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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Software models: A Bayesian approach to parameter estimation in the Jelenski-Moranda software reliability model

Maximum likelihood estimation procedures for the Jelinski-Moranda software reliability model often give misleading answers. A reparameterization and a Bayesian analysis eliminate some of the problems incurred by MLE methods and often give better predictions on sets of real and simulated data. Practical difficulties in estimating the initial number of errors N and the failure rate of each error phi by the method of maximum likelihood are: N, the MLE of N, is occasionally infinite (i.e., the routines for calculating N and phi do not converge). It is shown that N is finite sub i only if the regression line of the interevent times t sub i vs. i has positive slope. A serious problem is that often N approximates n, the sample size, and sometimes N = n. Thus the MLE predicts that the program is perfect even when it is far from being so. Only when almost all failures have been removed can N and phi be trusted near the end of debugging.

Littlewood, B.↗
NASA NTRS2012

Accelerated Aging Experiments for Capacitor Health Monitoring and Prognostics

This paper discusses experimental setups for health monitoring and prognostics of electrolytic capacitors under nominal operation and accelerated aging conditions. Electrolytic capacitors have higher failure rates than other components in electronic systems like power drives, power converters etc. Our current work focuses on developing first-principles-based degradation models for electrolytic capacitors under varying electrical and thermal stress conditions. Prognostics and health management for electronic systems aims to predict the onset of faults, study causes for system degradation, and accurately compute remaining useful life. Accelerated life test methods are often used in prognostics research as a way to model multiple causes and assess the effects of the degradation process through time. It also allows for the identification and study of different failure mechanisms and their relationships under different operating conditions. Experiments are designed for aging of the capacitors such that the degradation pattern induced by the aging can be monitored and analyzed. Experimental setups and data collection methods are presented to demonstrate this approach.

Prognostics of Electronics↗
NASA NTRS2017

Oxygen Storage Tanks Are Feasible for Mars Transit

The Mars transit tanks will probably be titanium lined, composite over wrapped pressure vessels (COPVs) similar to those used in the space shuttle and International Space Station(ISS). Since the mass of a storage tank is proportional to the mass of the gas it contains, the required oxygen will use about the same mass of tanks regardless of the number and size ofthe tanks. Using existing relatively small COPVs is possible. Pressure vessels can fail due torupture and leakage but no failures have occurred in space and the expected failure rates are very low. Since one or two spare tanks are required for reliability, using smaller tanks can reduce the total mass. For a Mars round trip, the mass of oxygen and tanks including spares is roughly equal to the mass of the ISS Oxygen Generation Assembly (OGA) and its spares. Since the OGA must orbit Mars and be returned to Earth, while half the storage tanks are emptied on the way to Mars and can be abandoned, storage tanks have a significant launch mass advantage over the OGA. Storage tanks are simpler, more reliable,and have fewer failure modes than an OGA. They would have smaller design and development costs and need less crew time and maintenance. Oxygen storage tanks are feasible for Mars transit and are attractive compared to the ISS OGA.

Jones, Harry W.↗
NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repairs Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, it will be necessary to understand how crew repair and maintenance timelines are impacted by mission operations and technology changes. Past work has been done using historical ISS data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available data sets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat, and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA International Space Station (ISS) maintenance and crew time data are logged into two central databases, the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS) respectively. Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. MDC provides a detailed catalog of failure events and an overview of the failure’s required maintenance and OPTimIS provides a description of crew activities and crew time durations dedicated to maintenance. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair times for each failure event. Data is also classified by the outcome of each repair event, whether the failed component was replaced or whether it was repaired in place. The entire maintenance activity dataset is then categorized based on the class of failed component to allow for a statistically significant sample size for each class and to provide accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time To Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗
NASA NTRSDate not supplied

Assessment of Crew Time for Maintenance and Repair Activities for Lunar Surface Missions

NASA is currently evaluating different methods to predict how much time crewmembers will spend conducting repair and maintenance activities on future space missions. As mission scope and spacecraft architectures change, understanding how crew repair and maintenance timelines are impacted by mission operations and technology changes is vital for future mission planning. Past work has been done using historical International Space Station (ISS) data to accurately predict crew habitation and operation timelines, resulting in the development of NASA’s Exploration Crew Time Model (ECTM). However, understanding crew maintenance and repair requirements has posed a unique challenge due to the complexity of available datasets, the probabilistic nature of sub-system failures, and the impacts of reliability growth on failure rates. This paper presents a methodology to collect and condition empirical repair and maintenance time data from available datasets, to extrapolate from that data to estimate projected maintenance and repair times for a lunar Surface Habitat (SH), and to assess how uncertainty in repair time could impact utilization time on the lunar surface. NASA ISS maintenance and crew time data are logged into two central databases: the Maintenance Data Collection (MDC) and the Operations Planning Timeline Integration System (OPTimIS). Separately, each of these two datasets capture only portions of the complete set of data required to generate an accurate assessment of crew time spent on maintenance activities at a sub-system level. To create a more useful crew time estimate for maintenance timelines, the authors developed a methodology to capture relevant data from each set and combine and utilize that data by linking crew time requirements to specific components. The authors compare the failure logs in the MDC to crew activity logs pulled from OPTimIS and then process the data to estimate required repair time for each failure and repair event. The entire maintenance activity dataset is then categorized based on the class of failed component to ensure a significant sample size for each class and accurate crew time estimates for any components lacking relevant data. This resultant component repair time data can be used in the future to generate Mean Time to Repair (MTTR) estimates and confidence intervals for each class of component based on a probabilistic distribution of documented maintenance events. These improved MTTR values can then be applied to candidate element sub-system architectures, along with component Mean Time Between Failure (MTBF) data to generate distributions for potential required system crew repair time estimates for a given mission. The authors applied these modeling methods to a case study of a crewed mission to the planned SH and produced expected corrective maintenance crew time distributions. The results produced an expected corrective maintenance crew time at over 24 hours per mission, and a maintenance crew time distribution that reflects the importance of planning for sufficient maintenance requirements each mission. Repair time distributions can then be used to develop more accurate crew schedules and to assess potential available utilization time.

Crew Time↗
NASA NTRS1992

Size of metallic and polyethylene debris particles in failed cemented total hip replacements

Reports of differing failure rates of total hip prostheses made of various metals prompted us to measure the size of metallic and polyethylene particulate debris around failed cemented arthroplasties. We used an isolation method, in which metallic debris was extracted from the tissues, and a non-isolation method of routine preparation for light and electron microscopy. Specimens were taken from 30 cases in which the femoral component was of titanium alloy (10), cobalt-chrome alloy (10), or stainless steel (10). The mean size of metallic particles with the isolation method was 0.8 to 1.0 microns by 1.5 to 1.8 microns. The non-isolation method gave a significantly smaller mean size of 0.3 to 0.4 microns by 0.6 to 0.7 microns. For each technique the particle sizes of the three metals were similar. The mean size of polyethylene particles was 2 to 4 microns by 8 to 13 microns. They were larger in tissue retrieved from failed titanium-alloy implants than from cobalt-chrome and stainless-steel implants. Our results suggest that factors other than the size of the metal particles, such as the constituents of the alloy, and the amount and speed of generation of debris, may be more important in the failure of hip replacements.

Non-NASA Center↗
NASA NTRS1967

Development of reliability prediction technique for semiconductor diodes

New fundamental technique of reliability prediction for semiconductor diodes based on realistic mathematical models can be applied to component failure rate prediction including mechanical degradation, electrical degradation, environmental stress factors, and electrical load stress factors.

Ryerson, C. M.↗
NASA NTRS1972

Failure in glass

Review of state of the art concerning glass failure mechanisms and fatigue theories discusses brittle fracture in glass, fatigue mechanisms, fatigue behavior, environmental effects on failure rate, and aging.

Keeton, S. C.↗
NASA NTRS1972

Methods of measurement for semiconductor materials, process control, and devices

Activities directed toward the development of methods of measurement for semiconductor materials, process control, and devices are described. Topics investigated include: measurements of transistor delay time; application of the infrared response technique to the study of radiation-damaged, lithium-drifted silicon detectors; and identification of a condition that minimizes wire flexure and reduces the failure rate of wire bonds in transistors and integrated circuits under slow thermal cycling conditions. Supplementary data concerning staff, standards committee activities, technical services, and publications are included as appendixes.

Bullis, W. M.↗
NASA NTRS1973

Low-thrust mission risk analysis, with application to a 1980 rendezvous with the comet Encke

A computerized failure process simulation procedure is used to evaluate the risk in a solar electric space mission. The procedure uses currently available thrust-subsystem reliability data and performs approximate simulations of the thrust sybsystem burn operation, the system failure processes, and the retargeting operations. The method is applied to assess the risks in carrying out a 1980 rendezvous mission to the comet Encke. Analysis of the results and evaluation of the effects of various risk factors on the mission show that system component failure rates are the limiting factors in attaining a high mission relability. It is also shown that a well-designed trajectory and system operation mode can be used effectively to partially compensate for unreliable thruster performance.

Yen, C. L.↗
NASA NTRS1973

Low-thrust mission risk analysis.

A computerized multi-stage failure process simulation procedure is used to evaluate the risk in a solar electric space mission. The procedure uses currently available thrust-subsystem reliability data and performs approximate simulations of the thrust subsystem burn operation, the system failure processes, and the retargetting operations. The application of the method is used to assess the risks in carrying out a 1980 rendezvous mission to Comet Encke. Analysis of the results and evaluation of the effects of various risk factors on the mission show that system component failure rates is the limiting factor in attaining a high mission reliability. But it is also shown that a well-designed trajectory and system operation mode can be used effectively to partially compensate for unreliable thruster performance.

Yen, C. L.↗
NASA NTRS1973

A reliability and comparative analysis of two standby system configurations.

Equations are derived which enable one to calculate the system reliability for parallel or triple modular redundant systems with standby spares. Software error detection is introduced into the TMR/Spares system configuration in order to utilize fully all of the units. An indication of the sensitivity of the system reliability to an increase in the number of spares, partitioning, switching, variations in the powered and unpowered failures rates, and time is presented. A comparison of the parallel and the TMR/Spares system configurations, under similar conditions, is given.

Taylor, D. S.↗