Transor analysis
Reliability and failure modes of adaptive restoring circuit module Transor
LOGIC CIRCUIT↗
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Results for “failure mode analysis”
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Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling
Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.
Validation↗
Discovery and Analysis of Rare High-Impact Failure Modes using Adversarial RL-Informed Sampling
Adaptive learning agents have tremendous potential to handle critical tasks currently performed by humans. Unfortunately, due to their complexity, it can be difficult to verify that these learning agents do not have critical failure modes. Standard verification and validation methods often do not apply directly to learning agents and Monte Carlo methods have difficulty covering even a small fraction of the state space, especially in multiagent systems or over long time horizons. To overcome this difficulty, we demonstrate an adaptive stress-testing method based on reinforcement learning of correlations that raise the probability of failure. This approach has three key properties: (1) it is able to find rare failure modes with far greater sample efficiency than Monte Carlo methods, (2) it can estimate the true probability of a failure mode despite the inherent bias in the learning method, and (3) it is capable of learning and resampling compact representations of multimodal failure spaces. These properties are important in practice as we need to find disparate failure modes while accounting for their actual relevance. This is a significant advantage over traditional adaptive stress testing methods that give abstract likelihoods of particular failure instances, but cannot estimate the probability of a broader failure mode. We test our algorithm on a simple problem from the aviation domain where an autonomous aircraft lands in gusty wind conditions. The results suggest that we can find failure modes with far fewer samples than the Monte Carlo approach and simultaneously estimate the probability of failure.
Validation↗
Independent Orbiter Assessment (IOA): Assessment of the reaction control system, volume 5
The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA effort first completed an analysis of the aft and forward Reaction Control System (RCS) hardware and Electrical Power Distribution and Control (EPD and C), generating draft failure modes and potential critical items. The IOA results were then compared to the proposed Post 51-L NASA FMEA/CIL baseline. This report documents the results of that comparison for the Orbiter RCS hardware and EPD and C systems. Volume 5 contains detailed analysis and superseded analysis worksheets and the NASA FMEA to IOA worksheet cross reference and recommendations.
Prust, Chet D.↗
Orbiter subsystem hardware/software interaction analysis. Volume 8: Forward reaction control system
The results of the orbiter hardware/software interaction analysis for the AFT reaction control system are presented. The interaction between hardware failure modes and software are examined in order to identify associated issues and risks. All orbiter subsystems and interfacing program elements which interact with the orbiter computer flight software are analyzed. The failure modes identified in the subsystem/element failure mode and effects analysis are discussed.
Becker, D. D.↗
Independent Orbiter Assessment (IOA): Assessment of the reaction control system, volume 2
The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA effort first completed an analysis of the aft and forward Reaction Control System (RCS) hardware and Electrical Power Distribution and Control (EPD and C), generating draft failure modes and potential critical items. The IOA results were then compared to the proposed Post 51-L NASA FMEA/CIL baseline. This report documents the results of that comparison for the Orbiter RCS hardware and EPD and C systems. Volume 2 continues the presentation of IOA worksheets.
Prust, Chet D.↗
Independent Orbiter Assessment (IOA): Assessment of the reaction control system, volume 3
The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA effort first completed an analysis of the aft and forward Reaction Control System (RCS) hardware and Electrical Power Distribution and Control (EPD and C), generating draft failure modes and potential critical items. The IOA results were then compared to the proposed Post 51-L NASA FMEA/CIL baseline. This report documents the results of that comparison for the Orbiter RCS hardware and EPD and C systems. Volume 3 continues the presentation of IOA worksheets.
Prust, Chet D.↗
Independent Orbiter Assessment (IOA): Assessment of the reaction control system, volume 4
The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA effort first completed an analysis of the aft and forward Reaction Control System (RCS) hardware and Electrical Power Distribution and Control (EPD and C), generating draft failure modes and potential critical items. The IOA results were then compared to the proposed Post 51-L NASA FMEA/CIL baseline. This report documents the results of that comparison for the Orbiter RCS hardware and EPD and C systems. Volume 4 continues the presentation of IOA worksheets and contains the potential critical items list.
Prust, Chet D.↗
High Energy Flywheel Containment Evaluation
A flywheel testing facility is being constructed at the NASA Glenn Research Center. This facility is to be used for life cycle testing of various flywheel rotors. The lifecycle testing consists of spinning a rotor from a low rpm (approx. 20,000 ) to a high rpm (approx. 60,000) and then back to the low rpm. This spin cycle will model that which the rotor will see during use. To simulate the lifetime of the rotor, the spin cycle will be performed tens of thousands of times. A typical life cycle spin test is expected to last six months. During this time the rotor will be spun through a cycle every five minutes. The test will run continuously for the six month period barring a flywheel failure. Since it is not reasonable to have the surrounding area evacuated of personnel for the duration of the testing, the flywheel facility has to be designed to withstand a flywheel rotor failure and insure that there is no danger to any personnel in the adjacent buildings or surrounding areas. In order to determine if the facility can safely contain a flywheel rotor failure an analysis of the facility in conjunction with possible flywheel failure modes was performed. This analysis is intended as a worst case evaluation of the burst liner and vacuum tank's ability to contain a failure. The test chamber consists of a cylindrical stainless steel vacuum tank, two outer steel containment rings, and a stainless steel burst liner. The stainless steel used is annealed 302, which has an ultimate strength of 620 MPa (90,000 psi). A diagram of the vacuum tank configuration is shown. The vacuum tank and air turbine will be located below ground in a pit. The tank is secured in the pit with 0.3 m (12 in.) of cement along the base and the remaining portion of the tank is surrounded by gravel up to the access ports. A 590 kg (1300 lb.) bulkhead is placed on top of the pit during operation and the complete facility is housed within a concrete structure which has 7.5 cm (3 in.) thick walls. A cutaway of the facility is shown.
Colozza, Anthony J.↗
A methodology for probabilistic prediction of structural failures of launch vehicle propulsion systems
A quantitative methodology for estimating the probability of occurrence of critical structural failure modes of launch vehicle propulsion systems during mission operation is presented. In this approach, engineering analysis models that characterize specific failure modes based on the physics and mechanics of the failure phenomenon are used in a prescribed probabalistic structure in which quantified uncertainty in the information used in the analysis and quantified uncertainty of the engineering analysis models themselves are both considered in estimating failure risk. The methodology is applicable to high cycle fatigue, low cycle fatigue, flaw propagation stress rupture, and other failure modes of launch vehicle propulsion systems and other space-flight systems. The probabalistic failure assessment presented provides the capability of performing quantitative analyses to rank options to improve reliability, thereby enabling limited financial resources of development and improvement programs to be more effectively allocated.
Moore, N.↗
S&MA Internship to Support Orion and the European Service Module
As a University Space Research Association (USRA) intern for NASA Johnson Space Center (JSC) during the summer 2016 work term, I worked on three main projects for the Space Exploration Division (NC) of the Safety and Mission Assurance (S&MA) Directorate. I worked on all three projects concurrently. One of the projects involved facilitating the status and closure of technical actions that were created during European Service Module (ESM) safety reviews by the MPCV Safety & Engineering Review Panel (MSERP). The two main duties included accurately collecting and summarizing qualitative data, and communicating that information to the European Space Agency (ESA) and Airbus (ESA's prime contractor) in a clear, succinct and precise manner. This project also required that I create a report on the challenges and opportunities of international S&MA. With its heavy emphasis on soft skills, this project taught me how to communicate better, by showing me how to present and share information in an easy-to-read and understandable format, and by showing me how to cooperate with and culturally respect international partners on a technical project. The second project involved working with the Orion Thermal Protection System (TPS) Process Failure Modes and Effects Analysis (PFMEA) Working Group to create the first full version of the Orion TPS PFMEA. The Orion TPS PFMEA Working Group met twice a week to analyze the Avcoat block installation process for failure modes, the failure modes effects, and how such failure modes could be controlled. I was in charge of implementing changes that were discussed in meeting, but were not implemented real time. Another major task included creating a significant portion of the content alongside another team member outside the two weekly meetings. This project caused me to become knowledgeable about TPS, heatshields, space-rated manufacturing, and non-destructive evaluation (NDE). The project also helped me to become better at working with a small team and helped improved my technical communication skills. My main duty for the third project was creating a Safety Verification Tracking Log (SVTL) for the Orbital Maneuvering System Engine (OMS-E), and contacting subject matter experts to close Hazard Report (HR) control verifications. This project also required me to support other OMS-E safety process tasks, like monitoring OMS-E vibration testing for Quality Assurance (QA) purposes. This project helped me become even more proficient in Excel. Throughout the project, I gained knowledge about the OMS-E system and improved my understanding of pressure systems and propellant systems. In terms of education goals, this work term has affirmed my desire to take a few more space-related courses, like orbital mechanics, so that I can have a better understanding of human spaceflight and the industry surrounding it. However, the work term did not persuade me to pursue a master's degree. In terms of career goals, this work term has helped me clarify the direction I would like to head in the future. The perspective of three summer terms working for NASA in S&MA has allowed me to observe that most S&MA employees joined S&MA after working in other NASA directorates, such as Engineering or Flight Operations. It is my belief that it would be advantageous for both NASA and I for me to broaden my knowledge base and technical skill set by completing hands-on technical work on human spaceflight projects, and for me to integrate my safety experience directly into technical work in other directorates. The other significant advantage to this proposed situation is that if I were to eventually return to S&MA, I would be returning with a substantial set of hands-on technical experience and knowledge, which would be a significant resource for S&MA tasks and projects.
Hutcheson, Connor↗
Comprehension and retrieval of failure cases in airborne observatories
This paper describes research dealing with the computational problem of analyzing and repairing failures of electronic and mechanical systems of telescopes in NASA's airborne observatories, such as KAO (Kuiper Airborne Observatory) and SOFIA (Stratospheric Observatory for Infrared Astronomy). The research has resulted in the development of an experimental system that acquires knowledge of failure analysis from input text, and answers questions regarding failure detection and correction. The system's design builds upon previous work on text comprehension and question answering, including: knowledge representation for conceptual analysis of failure descriptions, strategies for mapping natural language into conceptual representations, case-based reasoning strategies for memory organization and indexing, and strategies for memory search and retrieval. These techniques have been combined into a model that accounts for: (a) how to build a knowledge base of system failures and repair procedures from descriptions that appear in telescope-operators' logbooks and FMEA (failure modes and effects analysis) manuals; and (b) how to use that knowledge base to search and retrieve answers to questions about causes and effects of failures, as well as diagnosis and repair procedures. This model has been implemented in FANSYS (Failure ANalysis SYStem), a prototype text comprehension and question answering program for failure analysis.
Alvarado, Sergio J.↗
Achieving Improved Reliability with Failure Analysis
Reliability is the ability of a product to properly function, within specified performance limits, for a specified period of time, under the life cycle application conditions. Failure analysis is a vital tool in the effort to ensure reliability of electronic products and systems throughout their product lifecycle. Today, organizations involved in activities within the electronics supply chain are facing new challenges, not just from complex assembly styles, harsher lifecycle environments, and sophisticated supply chains, but also from customers who are demanding a quicker turn-around. Unfortunately, root cause failure analysis is often performed incompletely, leading to a poor understanding of failure mechanisms and causes and, customer dissatisfaction due to recurring failures. The PDC (Professional Development Course) starts with an introduction to reliability concepts, physics of failure and an overview of failure mechanisms that affect PCBs (Printed Circuit Boards), PCBAs (Printed Circuit Board Assembly) and components. The PDC then dives into root cause hypothesizing techniques (Pareto, FMEA (Failure Modes and Effects Analysis), fishbone (Cause-And-Effect Diagram), FTA (Fault Tree Analysis)), non-destructive and destructive analysis and, materials characterization will be discussed. Numerous failure analysis case studies will be used to illustrate the techniques and analysis principles to arrive at the root cause(s) of field failures on printed circuit boards, active components, and assemblies. What Attendees will Learn: Topics include: Overview of Reliability Concepts Failure mechanisms of electronic products Root cause analysis Failure analysis techniques -Non-destructive techniques (optical, CSAM (Confocal Scanning Electron Microscopy) etc.) -Destructive analysis (DPA (Destructive Physical Analysis), Decap (Decapsulation), FIB (Focused Ion Beam) etc.) -Materials characterization (XRF (X-Ray Fluorescence) , EDS (Error Detection Sequential), TMA/DSC (Thermal Mechanical Analysis/Differential Scanning Calorimetry) etc.)
PCB quality↗
Human Factors Process Task Analysis: Liquid Oxygen Pump Acceptance Test Procedure at the Advanced Technology Development Center
This viewgraph presentation provides information on Human Factors Process Failure Modes and Effects Analysis (HF PFMEA). HF PFMEA includes the following 10 steps: Describe mission; Define System; Identify human-machine; List human actions; Identify potential errors; Identify factors that effect error; Determine likelihood of error; Determine potential effects of errors; Evaluate risk; Generate solutions (manage error). The presentation also describes how this analysis was applied to a liquid oxygen pump acceptance test.
Diorio, Kimberly A.↗
Model 0A wind turbine generator FMEA
The results of Failure Modes and Effects Analysis (FMEA) conducted for the Wind Turbine Generators are presented. The FMEA was performed for the functional modes of each system, subsystem, or component. The single-point failures were eliminated for most of the systems. The blade system was the only exception. The qualitative probability of a blade separating was estimated at level D-remote. Many changes were made to the hardware as a result of this analysis. The most significant change was the addition of the safety system. Operational experience and need to improve machine availability have resulted in subsequent changes to the various systems which are also reflected in this FMEA.
Klein, William E.↗
From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems
The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.
Failure Analysis↗
From BERTopic to SysML: Informing Model-Based Failure Analysis With Natural Language Processing for Complex Aerospace Systems
The development of emerging complex aerospace systems will require new approaches for capturing safety incident scenarios as early as possible in the design phase. However, for novel systems, relevant data available is limited. In this work, we propose a framework informing model-based mission assurance activities with historical incident reports, lessons learned, or other relevant engineering documents using natural language processing. In doing so, we investigate whether there is useful information in data sets that are relevant, if not identical, to the system under design and whether, through rigorous systems engineering practice, this information can be effectively leveraged through model-based failure analysis. In a worked case study, we apply state-of-the-art topic modeling techniques to two data sets, a mission relevant data set and a system relevant data set. The sets of topics are merged and interpreted to form a preliminary list of failure topics that can be used to inform the identification of off-nominal modes in the model-based failure modes and effects analysis development. Once data from the system in operation is available, it can be used to update the topics identified. By extracting information about likely failures from relevant historical data sets and utilizing model-based mission assurance to ensure relevance and rigor, unanticipated failures can be reduced, and projects can more effectively learn from past missions.
Failure Analysis↗
What Went Wrong: A Survey of Wildfire UAS Mishaps through Named Entity Recognition
Increasingly, unmanned aircraft systems (UAS) are being applied to wildfire incidents for tasks such as mapping, aerial ignition, and delivery. As a result, incident reporting systems for wildfires are beginning to accumulate data related to UAS mishaps in wildfire response. In this research, we apply state-of-the-art natural language processing (NLP) techniques to develop a custom Named Entity Recognition (NER) model which extracts a Failure Modes and Effects Analysis (FMEA)-style survey of wildfire UAS mishaps reported in SAFECOM. The custom NER model is built by fine-tuning an existing (BERT) model, resulting in a generalizable NER model that can extract engineering relevant entities including failure modes, causes, effects, control processes, and recommendations from any failure-relevant text. Similar mishaps are clustered and reported as single rows within the FMEA. For each cluster, frequency, severity, and overall risk are computed. The methodology can be applied as part of a broader safety management system to track trends in mishaps and discover knowledge that can be utilized to improve safety outcomes and system performance.
Machine Learning↗