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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

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NASA NTRS1971

Saturn component failure rate and failure rate modifiers

Failure mode frequency ratios, environmental adjustment factors, and failure rates for mechanical and electromechanical component families are presented. The failure rates and failure rate modifiers resulted from a series of studies whose purpose was to provide design, tests, reliability, and systems engineers with accurate, up-to-date failure rate information. The results of the studies were achieved through an extensive engineering analysis of the Saturn Program test data and Unsatisfactory Condition Reports (UCR's) and the application of mathematical techniques developed for the studies.

Source record↗

NASA NTRS2004

Derivation of Failure Rates and Probability of Failures for the International Space Station Probabilistic Risk Assessment Study

National Aeronautics and Space Administration s (NASA) International Space Station (ISS) Program uses Probabilistic Risk Assessment (PRA) as part of its Continuous Risk Management Process. It is used as a decision and management support tool to not only quantify risk for specific conditions, but more importantly comparing different operational and management options to determine the lowest risk option and provide rationale for management decisions. This paper presents the derivation of the probability distributions used to quantify the failure rates and the probability of failures of the basic events employed in the PRA model of the ISS. The paper will show how a Bayesian approach was used with different sources of data including the actual ISS on orbit failures to enhance the confidence in results of the PRA. As time progresses and more meaningful data is gathered from on orbit failures, an increasingly accurate failure rate probability distribution for the basic events of the ISS PRA model can be obtained. The ISS PRA has been developed by mapping the ISS critical systems such as propulsion, thermal control, or power generation into event sequences diagrams and fault trees. The lowest level of indenture of the fault trees was the orbital replacement units (ORU). The ORU level was chosen consistently with the level of statistically meaningful data that could be obtained from the aerospace industry and from the experts in the field. For example, data was gathered for the solenoid valves present in the propulsion system of the ISS. However valves themselves are composed of parts and the individual failure of these parts was not accounted for in the PRA model. In other words the failure of a spring within a valve was considered a failure of the valve itself.

Vitali, Roberto↗

NASA NTRS1969

Spacecraft failure rates - Where are we?

OGO program failure rate analysis with respect to cost, schedule and performance tradeoffs, considering component and systems design and reliability, prelaunch feedback, etc

Bloomquist, C. E.↗

NASA NTRS1985

Completely monotone regression estimates of software failure rates

A method for estimating the present failure rate of a program is presented. A crude nonparameter estimate of the failure rate function is obtained from past failure times. This estimate is then smoothed by fitting a completely monotonic function, which is the solution of a quadratic programming problem. The value of the smoothed function at present time is used as the estimate of present failure rate. Results of a Monte Carlo study of performance are given.

Miller, D. R.↗

NASA NTRS1976

A study of Mariner 10 flight experiences and some flight piece part failure rate computations

The problems and failures encountered in Mariner flight are discussed and the data available through a quantitative accounting of all electronic piece parts on the spacecraft are summarized. It also shows computed failure rates for electronic piece parts. It is intended that these computed data be used in the continued updating of the failure rate base used for trade-off studies and predictions for future JPL space missions.

Paul, F. A.↗

NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

Current Environmental Control and Life Support System (ECLSS) development and test activities are not generating data fast enough to provide statistically-supportable precise Orbital Replacement Unit (ORU) failure rate estimates for future missions. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches – namely regular resupply and rapid abort capabilities – will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of operations on board the International Space Station (ISS), a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass for future missions. A Bayesian failure rate estimation approach, such as the one currently implemented by the ISS Program, can help reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior failure rate estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, these subjective (and often inaccurate) prior estimates currently have a strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.

Supportability↗

NASA NTRS2019

Small-Satellite Mission Failure Rates

The purpose of this report is to determine the failure rate of small-satellite missions launched between the years 2000 and 2016. This analysis considers the rates of both partial and total mission failure, as well as the failures attributable to failure of the launch vehicle. This study observed that between the years of 2000 to 2016, 41.3% of all small satellites launched failed or partially failed. Of these small satellite missions, 24.2% were total mission failures, another 11% were partial mission failures, and 6.1% were launch vehicle failures. The small satellite failure data reveals an increase in the failure rate as the yearly launch rate has increased. The period 2000 to 2008 averaged 15 launches per year, during which 28.6% of the small satellite missions failed or partially failed. The period from 2009 to 2016 averaged 48 launches per year, during which 42.6% of the small satellite missions failed or partially failed. The launch vehicle failure rate for both periods was the same at around 6.1%. The implication is that for modern small satellite missions, almost one out of every two small satellite missions will result in either a total or a partial mission failure. Counting the partial mission successes as “successful missions” reduces the failure rate, but only to 38.2% for the period 2009 to 2016.

Small Satellite↗

NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

The currently planned schedule for advanced Environmental Control and Life Support System (ECLSS) development and test activities to support human exploration missions is unlikely to generate sufficient data to enable statistically-supportable, precise Orbital Replacement Unit (ORU) failure rate estimates to meet existing crew safety expectations. Accurate and precise failure rate estimates are critical for missions beyond Low Earth Orbit (LEO) because current risk mitigation approaches –namely regular resupply and rapid abort capabilities –will not be available. Safe operations will depend on mission planners’ ability to accurately forecast spares demand and efficiently provide the necessary resources. However, even after more than a decade of International Space Station (ISS) ECLSS operations, a significant amount of uncertainty remains in failure rate estimates. Uncertain or inaccurate failure rates result in increased risk and spares mass. A Bayesian estimation approach, such as the one currently implemented by the ISS Program, can reduce uncertainty by incorporating engineering judgement into failure rate estimates. However, experience on the ISS and with other complex systems shows that these prior failure rate estimates are often inaccurate. In addition, prior estimates are typically point values; some level of uncertainty must be added to convert these into probability distributions for Bayesian updating, and there are several potential methods for doing so. Due to the low rate of data collection, any inaccuracy in theseprior estimates currently hasa strong influence on the end result. This paper examines the challenges associated with failure rate estimation, validation, and uncertainty reduction in the context of ECLSS development for beyond-LEO missions. A variety of techniques for generating and updating Bayesian priors are discussed and evaluated using both real-world and simulated data. Potential solutions for improving failure rate estimation, including testing additional units, are analyzed and discussed, and a set of recommendations are provided for next-generation system development activities.

Reliability↗

NASA NTRSDate not supplied

More Data Needed for Failure Rate Estimation, Validation, and Uncertainty Reduction

Reliability↗

NASA NTRS2020

A Method and Model to Predict Initial Failure Rates

It has long been well known that actual system reliability typically falls well short of early estimates. Failure rates are often ten or more times higher than anticipated. Many reasons have been given for this, but over-optimism is the fundamental cause of too-favorable reliability predictions. Most forecasts of reliability are essentially best-case scenarios, as are predictions of budget and schedule. Confident engineers assemble estimates bottom-up, including the known factors and ignoring problems that they hope won’t happen. Traditional reliability estimation is based on simply summing up the component failure rates. This ignores most actual failure causes. The way to reduce over-optimism is to use the historical system level failure rate from similar projects. Adjustments should not be made based purely on engineering judgment, but only if there is so logical quantitative justification. The traditional component-based reliability estimate is useful as a lower bound on the system failure rate. The difference between this lower bound component-based reliability and the historical system level reliability indicates how much of the total failure rate is due to system level problems rather than component failures.

Jones, Harry W.↗

NASA NTRS2020

A Method and Model to Predict Initial Failure Rates

It has long been well known that actual system reliability typically falls well short of early estimates. Failure rates are often ten or more times higher than anticipated. Many reasons have been given for this, but over-optimism is the fundamental cause of too-favorable reliability predictions. Most forecasts of reliability are essentially best-case scenarios, as are predictions of budget and schedule. Confident engineers assemble estimates bottom-up, including the known factors and ignoring problems that they hope won't happen. Traditional reliability estimation is based on simply summing up the component failure rates. This ignores most actual failure causes. The way to reduce over-optimism is to use the historical system level failure rate from similar projects. Adjustments should not be made based purely on engineering judgment, but only if there is so logical quantitative justification. The traditional component-based reliability estimate is useful as a lower bound on the system failure rate. The difference between this lower bound component-based reliability and the historical system level reliability indicates how much of the total failure rate is due to system level problems rather than component failures

Jones, Harry W.↗

NASA NTRS1980

Estimation procedures to measure and monitor failure rates of components during thermal-vacuum testing

Estimation procedures are described for measuring component failure rates, for comparing the failure rates of two different groups of components, and for formulating confidence intervals for testing hypotheses (based on failure rates) that the two groups perform similarly or differently. Appendix A contains an example of an analysis in which these methods are applied to investigate the characteristics of two groups of spacecraft components. The estimation procedures are adaptable to system level testing and to monitoring failure characteristics in orbit.

Williams, R. E.↗

NASA NTRS2014

Using Generic Data to Establish Dormancy Failure Rates

Many hardware items are dormant prior to being operated. The dormant period might be especially long, for example during missions to the moon or Mars. In missions with long dormant periods the risk incurred during dormancy can exceed the active risk contribution. Probabilistic Risk Assessments (PRAs) need to account for the dormant risk contribution as well as the active contribution. A typical method for calculating a dormant failure rate is to multiply the active failure rate by a constant, the dormancy factor. For example, some practitioners use a heuristic and divide the active failure rate by 30 to obtain an estimate of the dormant failure rate. To obtain a more empirical estimate of the dormancy factor, this paper uses the recently updated database NPRD-2011 [1] to arrive at a set of distributions for the dormancy factor. The resulting dormancy factor distributions are significantly different depending on whether the item is electrical, mechanical, or electro-mechanical. Additionally, this paper will show that using a heuristic constant fails to capture the uncertainty of the possible dormancy factors.

Reistle, Bruce↗

NASA NTRS1968

The impact of the flight specifications on semiconductor failure rates.

Flight specifications effects on failure rates of semiconductor parts in Apollo Guidance Computer, detailing procurement, screen, burn-in and field history

Hanley, L. D.↗

NASA NTRS1971

Field failure rate assessment methodology

Components field failure rates assessment methods for ground support equipment

Bean, E. E.↗

NASA NTRS2004

A Study to Compare the Failure Rates of Current Space Shuttle Ground Support Equipment with the New Pathfinder Equipment and Investigate the Effect that the Proposed GSE Infrastructure Upgrade Might Have to Reduce GSE Infrastructure Failures

The purposes of this study are to compare the current Space Shuttle Ground Support Equipment (GSE) infrastructure with the proposed GSE infrastructure upgrade modification. The methodology will include analyzing the first prototype installation equipment at Launch PAD B called the "Pathfinder". This study will begin by comparing the failure rate of the current components associated with the "Hardware interface module (HIM)" at the Kennedy Space Center to the failure rate of the neW Pathfinder components. Quantitative data will be gathered specifically on HIM components and the PAD B Hypergolic Fuel facility and Hypergolic Oxidizer facility areas which has the upgraded pathfinder equipment installed. The proposed upgrades include utilizing industrial controlled modules, software, and a fiber optic network. The results of this study provide evidence that there is a significant difference in the failure rates of the two studied infrastructure equipment components. There is also evidence that the support staff for each infrastructure system is not equal. A recommendation to continue with future upgrades is based on a significant reduction of failures in the new' installed ground system components.

Kennedy, Barbara J.↗

NASA NTRS1967

Cut-through tester accurately measures insulation failure rates

Cut-through tester electronically measures the rate of failure of various wire and cable insulating materials both as to time and the amount of applied pressure. The force /weight/ acting on the penetrator can be applied through a near infinite range.

Baker, E. U.↗

NASA NTRSDate not supplied

The System Complexity Metric (SCM) Predicts System Costs and Failure Rates

A complex system has many parts and interactions and so is difficult to understand. Systems with higher complexity generally have higher costs and failure rates. A System Complexity Metric (SCM) is defined to be the sum of the number of nodes, N, in the system block diagram plus the number of one-way interactions, I, between the nodes. SCM = N + I. SCMs are easily determined by direct inspection of high level block diagrams of life support systems. System cost was found to be directly proportional to SCM. The system MTBF (Mean Time Before Failure) is the inverse of the system failure rate. MTBF = 1/f. The system MTBF was found to be proportional to SCM^(-2.2) for estimated preflight MTBFs. As is typical for systems that are not extensively tested and redesigned to eliminate unexpected failure modes, the life support flight failure rates were about ten times higher than the preflight estimates and the MTBFs one-tenth the preflight estimates. The system MTBF was found to be proportional to SCM^(-2.6) for observed flight MTBFs.

System compleity↗