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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Initial Criticality Assessments to Guide FMEAs on Rocket Engine Hot Fire Testing

Presented is an explanation of the use of the Initial Criticality Assessment (ICA) technique, a triage process for prioritizing required Failure Modes and Effects Analysis (FMEAs), for the European Service Module's main propulsion system's hot-fire test bed at White Sands New Mexico. Rather than instinctively performing many FMEAs of subsystems, or one large system level FMEA where every subcomponent is analyzed, the ICA guided an informed analysis of only the hardware that had a large impact to hazards. The low criticality hardware was documented via the ICA and no FMEA was performed; the work could then focus on the high criticality hardware. Thus a savings of Program resources was achieved. The experiences gained in creating these ICAs for this international collaborative project confirmed that the need for continuous communication across the technical teams is one of the greatest areas of emphasis. The European Space Agency (ESA) is developing the European Service Module (ESM), with its primary contractor, Airbus Defence and Space in Germany, for delivery to the National Aeronautics and Space Administration (NASA). The module will be equipped with a total of 21 engines to support NASA’s Orion spacecraft: one U.S. Space Shuttle Orbital Maneuvering System-Engine (OMS-E), eight auxiliary thrusters and 12 smaller RCS (Reaction Control System) thrusters. The main ESM propulsion system, used for large translational maneuvers, consists of one OMS-E... To qualify the design of the ESM propulsion subsystem (PSS) an all-steel Propulsion Qualification Module (PQM) structure is used to test the propulsion systems on Orion, including “hot firing” of the OMS engine, thrusters, and RCS. The PQM has been developed as a hot-fire test bed to be tested at the NASA White Sands Test Facility (WSTF). One of the objectives of the testing is to assure that the OMS-E can be safely operated with the PQM. Testing will also demonstrate that the PQM can set the proper upstream pressures and temperatures for the OMS-E to operate nominally given the PQM has never been tested in hot-fire operation with OMS-E before. In order to safely conduct the test campaign, hardware such as the engine subassembly, fluid feed lines, valves, electrical power lines, instrumentation, stiff links, installation Ground Support Equipment (GSE), and diffuser [whose objectives are to collect the exhaust of the OMS-E to actively cool down the exhaust gases, reduce thermal exchanges, and create a vacuum at OMS-E level before igniting], had to be analyzed for any hazards and failure modes.

Fault Tolerance↗
NASA NTRSDate not supplied

Systems Health Monitoring: Integrating FMEA into Bayesian Networks

The foreseeable high traffic density suggests that a large number of electric propulsion systems will enter the airspace, and that they will also operate at high frequency, e.g., large number of take offs and landings per unit time. The reliability of such critical systems is therefore key to ensure high safety standards in the low-altitude airspace. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures or lower-than-expected reliability by performing early fault detection by monitoring the systems. A key element of fault diagnosis is fault detection and isolation (FDI), which complexity increases with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and the number of sensors available. The proposed approach leverages combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework to aid FDI. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. The integrated framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. In this work, sub systems of Urban Air Mobility (UAM) type vehicle like avionics, structures, power-train etc. are taken into account to show the approach at the system level. This work integrates early design phase in the development of UAM type vehicles with diagnostic tools, which are often developed later in the product life-cycle, or retrofitted at a later time on systems. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).

UAM↗
NASA NTRS1996

Meteorological Satellites (METSAT) and Earth Observing System (EOS) Advanced Microwave Sounding Unit-A (AMSU-A) Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL)

This Failure Modes and Effects Analysis (FMEA) is for the Advanced Microwave Sounding Unit-A (AMSU-A) instruments that are being designed and manufactured for the Meteorological Satellites Project (METSAT) and the Earth Observing System (EOS) integrated programs. The FMEA analyzes the design of the METSAT and EOS instruments as they currently exist. This FMEA is intended to identify METSAT and EOS failure modes and their effect on spacecraft-instrument and instrument-component interfaces. The prime objective of this FMEA is to identify potential catastrophic and critical failures so that susceptibility to the failures and their effects can be eliminated from the METSAT/EOS instruments.

Source record↗
DOE OSTI2020

CalWave Open Water Demo - FMEA Update Budget Period 2

The Failure Modes and Effects Analysis (FMEA) is a qualitative reliability technique for systematically analyzing each possible failure mode within a hardware system, and identifying the resulting effect on that system, the mission, and the personnel. This submission includes an updated FMEA summary for CalWave's open water demonstration including pre- and post-mitigation results, hazard identification (HAZID) analysis, and component/function rooted FMEA.

16 TIDAL AND WAVE POWER↗
NASA NTRS1988

Failure mode and effects analysis (FMEA) for the Space Shuttle solid rocket motor

The recertification of the Space Shuttle Solid Rocket Booster (SRB) and Solid Rocket Motor (SRM) has included an extensive rewriting of the Failure Mode and Effects Analysis (FMEA) and Critical Items List (CIL). The evolution of the groundrules and methodology used in the analysis is discussed and compared to standard FMEA techniques. Especially highlighted are aspects of the FMEA/CIL which are unique to the analysis of an SRM. The criticality category definitions are presented and the rationale for assigning criticality is presented. The various data required by the CIL and contribution of this data to the retention rationale is also presented. As an example, the FMEA and CIL for the SRM nozzle assembly is discussed in detail. This highlights some of the difficulties associated with the analysis of a system with the unique mission requirements of the Space Shuttle.

Russell, D. L.↗
NASA NTRS1989

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.↗
NASA NTRS2006

NASA Taxonomies for Searching Problem Reports and FMEAs

Many types of hazard and risk analyses are used during the life cycle of complex systems, including Failure Modes and Effects Analysis (FMEA), Hazard Analysis, Fault Tree and Event Tree Analysis, Probabilistic Risk Assessment, Reliability Analysis and analysis of Problem Reporting and Corrective Action (PRACA) databases. The success of these methods depends on the availability of input data and the analysts knowledge. Standard nomenclature can increase the reusability of hazard, risk and problem data. When nomenclature in the source texts is not standard, taxonomies with mapping words (sets of rough synonyms) can be combined with semantic search to identify items and tag them with metadata based on a rich standard nomenclature. Semantic search uses word meanings in the context of parsed phrases to find matches. The NASA taxonomies provide the word meanings. Spacecraft taxonomies and ontologies (generalization hierarchies with attributes and relationships, based on terms meanings) are being developed for types of subsystems, functions, entities, hazards and failures. The ontologies are broad and general, covering hardware, software and human systems. Semantic search of Space Station texts was used to validate and extend the taxonomies. The taxonomies have also been used to extract system connectivity (interaction) models and functions from requirements text. Now the Reconciler semantic search tool and the taxonomies are being applied to improve search in the Space Shuttle PRACA database, to discover recurring patterns of failure. Usual methods of string search and keyword search fall short because the entries are terse and have numerous shortcuts (irregular abbreviations, nonstandard acronyms, cryptic codes) and modifier words cannot be used in sentence context to refine the search. The limited and fixed FMEA categories associated with the entries do not make the fine distinctions needed in the search. The approach assigns PRACA report titles to problem classes in the taxonomy. Each ontology class includes mapping words - near-synonyms naming different manifestations of that problem class. The mapping words for Problems, Entities and Functions are converted to a canonical form plus any of a small set of modifier words (e.g. non-uniformity NOT + UNIFORM.) The report titles are parsed as sentences if possible, or treated as a flat sequence of word tokens if parsing fails. When canonical forms in the title match mapping words, the PRACA entry is associated with the corresponding Problem, Entity or Function in the ontology. The user can search for types of failures associated with types of equipment, clustering by type of problem (e.g., all bearings found with problems of being uneven: rough, irregular, gritty ). The results could also be used for tagging PRACA report entries with rich metadata. This approach could also be applied to searching and tagging failure modes, failure effects and mitigations in FMEAs. In the pilot work, parsing 52K+ truncated titles (the test cases that were available), has resulted in identification of both a type of equipment and type of problem in about 75% of the cases. The results are displayed in a manner analogous to Google search results. The effort has also led to the enrichment of the taxonomy, adding some new categories and many new mapping words. Further work would make enhancements that have been identified for improving the clustering and further reducing the false alarm rate. (In searching for recurring problems, good clustering is more important than reducing false alarms). Searching complete PRACA reports should lead to immediate improvement.

Malin, Jane T.↗
NASA NTRS2013

Failure Modes and Effects Analysis (FMEA) Assistant Tool Feasibility Study

An effort to determine the feasibility of a software tool to assist in Failure Modes and Effects Analysis (FMEA) has been completed. This new and unique approach to FMEA uses model based systems engineering concepts to recommend failure modes, causes, and effects to the user after they have made several selections from pick lists about a component s functions and inputs/outputs. Recommendations are made based on a library using common failure modes identified over the course of several major human spaceflight programs. However, the tool could be adapted for use in a wide range of applications from NASA to the energy industry.

Flores, Melissa↗
DOE OSTI2026

Failure Mode and Effects Analysis (FMEA) for Photovoltaic Inverter

Photovoltaic (PV) inverters are critical yet vulnerable components in modern energy systems, often acting as reliability bottlenecks that increase the levelized cost of energy (LCOE). To address this, this paper presents a comprehensive Failure Mode and Effects Analysis (FMEA) tailored for PV inverters. Leveraging field data and literature, we identify failure-prone components, such as capacitors,, and relays, and prioritize their risks based on quantitative Risk Priority Numbers (RPNs). The analysis reveals that surge-induced MOV short circuits, capacitor degradation, and environmental cooling fan failures dominate the risk profile. These findings provide a targeted framework for reliability improvement, guiding future efforts in predictive diagnostics, design optimization, and accelerated life testing strategies.

14 SOLAR ENERGY↗
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 OSTI2021

Reliability Prediction using FMEA, FTA, and Related Techniques [Slides]

Summary: We presented a reliability analysis framework. We made point estimates of reliability using reliability block diagrams, fault trees, success trees and estimates with uncertainty using expert elicitation, Monte Carlo simulation, Bayesian analysis. Expert elicitation of failure modes and probabilities is labor-intensive, but critical. Bayesian analysis updates information from expert elicitation with data from reliability and aging tests (aging/compatibility data are needed to estimate lower-bound reliabilities at end of life). Estimation by more than one method helps insure consistency and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Feedstock-Conversion Interface Consortium: Failure Mode and Effects Analysis Summary Report (FY2022)

This report provides an overview of the development of failure modes and effects analysis (FMEA) and its implementation as a systematic criticality and risk assessment tool supporting a quality by design (QbD) approach for FCIC research. This report also provides a high-level overview of the results for the FMEA evaluation of two feedstock preprocessing system configurations: (1) generation of pine residue materials for high-temperature pyrolysis conversion and (2) generation of corn stover materials for low-temperature conversion using deacetylation and disc mechanical refining pretreatment for fermentation to hydrocarbons. For the results presented in this report, our FMEA interviews included two approaches. The first approach was to perform FMEA interviews for the entire system of unit operations giving a wholistic system level view. The second approach consisted of detailed interviews for each individual unit operation within the system allowing for a “deep dive” into the specific failures for the individual components within the configuration. These two approaches provide different resolutions of information. The FMEA results of this report were focused on failures associated with meeting critical quality attributes (CQAs) identified for the target conversion processes for each processed feedstock type. The information gathered through the FMEA interviews include estimations of risk scores for meeting each given CQA specification, identification of the impacts for not meeting a CQA specification, capturing causes associated with material attributes and process parameters for each failure, identification of current detection methods, and speculation of potential mitigation strategies for decreasing a failure’s risk score. The complete results of all FMEA interviews are provided in the Appendices of this report.

09 BIOMASS FUELS↗
DOE OSTI2023

Failure Mode and Effects Analysis Summary Report

This report provides an overview of the development of failure modes and effects analysis (FMEA) and its implementation as a systematic criticality and risk assessment tool supporting a quality by design (QbD) approach for FCIC research. This report also provides a high-level overview of the results for the FMEA evaluation of two feedstock preprocessing system configurations: (1) generation of pine residue materials for high-temperature pyrolysis conversion and (2) generation of corn stover materials for low-temperature conversion using deacetylation and disc mechanical refining pretreatment for fermentation to hydrocarbons. For the results presented in this report, our FMEA interviews included two approaches. The first approach was to perform FMEA interviews for the entire system of unit operations giving a wholistic system level view. The second approach consisted of detailed interviews for each individual unit operation within the system allowing for a “deep dive” into the specific failures for the individual components within the configuration. These two approaches provide different resolutions of information. The FMEA results of this report were focused on failures associated with meeting critical quality attributes (CQAs) identified for the target conversion processes for each processed feedstock type. The information gathered through the FMEA interviews include estimations of risk scores for meeting each given CQA specification, identification of the impacts for not meeting a CQA specification, capturing causes associated with material attributes and process parameters for each failure, identification of current detection methods, and speculation of potential mitigation strategies for decreasing a failure’s risk score. The complete results of all FMEA interviews are provided in the Appendices of this report.

conversion↗
DOE OSTI2022

Failure Mode and Effects Analysis for a Photovoltaic Inverter

While PV panel reliability continues to increase, PV inverters become the limiting factor for PV system reliability. Consequently, it is critical to have a generic tool from a third party for PV inverter reliability assessment to help 1) utilities/PV farm operators schedule maintenance in advance, and 2) inverter developers improve the next-generation design. However, these two things cannot be accomplished without first understanding the reasons behind inverter failure. Following this idea, as the first step, it is essential to identify and investigate the most failure-prone components within a PV inverter system. After all, any system is only as reliable as the components that are contained within it. This motivates the failure mode and effects analysis (FMEA) work presented for this workshop. The FMEA is conducted as follows: first, the overview of the methodology on the development of the FMEA is presented; then, based on a top-down approach starting from the PV inverter system, critical inverter components with high failure rates are identified and summarized; afterward, a thorough FMEA study at a component-level is performed and its results, including failure modes, failure mechanisms, and critical stressors, are tabulated; finally, according to three rankings (chance of occurrence, severity of occurrence, and ease of detection prior to failure) for each failure mechanism provided by the FMEA, risk priority numbers are calculated and the failure mechanisms along with the critical stressors are ranked in terms of their potentially detrimental effect on the PV inverter.

Brown, Buck↗
NASA NTRS1988

Failure modes and effects analysis automation

A failure modes and effects analysis (FMEA) assistant was implemented as a knowledge based system and will be used during design of the Space Station to aid engineers in performing the complex task of tracking failures throughout the entire design effort. The three major directions in which automation was pursued were the clerical components of the FMEA process, the knowledge acquisition aspects of FMEA, and the failure propagation/analysis portions of the FMEA task. The system is accessible to design, safety, and reliability engineers at single user workstations and, although not designed to replace conventional FMEA, it is expected to decrease by many man years the time required to perform the analysis.

Kamhieh, Cynthia H.↗
NASA NTRS1988

Independent Orbiter Assessment (IOA): Assessment of the life support and airlock support systems, volume 1

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 Life Support and Airlock Support Systems (LSS and ALSS) hardware, generating draft failure modes and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. The IOA results were then compared to the NASA FMEA/CIL baseline with proposed Post 51-L updates included. The discrepancies were flagged for potential future resolution. This report documents the results of that comparison for the Orbiter LSS and ALSS hardware. The IOA product for the LSS and ALSS analysis consisted of 511 failure mode worksheets that resulted in 140 potential critical items. Comparison was made to the NASA baseline which consisted of 456 FMEAs and 101 CIL items. The IOA analysis identified 39 failure modes, 6 of which were classified as CIL items, for components not covered by the NASA FMEAs. It was recommended that these failure modes be added to the NASA FMEA baseline. The overall assessment produced agreement on all but 301 FMEAs which caused differences in 111 CIL items.

Arbet, J. D.↗