离理想功能的潜在噪声因素。这样做不仅能提高效率，还能降低忽略重要故障原因的可能性。
{"title":"P-Diagram Driven Robust PFMEA Development","authors":"Yavuz Goktas, Yunwei Hu, D. Yellamati","doi":"10.1109/RAMS51492.2024.10457592","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457592","url":null,"abstract":"In this paper, we introduce a systematic approach for developing Process Failure Mode Effects Analysis (PFMEA) by incorporating p-diagrams. P-diagrams have proven to be successful in the development of Design Failure Mode Effects Analysis (DFMEA). PFMEA is a crucial tool for minimizing process risks, and we believe that leveraging p-diagrams can greatly enhance its effectiveness. There is a noticeable gap in the literature regarding the application of p-diagrams in PFMEA development. To address this gap, our proposed approach aims to provide a comprehensive guide for developing p-diagram driven robust PFMEA development. By utilizing p-diagrams, we aim to improve the accuracy and robustness of the PFMEA process. We start by analyzing the process through the creation of a process flow diagram. This diagram provides a visual representation of the interconnected steps involved in the process. Following that, we develop p-diagrams for each focus item (any potentially critical step or cell in the manufacturing process) we have identified as a potential risk using Change Point Analysis or any other risk assessment methodology. These p-diagrams help us gain a better understanding of the relationships between manufacturing parameters, manufacturing failure modes, noise factors or potential causes(6M), and required functions of the focus area. This understanding is essential for a structured transition into Process Failure Mode and Effects Analysis (PFMEA). To further enhance our analysis, we leverage the 6M (Ishikawa) approach as a proactive input into p-diagram as potential causes that drive any potential manufacturing failure modes. This categorizes potential sources of noise factors into six main categories (Man, Machine, Materials, Methods, Measurement, Mother Nature), allowing us to identify and address various noise factors that may impact the process requirements. Subsequently, the PFMEA team proceeds to complete the remaining columns, such as failure consequences, and evaluates the Risk Priority Numbers (RPNs). This systematic process thoroughly examines all functions and potential noise factors that could lead to deviations from ideal functionality. By doing so, it enhances efficiency and reduces the likelihood of overlooking important causes of failures.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"103 2","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":"140531034","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/RAMS51492.2024.10457800
Lance R. Curtis, B. Ayyub
Since its introduction in 1945, Miner's Rule has enjoyed a ubiquitous role in providing remaining useful life (RUL) estimates despite its inaccuracy under various conditions. This paper examines the use of Miner's Rule, especially over the past 25 years, in order to explore potential pathways for improving its use. Special attention is given to approaches researchers have taken towards the damage limit value, model accuracy, and uncertainty characterization. This meta-analysis revealed that an alternative model which simply provides more accuracy than Miner's Rule will not retire Miner's Rule. Wide adoption of an alternative model requires balancing increased accuracy with model simplicity. The authors propose that such balance will best be achieved with a model that possesses nonlinear and probabilistic elements. They further hypothesize a probabilistic version of the Marco-Starkey model as a potential candidate. Recommendations for future work include efforts towards a common definition of failure, probabilistic characterizations of the damage limit, an extensive comparison of alternative models to identify candidate models offering optimal balance, and an increased partnership among academia, industry, and government in verification efforts of alternative models.
{"title":"A Meta-Analysis of Miner's Rule","authors":"Lance R. Curtis, B. Ayyub","doi":"10.1109/RAMS51492.2024.10457800","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457800","url":null,"abstract":"Since its introduction in 1945, Miner's Rule has enjoyed a ubiquitous role in providing remaining useful life (RUL) estimates despite its inaccuracy under various conditions. This paper examines the use of Miner's Rule, especially over the past 25 years, in order to explore potential pathways for improving its use. Special attention is given to approaches researchers have taken towards the damage limit value, model accuracy, and uncertainty characterization. This meta-analysis revealed that an alternative model which simply provides more accuracy than Miner's Rule will not retire Miner's Rule. Wide adoption of an alternative model requires balancing increased accuracy with model simplicity. The authors propose that such balance will best be achieved with a model that possesses nonlinear and probabilistic elements. They further hypothesize a probabilistic version of the Marco-Starkey model as a potential candidate. Recommendations for future work include efforts towards a common definition of failure, probabilistic characterizations of the damage limit, an extensive comparison of alternative models to identify candidate models offering optimal balance, and an increased partnership among academia, industry, and government in verification efforts of alternative models.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"334 2","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":"140531186","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/RAMS51492.2024.10457793
Alfian Tan, Joy Egede, R. Remenyte-Prescott, Michel Valstar, Don Sharkey
This research is conducted to develop an automated action recognition method to evaluate the performance of the Newborn Life Support (NLS) procedure. It will be useful to find deviations in the procedure, such as missing steps and incorrect actions, which will reflect the reliability of the performing protocol. This method is also part of the work towards its integration with the NLS reliability model. A combination of image segmentation and action classification methods is used. The U-net Deep Learning model is trained to do segmentation on 18 objects. Every 150 consecutive segmented video frames are then grouped for action analysis. Four types of handcrafted features are extracted from every grouped image. A training strategy using traditional Machine Learning models is developed to deal with an imbalanced dataset, as well as to reduce the complexity of the system. The predicted action segment is visually examined to make sure of its practicality. Results show that the NLS first step of wet towel removal was correctly recognized in 23 of 23 videos (52.2%), indicating the potential usefulness of the model in determining if this critical action is performed correctly and at the right time.
{"title":"An Automated Performance Evaluation of the Newborn Life Support Procedure","authors":"Alfian Tan, Joy Egede, R. Remenyte-Prescott, Michel Valstar, Don Sharkey","doi":"10.1109/RAMS51492.2024.10457793","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457793","url":null,"abstract":"This research is conducted to develop an automated action recognition method to evaluate the performance of the Newborn Life Support (NLS) procedure. It will be useful to find deviations in the procedure, such as missing steps and incorrect actions, which will reflect the reliability of the performing protocol. This method is also part of the work towards its integration with the NLS reliability model. A combination of image segmentation and action classification methods is used. The U-net Deep Learning model is trained to do segmentation on 18 objects. Every 150 consecutive segmented video frames are then grouped for action analysis. Four types of handcrafted features are extracted from every grouped image. A training strategy using traditional Machine Learning models is developed to deal with an imbalanced dataset, as well as to reduce the complexity of the system. The predicted action segment is visually examined to make sure of its practicality. Results show that the NLS first step of wet towel removal was correctly recognized in 23 of 23 videos (52.2%), indicating the potential usefulness of the model in determining if this critical action is performed correctly and at the right time.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"12 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":"140530658","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/RAMS51492.2024.10457597
Sara Kohtz, Pingfeng Wang
Efficient health monitoring for high power energy systems has become an imperative research area in the field of reliability engineering. Novel systems, such as permanent magnet synchronous motors (PMSM), have become prominent in many impactful applications. These include but are not limited to propulsion aircraft, electric vehicles, ultra-high-speed elevators, and industrial manufacturing. Therefore, determining an optimal fault detection framework is a significant task. However, due to the newness of this system, there is little to no experimental data to analyze, so finite element simulation data is a necessity for determining the monitoring system. In this study, a design optimization approach is implemented for sensor placement and fault detection on a PMSM with hall effect sensors. This system is prone to short-winding faults, which can lead to catastrophic failures. The proposed method simultaneously determines the optimal placement of sensors while training an optimal classifier. The sensor placement is identified with a genetic algorithm, which uses the classifier's accuracy as the fitness function. In this case, the classifier structure is “stacked,” which means it combines multiple classification models and makes a final output with a meta-learner. This advanced classifier enables not only fault detection, but the severity of said fault, which is a significant improvement over present methodologies. Overall, this proposed structure converges to a design that has high accuracy for detection of faults, as well as the severity level.
{"title":"Sensor Placement and Fault Detection in Electric Motor using Stacked Classifier and Search Algorithm","authors":"Sara Kohtz, Pingfeng Wang","doi":"10.1109/RAMS51492.2024.10457597","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457597","url":null,"abstract":"Efficient health monitoring for high power energy systems has become an imperative research area in the field of reliability engineering. Novel systems, such as permanent magnet synchronous motors (PMSM), have become prominent in many impactful applications. These include but are not limited to propulsion aircraft, electric vehicles, ultra-high-speed elevators, and industrial manufacturing. Therefore, determining an optimal fault detection framework is a significant task. However, due to the newness of this system, there is little to no experimental data to analyze, so finite element simulation data is a necessity for determining the monitoring system. In this study, a design optimization approach is implemented for sensor placement and fault detection on a PMSM with hall effect sensors. This system is prone to short-winding faults, which can lead to catastrophic failures. The proposed method simultaneously determines the optimal placement of sensors while training an optimal classifier. The sensor placement is identified with a genetic algorithm, which uses the classifier's accuracy as the fitness function. In this case, the classifier structure is “stacked,” which means it combines multiple classification models and makes a final output with a meta-learner. This advanced classifier enables not only fault detection, but the severity of said fault, which is a significant improvement over present methodologies. Overall, this proposed structure converges to a design that has high accuracy for detection of faults, as well as the severity level.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"275 10","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":"140531007","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/RAMS51492.2024.10457715
A. Benz, M. Dazer
In the last decade much progress was made using the probability of test success $(P_{ts})$ metric in order to find an optimal test strategy for single stage loads considering Success Run and End of Life tests, see [1]–[3]. In this paper a study is conducted comparing different strategies in terms of $P_{ts}$ values using the Pearl String and Horizon methods for reliability test planning while applying different operating load spectra. The results reveal the Horizon method generally achieves higher $P_{ts}$ values, indicating a superior performance. However, the importance of considering the cost-effectiveness of achieving higher $P_{ts}$ leads to further information to optimize test planning. Thus, the study suggests that the optimal approach to reliability test planning is dependent on the operating load spectra, the test strategy, number of specimen and the associated values for $P_{ts}$ and cost.
{"title":"A Comparison of Pearl String and Horizon Method in Terms of Reliability Demonstration Testing","authors":"A. Benz, M. Dazer","doi":"10.1109/RAMS51492.2024.10457715","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457715","url":null,"abstract":"In the last decade much progress was made using the probability of test success $(P_{ts})$ metric in order to find an optimal test strategy for single stage loads considering Success Run and End of Life tests, see [1]–[3]. In this paper a study is conducted comparing different strategies in terms of $P_{ts}$ values using the Pearl String and Horizon methods for reliability test planning while applying different operating load spectra. The results reveal the Horizon method generally achieves higher $P_{ts}$ values, indicating a superior performance. However, the importance of considering the cost-effectiveness of achieving higher $P_{ts}$ leads to further information to optimize test planning. Thus, the study suggests that the optimal approach to reliability test planning is dependent on the operating load spectra, the test strategy, number of specimen and the associated values for $P_{ts}$ and cost.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"226 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":"140531160","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/RAMS51492.2024.10457651
Y. Bot, A. Segal
This article presents new trends in testing electronic products during design using simulation, programmable rules, circuit analysis, and artificial intelligence (AI). The paper highlights how these innovative techniques can improve the accuracy and efficiency of testing electronic products, reducing development time and costs while ensuring product reliability and safety. The article begins by discussing the limitations of traditional physical testing methods, including the high costs and long lead times required to develop and test prototypes. The paper then explains how simulation testing can overcome these limitations, allowing for more accurate testing under different scenarios and conditions, without the need for physical prototypes. The article provides an overview of the different types of simulations that can be used in product development. The paper also discusses the use of programmable rules in simulation testing, which allows for the creation of specific testing protocols and rules to improve the accuracy of testing. Furthermore, the article highlights the role of AI in simulation testing, which can be used to optimize simulation models, identify design flaws, and reduce the risk of errors and failures. The paper also discusses the challenges involved in using AI in simulation testing and provides practical recommendations to mitigate them. The paper concludes by emphasizing the importance of integrating simulation testing into the overall product development process, as well as the need for collaboration and communication between stakeholders, including developers, testers, and customers, to ensure that testing requirements are clearly defined and agreed upon. In conclusion, this article presents new trends in testing electronic products during design using simulation, programmable rules, circuit analysis, and AI. The paper highlights the benefits of these innovative techniques, including reduced costs, improved accuracy of testing, and the ability to simulate extreme conditions, thereby ensuring the reliability and safety of electronic products.
{"title":"New Trends in Testing Electronic Products by Simulation During Design Using Programmable Rules, Circuit Analysis and AI","authors":"Y. Bot, A. Segal","doi":"10.1109/RAMS51492.2024.10457651","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457651","url":null,"abstract":"This article presents new trends in testing electronic products during design using simulation, programmable rules, circuit analysis, and artificial intelligence (AI). The paper highlights how these innovative techniques can improve the accuracy and efficiency of testing electronic products, reducing development time and costs while ensuring product reliability and safety. The article begins by discussing the limitations of traditional physical testing methods, including the high costs and long lead times required to develop and test prototypes. The paper then explains how simulation testing can overcome these limitations, allowing for more accurate testing under different scenarios and conditions, without the need for physical prototypes. The article provides an overview of the different types of simulations that can be used in product development. The paper also discusses the use of programmable rules in simulation testing, which allows for the creation of specific testing protocols and rules to improve the accuracy of testing. Furthermore, the article highlights the role of AI in simulation testing, which can be used to optimize simulation models, identify design flaws, and reduce the risk of errors and failures. The paper also discusses the challenges involved in using AI in simulation testing and provides practical recommendations to mitigate them. The paper concludes by emphasizing the importance of integrating simulation testing into the overall product development process, as well as the need for collaboration and communication between stakeholders, including developers, testers, and customers, to ensure that testing requirements are clearly defined and agreed upon. In conclusion, this article presents new trends in testing electronic products during design using simulation, programmable rules, circuit analysis, and AI. The paper highlights the benefits of these innovative techniques, including reduced costs, improved accuracy of testing, and the ability to simulate extreme conditions, thereby ensuring the reliability and safety of electronic products.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"118 3","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":"140531177","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/RAMS51492.2024.10457683
William A. Maul, Yunnhon Lo, Edmond Wong
This paper presents the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. Because initial analyses indicated a dominance in the total risk by software and firmware failures, efforts were made to refine those risks which involved: • Establishing software function traces for each detection algorithm, • Utilizing the Logical Source Lines of Code (LSLOC) count, • Refinement of the software failure rate, and • Establishing fractional multipliers for common hardware and software failure modes across the applicable individual fault trees. These efforts and their impact on the overall analyses are also discussed. The analysis scope, general assumptions and guide rules, and key modeling concepts are discussed to establish the basis of the risk assessments conducted. Even with the implementation of the analysis refinements, software and firmware are still key risk contributors, but hardware failures, primarily in the form of Common Cause Failures (CCFs), are also indicated as risk drivers. The refinements enable risk estimations of individual detection functions as well as the entire FM suite. There still remains issues of how to account for time and redundancy in the software risk estimations that will continue to be the focus of future work.
本文介绍了目前正在对太空发射系统（SLS）Artemis II 故障管理（FM）检测功能进行的假阳性（FP）和假阴性（FN）风险评估过程。由于最初的分析表明软件和固件故障在总风险中占主导地位，因此努力对这些风险进行细化，其中包括- 为每种检测算法建立软件功能跟踪， - 利用逻辑源代码行数（LSLOC）， - 改进软件故障率，以及 - 在适用的单个故障树中为常见的硬件和软件故障模式建立分数乘数。还讨论了这些工作及其对总体分析的影响。对分析范围、一般假设和指导规则以及关键建模概念进行了讨论，以建立风险评估的基础。即使实施了分析改进，软件和固件仍然是造成风险的主要因素，但硬件故障（主要以常见故障（CCF）的形式出现）也被视为风险驱动因素。通过改进，可以对单个检测功能和整个调频套件进行风险评估。在软件风险评估中如何考虑时间和冗余问题仍然是今后工作的重点。
{"title":"Fault Management Algorithm Risk Assessment for the NASA Space Launch System","authors":"William A. Maul, Yunnhon Lo, Edmond Wong","doi":"10.1109/RAMS51492.2024.10457683","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457683","url":null,"abstract":"This paper presents the false positive (FP) and false negative (FN) risk assessment process currently being conducted for the Space Launch System (SLS) Artemis II Fault Management (FM) detection functions. Because initial analyses indicated a dominance in the total risk by software and firmware failures, efforts were made to refine those risks which involved: • Establishing software function traces for each detection algorithm, • Utilizing the Logical Source Lines of Code (LSLOC) count, • Refinement of the software failure rate, and • Establishing fractional multipliers for common hardware and software failure modes across the applicable individual fault trees. These efforts and their impact on the overall analyses are also discussed. The analysis scope, general assumptions and guide rules, and key modeling concepts are discussed to establish the basis of the risk assessments conducted. Even with the implementation of the analysis refinements, software and firmware are still key risk contributors, but hardware failures, primarily in the form of Common Cause Failures (CCFs), are also indicated as risk drivers. The refinements enable risk estimations of individual detection functions as well as the entire FM suite. There still remains issues of how to account for time and redundancy in the software risk estimations that will continue to be the focus of future work.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"261 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":"140531020","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/RAMS51492.2024.10457784
Jan B. Smith
A new data analysis methodology uses system level event dates to recognize unreliability of any system with complex causes-and-effects and forecast the time to the next event date as a probability distribution. A single datum is often sufficient, and this makes it uniquely valuable for systems requiring catastrophic events and failures to be minimal.
{"title":"Reliability - an Emergent System Property, 737 MAX and Societal System Examples","authors":"Jan B. Smith","doi":"10.1109/RAMS51492.2024.10457784","DOIUrl":"https://doi.org/10.1109/RAMS51492.2024.10457784","url":null,"abstract":"A new data analysis methodology uses system level event dates to recognize unreliability of any system with complex causes-and-effects and forecast the time to the next event date as a probability distribution. A single datum is often sufficient, and this makes it uniquely valuable for systems requiring catastrophic events and failures to be minimal.","PeriodicalId":518362,"journal":{"name":"2024 Annual Reliability and Maintainability Symposium (RAMS)","volume":"293 11","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530965","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/RAMS51492.2024.10457629
Pietro Fanelli, Dharmjeet Verma, Mathew Thomas
The paper hereby presented intends to share an overview of a series of analyses performed on batteries equipping multiple products.
本文旨在概述对配备多种产品的电池所做的一系列分析。
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