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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

An Abrupt Transition to an Intergranular Failure Mode in the Near-Threshold Fatigue Crack Growth Regime in Ni-Based Superalloys

Cyclic near-threshold fatigue crack growth (FCG) behavior of two disk superalloys was evaluated and was shown to exhibit an unexpected sudden failure mode transition from a mostly transgranular failure mode at higher stress intensity factor ranges to an almost completely intergranular failure mode in the threshold regime. The change in failure modes was associated with a crossover of FCG resistance curves in which the conditions that produced higher FCG rates in the Paris regime resulted in lower FCG rates and increased Kth values in the threshold region. High-resolution scanning and transmission electron microscopy were used to carefully characterize the crack tips at these near-threshold conditions. Formation of stable Al-oxide followed by Cr-oxide and Ti-oxides was found to occur at the crack tip prior to formation of unstable oxides. To contrast with the threshold failure mode regime, a quantitative assessment of the role that the intergranular failure mode has on cyclic FCG behavior in the Paris regime was also performed. It was demonstrated that even a very limited intergranular failure content dominates the FCG response under mixed mode failure conditions.

Telesman, J.↗
NASA NTRS2000

Failure Modes and Effects Analysis (FMEA): A Bibliography

Failure modes and effects analysis (FMEA) is a bottom-up analytical process that identifies process hazards, which helps managers understand vulnerabilities of systems, as well as assess and mitigate risk. It is one of several engineering tools and techniques available to program and project managers aimed at increasing the likelihood of safe and successful NASA programs and missions. This bibliography references 465 documents in the NASA STI Database that contain the major concepts, failure modes or failure analysis, in either the basic index of the major subject terms.

Source record↗
DOE OSTI2016

SU-F-T-247: Collision Risks in a Modern Radiation Oncology Department: An Efficient Approach to Failure Modes and Effects Analysis

Purpose: Collisions between treatment equipment and patients are potentially catastrophic. Modern technology now commonly involves automated remote motion during imaging and treatment, yet a systematic assessment to identify and mitigate collision risks has yet to be performed. Failure modes and effects analysis (FMEA) is a method of risk assessment that has been increasingly used in healthcare, yet can be resource intensive. This work presents an efficient approach to FMEA to identify collision risks and implement practical interventions within a modern radiation therapy department. Methods: Potential collisions (e.g. failure modes) were assessed for all treatment and simulation rooms by teams consisting of physicists, therapists, and radiation oncologists. Failure modes were grouped into classes according to similar characteristics. A single group meeting was held to identify implementable interventions for the highest priority classes of failure modes. Results: A total of 60 unique failure modes were identified by 6 different teams of physicists, therapists, and radiation oncologists. Failure modes were grouped into four main classes: specific patient setups, automated equipment motion, manual equipment motion, and actions in QA or service mode. Two of these classes, unusual patient setups and automated machine motion, were identified as being high priority in terms severity of consequence and addressability by interventions. The two highest risk classes consisted of 33 failure modes (55% of the total). In a single one hour group meeting, 6 interventions were identified. Those interventions addressed 100% of the high risk classes of failure modes (55% of all failure modes identified). Conclusion: A class-based approach to FMEA was developed to efficiently identify collision risks and implement interventions in a modern radiation oncology department. Failure modes and interventions will be listed, and a comparison of this approach against traditional FMEA methods will be presented.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2002

Decomposition-Based Failure Mode Identification Method for Risk-Free Design of Large Systems

When designing products, it is crucial to assure failure and risk-free operation in the intended operating environment. Failures are typically studied and eliminated as much as possible during the early stages of design. The few failures that go undetected result in unacceptable damage and losses in high-risk applications where public safety is of concern. Published NASA and NTSB accident reports point to a variety of components identified as sources of failures in the reported cases. In previous work, data from these reports were processed and placed in matrix form for all the system components and failure modes encountered, and then manipulated using matrix methods to determine similarities between the different components and failure modes. In this paper, these matrices are represented in the form of a linear combination of failures modes, mathematically formed using Principal Components Analysis (PCA) decomposition. The PCA decomposition results in a low-dimensionality representation of all failure modes and components of interest, represented in a transformed coordinate system. Such a representation opens the way for efficient pattern analysis and prediction of failure modes with highest potential risks on the final product, rather than making decisions based on the large space of component and failure mode data. The mathematics of the proposed method are explained first using a simple example problem. The method is then applied to component failure data gathered from helicopter, accident reports to demonstrate its potential.

Tumer, Irem Y.↗
DOE OSTI2015

Practical Implementation of Failure Mode and Effects Analysis for Safety and Efficiency in Stereotactic Radiosurgery

Purpose: To improve the safety and efficiency of a new stereotactic radiosurgery program with the application of failure mode and effects analysis (FMEA) performed by a multidisciplinary team of health care professionals. Methods and Materials: Representatives included physicists, therapists, dosimetrists, oncologists, and administrators. A detailed process tree was created from an initial high-level process tree to facilitate the identification of possible failure modes. Group members were asked to determine failure modes that they considered to be the highest risk before scoring failure modes. Risk priority numbers (RPNs) were determined by each group member individually and then averaged. Results: A total of 99 failure modes were identified. The 5 failure modes with an RPN above 150 were further analyzed to attempt to reduce these RPNs. Only 1 of the initial items that the group presumed to be high-risk (magnetic resonance imaging laterality reversed) was ranked in these top 5 items. New process controls were put in place to reduce the severity, occurrence, and detectability scores for all of the top 5 failure modes. Conclusions: FMEA is a valuable team activity that can assist in the creation or restructuring of a quality assurance program with the aim of improved safety, quality, and efficiency. Performing the FMEA helped group members to see how they fit into the bigger picture of the program, and it served to reduce biases and preconceived notions about which elements of the program were the riskiest.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2009

Lunar Module ECS (Environmental Control System) - Design Considerations and Failure Modes

Design considerations and failure modes for the Lunar Module (LM) Environmental Control System (ECS) are described. An overview of the the oxygen supply and cabin pressurization, atmosphere revitalization, water management and heat transport systems are provided. Design considerations including reliability, flight instrumentation, modularization and the change to the use of batteries instead of fuel cells are discussed. A summary is provided for the LM ECS general testing regime.

Interbartolo, Michael↗
DOE OSTI2016

WE-H-BRC-03: Failure Mode and Effects Analysis in the First Clinical Implementation of a Novel Stereotactic Breast Radiotherapy Device: GammaPod™

Purpose: GammaPod™, the first stereotactic radiotherapy device for early stage breast cancer treatment, has been recently installed and commissioned at our institution. A multidisciplinary working group applied the failure mode and effects analysis (FMEA) approach to perform a risk analysis. Methods: FMEA was applied to the GammaPod™ treatment process by: 1) generating process maps for each stage of treatment; 2) identifying potential failure modes and outlining their causes and effects; 3) scoring the potential failure modes using the risk priority number (RPN) system based on the product of severity, frequency of occurrence, and detectability (ranging 1–10). An RPN of higher than 150 was set as the threshold for minimal concern of risk. For these high-risk failure modes, potential quality assurance procedures and risk control techniques have been proposed. A new set of severity, occurrence, and detectability values were re-assessed in presence of the suggested mitigation strategies. Results: In the single-day image-and-treat workflow, 19, 22, and 27 sub-processes were identified for the stages of simulation, treatment planning, and delivery processes, respectively. During the simulation stage, 38 potential failure modes were found and scored, in terms of RPN, in the range of 9-392. 34 potential failure modes were analyzed in treatment planning with a score range of 16-200. For the treatment delivery stage, 47 potential failure modes were found with an RPN score range of 16-392. The most critical failure modes consisted of breast-cup pressure loss and incorrect target localization due to patient upper-body alignment inaccuracies. The final RPN score of these failure modes based on recommended actions were assessed to be below 150. Conclusion: FMEA risk analysis technique was applied to the treatment process of GammaPod™, a new stereotactic radiotherapy technology. Application of systematic risk analysis methods is projected to lead to improved quality of GammaPod™ treatments. Ying Niu and Cedric Yu are affiliated with Xcision Medical Systems.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2002

Solar Array Arcing Failure Mode and High Voltage Array Testing

In 1998, a new failure mode for space solar arrays was discovered. A flowchart for this failure mode is presented. Since the discovery of this arc failure mode, many tactics have been used to defeat it. The arc thresholds and arc mitigation strategies must be determined in vacuum-plasma tank testing on Earth. Results from these tests must then be extrapolated to the space plasma environment. Thus, the test conditions on Earth must be adequate to reproduce the important aspects of the phenomenon in space. At Glenn Research Center, we have been testing solar arrays for their arc thresholds and sustained arcing thresholds. In this paper, we detail the test conditions for a specific set of tests-those aimed at qualifying the Boeing Solar Tile solar arrays to operate in space at very high voltages (300 V or more).

Ferguson, Dale C.↗
NASA NTRS2002

Failure Mode Identification Through Clustering Analysis

Research has shown that nearly 80% of the costs and problems are created in product development and that cost and quality are essentially designed into products in the conceptual stage. Currently, failure identification procedures (such as FMEA (Failure Modes and Effects Analysis), FMECA (Failure Modes, Effects and Criticality Analysis) and FTA (Fault Tree Analysis)) and design of experiments are being used for quality control and for the detection of potential failure modes during the detail design stage or post-product launch. Though all of these methods have their own advantages, they do not give information as to what are the predominant failures that a designer should focus on while designing a product. This work uses a functional approach to identify failure modes, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component. In this paper, a statistical clustering procedure is proposed to retrieve information on the set of predominant failures that a function experiences. The various stages of the methodology are illustrated using a hypothetical design example.

Arunajadai, Srikesh G.↗
DOE OSTI2016

SU-G-TeP4-05: An Evaluation of a Low Dose Rate (LDR) Prostate Brachytherapy Procedure Using a Failure Modes and Effects Analysis (FMEA)

Purpose: The aim of the study is the application of a Failure Modes and Effects Analysis (FMEA) to access the risks for patients undergoing a Low Dose Rate (LDR) Prostate Brachytherapy Treatment. Methods: FMEA was applied to identify all the sub processes involved in the stages of identifying patient, source handling, treatment preparation, treatment delivery, and post treatment. These processes characterize the radiation treatment associated with LDR Prostate Brachytherapy. The potential failure modes together with their causes and effects were identified and ranked in order of their importance. Three indexes were assigned for each failure mode: the occurrence rating (O), the severity rating (S), and the detection rating (D). A ten-point scale was used to score each category, ten being the number indicating most severe, most frequent, and least detectable failure mode, respectively. The risk probability number (RPN) was calculated as a product of the three attributes: RPN = O X S x D. The analysis was carried out by a working group (WG) at UPMC. Results: The total of 56 failure modes were identified including 32 modes before the treatment, 13 modes during the treatment, and 11 modes after the treatment. In addition to the protocols already adopted in the clinical practice, the prioritized risk management will be implanted to the high risk procedures on the basis of RPN score. Conclusion: The effectiveness of the FMEA method was established. The FMEA methodology provides a structured and detailed assessment method for the risk analysis of the LDR Prostate Brachytherapy Procedure and can be applied to other radiation treatment modes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

StingRAY Failure Mode, Effects and Criticality Analysis: WEC Risk Registers

Analysis method to systematically identify all potential failure modes and their effects on the Stingray WEC system. This analysis is incorporated early in the development cycle such that the mitigation of the identified failure modes can be achieved cost effectively and efficiently. The FMECA can begin once there is enough detail to functions and failure modes of a given system, and its interfaces with other systems. The FMECA occurs coincidently with the design process and is an iterative process which allows for design changes to overcome deficiencies in the analysis.Risk Registers for major subsystems completed according to the methodology described in "Failure Mode Effects and Criticality Analysis Risk Reduction Program Plan.pdf" document below, in compliance with the DOE Risk Management Framework developed by NREL.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Failure mode and effects analysis based risk profile assessment for stereotactic radiosurgery programs at three cancer centers in Brazil

Purpose: The goal of this study was to evaluate the safety and quality management program for stereotactic radiosurgery (SRS) treatment processes at three radiotherapy centers in Brazil by using three industrial engineering tools (1) process mapping, (2) failure modes and effects analysis (FMEA), and (3) fault tree analysis. Methods: The recommendations of Task Group 100 of American Association of Physicists in Medicine were followed to apply the three tools described above to create a process tree for SRS procedure for each radiotherapy center and then FMEA was performed. Failure modes were identified for all process steps and values of risk priority number (RPN) were calculated from O, S, and D (RPN = O × S × D) values assigned by a professional team responsible for patient care. Results: The subprocess treatment planning was presented with the highest number of failure modes for all centers. The total number of failure modes were 135, 104, and 131 for centers I, II, and III, respectively. The highest RPN value for each center is as follows: center I (204), center II (372), and center III (370). Failure modes with RPN ≥ 100: center I (22), center II (115), and center III (110). Failure modes characterized by S ≥ 7, represented 68% of the failure modes for center III, 62% for center II, and 45% for center I. Failure modes with RPNs values ≥100 and S ≥ 7, D ≥ 5, and O ≥ 5 were considered as high priority in this study. Conclusions: The results of the present study show that the safety risk profiles for the same stereotactic radiotherapy process are different at three radiotherapy centers in Brazil. Although this is the same treatment process, this present study showed that the risk priority is different and it will lead to implementation of different safety interventions among the centers. Therefore, the current practice of applying universal device-centric QA is not adequate to address all possible failures in clinical processes at different radiotherapy centers. Integrated approaches to device-centric and process specific quality management program specific to each radiotherapy center are the key to a safe quality management program.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS1979

Maximum likelihood estimation for life distributions with competing failure modes

The general model for the competing failure modes assuming that location parameters for each mode are expressible as linear functions of the stress variables and the failure modes act independently is presented. The general form of the likelihood function and the likelihood equations are derived for the extreme value distributions, and solving these equations using nonlinear least squares techniques provides an estimate of the asymptotic covariance matrix of the estimators. Monte-Carlo results indicate that, under appropriate conditions, the location parameters are nearly unbiased, the scale parameter is slightly biased, and the asymptotic covariances are rapidly approached.

Sidik, S. M.↗
DOE OSTI2020

Continued Discussion of Failure Mode Modeling and Overall Component Reliability: Is the Data Missing or Censored?

This paper is a continuation of a paper presented at the 13th Probabilistic Safety Assessment and Management Conference, in which a discussion of modeling failure modes of complex components was presented; see Paulos and Smith (2016). The paper demonstrated how the typical method of treating failure modes as being exponential in nature may yield optimistic predictions when estimating how improvements to components will perform in the real world. Instead of relying on textbook methods, it is more accurate to model the failure modes as a race in time; unfortunately, this does not give a closed-form solution. This work uses a simulation to solve for the model of the world, and is compared to the standard methodology of treating the failure modes as exponential random failures. The standard method is shown to have optimistic predictions, which will lead to prediction errors when failure modes were removed or “fixed.” The failure mode methodology presented in the first paper treated the data as being censored when the test stopped. In this paper, we will compare the results from treating the data as both censored and missing, and comparisons will be shown showing which method is more accurate and under what conditions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

Continued Discussion of Failure Mode Modeling and Overall Component Reliability: Are the Data Missing or Censored?

This paper is the continuation of a paper presented at the 13th Probabilistic Safety Assessment and Management Conference, in which a methodology of modeling failure modes of complex components was presented; see Paulos and Smith (2016). This methodology is not particularly helpful in the space industry where there is a lack of failure data, but is more helpful in industries that see a lot of component repairs and improvements, such as in the aircraft or automotive industries. The previous paper demonstrated how the typical approach of treating failure modes as being exponential in nature may yield optimistic predictions when estimating how improvements to components will perform in the future. It is more accurate to model the failure modes as a race in time; unfortunately, this does not give a closed-form solution. This paper uses simulation to solve for the model of the world, and the results compared to the standard methodology of treating the failure modes as being exponential random failures. The standard method is shown to have optimistic predictions, which will lead to prediction errors when failure modes are removed or “fixed.” The failure mode methodology presented in the first paper treated the data as being censored when the test stopped. In this paper, we will compare the results from treating the data as both censored and missing.

Smith, Curtis↗
DOE OSTI2016

WE-H-BRC-02: Failure Mode and Effect Analysis of Liver Stereotactic Body Radiotherapy

Purpose: To identify areas of improvement in our liver stereotactic body radiation therapy (SBRT) program, using failure mode and effect analysis (FMEA). Methods: A multidisciplinary group consisting of one physician, three physicists, one dosimetrist and two therapists was formed. A process map covering 10 major stages of the liver SBRT program from the initial diagnosis to post treatment follow-up was generated. A total of 102 failure modes, together with their causes and effects, were identified. The occurrence (O), severity (S) and lack of detectability (D) were independently scored. The ranking was done using the risk probability number (RPN) defined as the product of average O, S and D numbers for each mode. The scores were normalized to remove inter-observer variability, while preserving individual ranking order. Further, a correlation analysis on the overall agreement on rank order of all failure modes resulted in positive values for successive pairs of evaluators. The failure modes with the highest RPN value were considered for further investigation. Results: The average normalized RPN values for all modes were 39 with a range of 9 to 103. The FMEA analysis resulted in the identification of the top 10 critical failures modes as: Incorrect CT-MR registration, MR scan not performed in treatment position, patient movement between CBCT acquisition and treatment, daily IGRT QA not verified, incorrect or incomplete ITV delineation, OAR contours not verified, inaccurate normal liver effective dose (Veff) calculation, failure of bolus tracking for 4D CT scan, setup instructions not followed for treatment and plan evaluation metrics missed. Conclusion: The application of FMEA to our liver SBRT program led to the identification and possible improvement of areas affecting patient safety.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Failure Modes and Mitigation Strategies for a Turboelectric Aircraft Concept with Turbine Electrified Energy Management

The electrification of gas turbine engines represents a major step-change in aircraft propulsion systems commonly known as Electrified Aircraft Propulsion (EAP). EAP involves the integration of electric machines potentially functioning as generators for large scale power extraction, as motors providing electrical augmentation of the engine spools, and even as a means of driving propulsors beyond the immediate scope of the engine. It may also include the use of energy storage. These and other characteristics of hybrid electric propulsion systems are relatively new to aircraft propulsion. The expanded propulsion architecture can be leveraged to improve propulsive efficiency and achieve better vehicle aerodynamics and controllability. It can also allow for additional benefits such as those enabled through Turbine Electrified Energy Management (TEEM). As the propulsion system and its functions expand, new failure modes are introduced. Due to the highly coupled nature of the propulsion system, failures could propagate throughout the system in ways that are unique to the new EAP architectures. To build confidence in the safety and practicality of EAP concepts, these failure modes need to be identified, explored, and addressed through mitigation strategies. This paper seeks to evaluate failure modes and failure mitigation strategies for the EAP concept known as the Single aisle Turboelectric AiRCaft with Aft Boundary Layer propulsor (STARC-ABL). The TEEM control concept is applied to improve transient operability. Several failure modes are considered and control based failure mitigation strategies are proposed with the goal of retaining operability and overall thrust. The results demonstrate the ability to maintain operability and a substantial amount of thrust in the event of various types of failures. Some challenges are also identified and discussed.

Turbine Electrified Energy Management↗
NASA NTRSDate not supplied

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.

reinforcement learning↗