Results for “Failure Rate” — Page 7

NASA NTRS2015

Data Applicability of Heritage and New Hardware for Launch Vehicle System Reliability Models

Many launch vehicle systems are designed and developed using heritage and new hardware. In most cases, the heritage hardware undergoes modifications to fit new functional system requirements, impacting the failure rates and, ultimately, the reliability data. New hardware, which lacks historical data, is often compared to like systems when estimating failure rates. Some qualification of applicability for the data source to the current system should be made. Accurately characterizing the reliability data applicability and quality under these circumstances is crucial to developing model estimations that support confident decisions on design changes and trade studies. This presentation will demonstrate a data-source classification method that ranks reliability data according to applicability and quality criteria to a new launch vehicle. This method accounts for similarities/dissimilarities in source and applicability, as well as operating environments like vibrations, acoustic regime, and shock. This classification approach will be followed by uncertainty-importance routines to assess the need for additional data to reduce uncertainty.

Al Hassan Mohammad↗

NASA NTRS1982

Software reliability: Repetitive run experimentation and modeling

A software experiment conducted with repetitive run sampling is reported. Independently generated input data was used to verify that interfailure times are very nearly exponentially distributed and to obtain good estimates of the failure rates of individual errors and demonstrate how widely they vary. This fact invalidates many of the popular software reliability models now in use. The log failure rate of interfailure time was nearly linear as a function of the number of errors corrected. A new model of software reliability is proposed that incorporates these observations.

Nagel, P. M.↗

NASA NTRS1990

Single event induced transients in I/O devices - A characterization

The results of single-event upset (SEU) testing performed to evaluate the parametric transients, i.e., amplitude and duration, in several I/O devices, and the impact of these transients are discussed. The failure rate of these devices is dependent on the susceptibility of interconnected devices to the resulting transient change in the output of the I/O device. This failure rate, which is a function of the susceptibility of the interconnected device as well as the SEU response of the I/O device itself, may be significantly different from an upset rate calculated without taking these factors into account. The impact at the system level is discussed by way of an example.

Newberry, D. M.↗

NASA NTRS1973

Sensitivity of a critical tracking task to alcohol impairment

A first order critical tracking task is evaluated for its potential to discriminate between sober and intoxicated performances. Mean differences between predrink and postdrink performances as a function of BAC are analyzed. Quantification of the results shows that intoxicated failure rates of 50% for blood alcohol concentrations (BACs) at or above 0.1%, and 75% for BACs at or above 0.14%, can be attained with no sober failure rates. A high initial rate of learning is observed, perhaps due to the very nature of the task whereby the operator is always pushed to his limit, and the scores approach a stable asymptote after approximately 50 trials. Finally, the implementation of the task as an ignition interlock system in the automobile environment is discussed. It is pointed out that lower critical performance limits are anticipated for the mechanized automotive units because of the introduction of larger hardware and neuromuscular lags. Whether such degradation in performance would reduce the effectiveness of the device or not will be determined in a continuing program involving a broader based sample of the driving population and performance correlations with both BACs and driving proficiency.

Tennant, J. A.↗

NASA NTRSDate not supplied

Design for Reliability (DfR) in Space Life Support

The engineering process of Design for Reliability (DfR) is well established in the automotive and aerospace industries. DfR should be useful in the future development of space life support systems. DfR is a sequence of tasks that develop system requirements and plan reliability analysis and testing. First and fundamentally, the reliability requirement is defined. Next the system reliability model is developed, often using a reliability block diagram. The overall system reliability requirement is allocated to the subsystems and an estimate of the attainable reliability is made. This expected reliability can be improved by simplifying the design by removing components or by replacing less reliable components. Improving reliability can require difficult compromises, such as reducing performance requirements, increasing budget, or extending testing. The actual system reliability can be determined only by testing, which should continue long enough to provide the required confidence in the measured value. New systems often have unexpected design errors that cause failures in early testing. The usual reliability improvement process of testing, finding the failure modes, and redesigning to remove them reduces the failure rate and is referred to as “reliability growth.” After redesign has been completed, the system should be further tested to determine the actual achieved reliability more accurately. If the final system failure rate is too high, redundant systems can be used to improve overall operational reliability. Adding redundancy simply to increase the one- or two-fault tolerance metric may sometimes reduce reliability. Reliability can be improved in three ways: redesigning the system to include more reliable subsystems and components, reliability growth testing and failure mode removal, and by using parallel redundant systems. DfR should combine these approaches to achieve the required reliability while managing performance, cost, and schedule.

Reliability↗

NASA NTRSDate not supplied

Design for Reliability (DfR) in Space Life Support

The engineering process of Design for Reliability (DfR) is well established in the automotive and aerospace industries. DfR should be useful in the future development of space life support systems. DfR is a sequence of tasks that develop system requirements and plan reliability analysis and testing. First and fundamentally, the reliability requirement is defined. Next the system reliability model is developed, often using a reliability block diagram. The overall system reliability requirement is allocated to the subsystems and an estimate of the attainable reliability is made. This expected reliability can be improved by simplifying the design by removing components or by replacing less reliable components. Improving reliability can require difficult compromises, such as reducing performance requirements, increasing budget, or extending testing. The actual system reliability can be determined only by testing, which should continue long enough to provide the required confidence in the measured value. New systems often have unexpected design errors that cause failures in early testing. The usual reliability improvement process of testing, finding the failure modes, and redesigning to remove them reduces the failure rate and is referred to as “reliability growth.” After redesign has been completed, the system should be further tested to determine the actual achieved reliability more accurately. If the final system failure rate is too high, redundant systems can be used to improve overall operational reliability. Adding redundancy simply to increase the one- or two-fault tolerance metric may sometimes reduce reliability. Reliability can be improved in three ways: redesigning the system to include more reliable subsystems and components, reliability growth testing and failure mode removal, and by using parallel redundant systems. DfR should combine these approaches to achieve the required reliability while managing performance, cost, and schedule.

Reliability↗

NASA NTRS2020

Risk and Performance Assessment of Generic Mission Architectures: Showcasing the Artemis Mission

A has initiated a strong push to return face. In this work, we astronaut assess performance and risk for proposed mission architectures using a new Mission Architecture Risk Assessment (MARA) tool. The MARA tool can produce statistics about the availability of components and overall performance of the mission considering potential failures of any of its components. In a Monte Carlo approach, the tool repeats the mission simulation multiple times while a random generator lets modules fail according to their failure rates. The results provide statistically meaningful insights into the overall performance of the chosen architecture. A given mission architecture can be freely replicated in the tool, with the mission timeline and basic characteristics of employed mission modules (habitats, rovers, power generation units, etc.) specified in a configuration file. Crucially, failure rates for each module need to be known or estimated. The tool performs an event-driven simulation of the mission and accounts for random failure events. Failed modules can be repaired, which takes crew time but restores operations. In addition to tracking individual modules, MARA can assess the availability of predefined functions throughout the mission. For instance, the function of resource collection would require a rover to collect the resources, a power generation unit to charge the rover, and a resource processing module. Together, the modules that are required for a given function are called a functional group. Similarly, we can assess how much crew time is available to achieve a mission benefit (e.g. research, building a base, etc) as opposed to spending crew time on repairs. Here we employ the method on the proposed NASA Artemis mission. Artemis aims to return United States astronauts to the lunar surface by 2024. Results provide insights into mission failure probabilities, up- and downtime for individual modules and crew-time resources spent on the repair of failed modules. The tool also allows us to tweak the mission architecture in order to find setups that produce more favorable mission performance. As such, the tool can be an aid in improving the mission architect abling cost-benefit analysis for mission improvement.

Rumpf, Clemens M.↗

NASA NTRS2003

NASA Helps Keep the Light Burning for the Saturn Car Company

The Saturn Electronics & Engineering, Inc. (Saturn) facility in Marks, Miss., that produces lamp assemblies was experiencing itermittent problems with its automotive under the hood lamps. After numerous testing and engineering efforts, technicians could not pin down the root of the problem. So Saturn contacted the NASA Technology Assistance Program (TAP) at Stennis Space Center. The Marks production facility had been experiencing intermittent problems with under the hood lamp assemblies for some time. The failure rate, at 2 percent, was unacceptable. Every effort was made to identify the problem so that corrective action could be put in place. The problem was investigated and researched by Saturn's engineering department. In addition, Saturn brought in several independent testing laboratories. Other measures included examining the switch component suppliers and auditing them for compliance to the design specifications and for surface contaminants. All attempts to identify the factors responsible for the failures were inconclusive. In an effort to get to the root of the problem, and at the recommendation of the Mississippi Department of Economic Development, Saturn contacted the NASA TAP at Stennis. The NASA Materials and Contamination Laboratory, with assistance from the Stennis Prototype Laboratory, conducted a materials evaluation study on the switch components. The laboratory findings showed the failures were caused by a build-up of carbon-based contaminants on the switch components. Saturn Electronics & Engineering, Inc., is a minority-owned provider of contract manufacturing services to a diverse global marketplace. Saturn operates manufacturing facilities globally serving the North American, European, and Asian markets. Saturn's production facility in Marks, Mississippi, produces more than 1,000,000 lamps and switches monthly. "Since the NASA recommendations were implemented, our internal failure rate for intermittency has dropped to less than .02 percent. Most importantly, we restored our high-level of customer satisfaction. Stennis provided an invaluable service to our business," Patrick said. Both NASA and Saturn were pleased with the results form this technical assistance project. The Technology Assistance Program at Stennis makes available to the public NASA technical expertise and access to lab facilities. This project provided both services with a positive outcome.

Source record↗

NASA NTRS2011

Using Technical Performance Measures

All programs have requirements. For these requirements to be met, there must be a means of measurement. A Technical Performance Measure (TPM) is defined to produce a measured quantity that can be compared to the requirement. In practice, the TPM is often expressed as a maximum or minimum and a goal. Example TPMs for a rocket program are: vacuum or sea level specific impulse (lsp), weight, reliability (often expressed as a failure rate), schedule, operability (turn-around time), design and development cost, production cost, and operating cost. Program status is evaluated by comparing the TPMs against specified values of the requirements. During the program many design decisions are made and most of them affect some or all of the TPMs. Often, the same design decision changes some TPMs favorably while affecting other TPMs unfavorably. The problem then becomes how to compare the effects of a design decision on different TPMs. How much failure rate is one second of specific impulse worth? How many days of schedule is one pound of weight worth? In other words, how to compare dissimilar quantities in order to trade and manage the TPMs to meet all requirements. One method that has been used successfully and has a mathematical basis is Utility Analysis. Utility Analysis enables quantitative comparison among dissimilar attributes. It uses a mathematical model that maps decision maker preferences over the tradeable range of each attribute. It is capable of modeling both independent and dependent attributes. Utility Analysis is well supported in the literature on Decision Theory. It has been used at Pratt & Whitney Rocketdyne for internal programs and for contracted work such as the J-2X rocket engine program. This paper describes the construction of TPMs and describes Utility Analysis. It then discusses the use of TPMs in design trades and to manage margin during a program using Utility Analysis.

Garrett, Christopher J.↗

NASA NTRS1980

JANTX1N5420 diode

Testing of sample lots from Unitrode and Micro Semiconductor had to be stopped in group 1 test because 50% failure rate limit was reached. Failure analysis was performed only for group 2 testing because of apparent failure mode.

Source record↗

NASA NTRS1991

General Monte Carlo reliability simulation code including common mode failures and HARP fault/error-handling

A Monte Carlo Fortran computer program was developed that uses two variance reduction techniques for computing system reliability applicable to solving very large highly reliable fault-tolerant systems. The program is consistent with the hybrid automated reliability predictor (HARP) code which employs behavioral decomposition and complex fault-error handling models. This new capability is called MC-HARP which efficiently solves reliability models with non-constant failures rates (Weibull). Common mode failure modeling is also a specialty.

Platt, M. E.↗

NASA NTRS1975

Study to evaluate the effect of EVA on payload systems. Volume 1: Executive summary

Programmatic benefits to payloads are examined which can result from the routine use of extravehicular activity (EVA) during space missions. Design and operations costs were compared for 13 representative baseline payloads to the costs of those payloads adapted for EVA operations. The EVA-oriented concepts developed in the study were derived from these baseline concepts and maintained mission and program objectives as well as basic configurations. This permitted isolation of cost saving factors associated specifically with incorporation of EVA in a variety of payload designs and operations. The study results were extrapolated to a total of 74 payload programs. Using appropriate complexity and learning factors, net EVA savings were extrapolated to over $551M for NASA and U.S. civil payloads for routine operations. Adding DOD and ESRO payloads increases the net estimated savings of $776M. Planned maintenance by EVA indicated an estimated $168M savings due to elimination of automated service equipment. Contingency problems of payloads were also analyzed to establish expected failure rates for shuttle payloads. The failure information resulted in an estimated potential for EVA savings of $1.9 B.

Patrick, J. W.↗

NASA NTRS1981

Field failure mechanisms for photovoltaic modules

Beginning in 1976, Department of Energy field centers have installed and monitored a number of field tests and application experiments using current state-of-the-art photovoltaic modules. On-site observations of module physical and electrical degradation, together with in-depth laboratory analysis of failed modules, permits an overall assessment of the nature and causes of early field failures. Data on failure rates are presented, and key failure mechanisms are analyzed with respect to origin, effect, and prospects for correction. It is concluded that all failure modes identified to date are avoidable or controllable through sound design and production practices.

Dumas, L. N.↗

NASA NTRS1965

Conservative tolerance and confidence limits

Extension of exponential tolerance and confidence limits to class of distributions with monotone failure rate

FAILURE MODE↗

NASA NTRS1999

Application of Single Crystal Failure Criteria: Theory and Turbine Blade Case Study

The orientation of the single crystal material within a structural component is known to affect the strength and life of the part. The first stage blade of the High Pressure Fuel Turbopump (HPFTP)/ Alternative Turbopump Development (ATD), of the Space Shuttle Main Engine (SSME) was used to study the effects of secondary axis'orientation angles on the failure rate of the blade. A new failure criterion was developed based on normal and shear strains on the primary crystallographic planes. The criterion was verified using low cycle fatigue (LCF) specimen data and a finite element model of the test specimens. The criterion was then used to study ATD/HPFTP first stage blade failure events. A detailed ANSYS finite element model of the blade was used to calculate the failure parameter for the different crystallographic orientations. A total of 297 cases were run to cover a wide range of acceptable orientations within the blade. Those orientations are related to the base crystallographic coordinate system that was created in the ANSYS finite element model. Contour plots of the criterion as a function of orientation for the blade tip and attachment were obtained. Results of the analysis revealed a 40% increase in the failure parameter due to changing of the primary and secondary axes of material orientations. A comparison between failure criterion predictions and actual engine test data was then conducted. The engine test data comes from two ATD/HPFTP builds (units F3- 4B and F6-5D), which were ground tested on the SSME at the Stennis Space Center in Mississippi. Both units experienced cracking of the airfoil tips in multiple blades, but only a few cracks grew all the way across the wall of the hollow core airfoil.

Sayyah, Tarek↗

NASA NTRS2021

Acceleration Factors for Reliability Assessment of Polymer Tantalum Capacitors

Using polymer tantalum capacitors in Hi-Rel systems requires an assessment of the reliability characteristics of the parts. For this assessment, tantalum capacitors are typically subjected to reliability testing at temperatures and voltages exceeding their specified values, and the failure rate (FR) — or the probability of failure during use conditions — is calculated based on voltage and temperature acceleration factors. In this work, various types and lots of polymer tantalum capacitors have been tested at highly accelerated life test (HALT) conditions and the acceleration factors have been determined using different techniques. It has been shown that the behavior of capacitors under HALT conditions can be described based on the time-dependent dielectric breakdown (TDDB) model, which explains the presence of infant mortality (IM) and wear-out failures using the same failure mechanisms and allows for an assessment of the acceleration factors. The difference in acceleration factors obtained using exponential and power models is discussed. Analysis shows that with proper derating, screening, and qualification testing, the reliability of frameless Hi-Rel COTS polymer tantalum capacitors is adequate for space missions.

Alex Eidelman↗

NASA NTRS2021

Acceleration Factors for Reliability Assessment of Polymer Tantalum Capacitors

Using polymer tantalum capacitors in Hi-Rel systems requires an assessment of the reliability characteristics of the parts. For this assessment, tantalum capacitors are typically subjected to reliability testing at temperatures and voltages exceeding their specified values, and the failure rate (FR) — or the probability of failure during use conditions — is calculated based on voltage and temperature acceleration factors. In this work, various types and lots of polymer tantalum capacitors have been tested at highly accelerated life test (HALT) conditions and the acceleration factors have been determined using different techniques. It has been shown that the behavior of capacitors under HALT conditions can be described based on the time-dependent dielectric breakdown (TDDB) model, which explains the presence of infant mortality (IM) and wear-out failures using the same failure mechanisms and allows for an assessment of the acceleration factors. The difference in acceleration factors obtained using exponential and power models is discussed. Analysis shows that with proper derating, screening, and qualification testing, the reliability of frameless Hi-Rel COTS polymer tantalum capacitors is adequate for space missions.

Alex Eidelman↗

NASA NTRSDate not supplied

NASA Physics of Failure (PoF) for Reliability

An item’s reliability or longevity is dependent not only on its design but also on how it is used, manufactured, tested, and the stresses it has or will experience. Stresses include operational and environmental exposures to thermal, voltage, current, age/exposure, mechanical, and radiation mechanisms. Therefore, in reliability analysis, it is important to consider the contributions of all these factors when predicting the failure rates of components. Historically, there has been a reliance on handbook data (e.g., MIL-HDBK-217), but experience has shown that these values and distributions are not representative of actual performance. Therefore, to make more credible reliability and risk assessments for its missions, NASA must transition to estimating likelihoods of failure based on an item’s reliability or longevity factors (or the physical susceptibilities and strengths impacting the design’s performance) has or will experience, whenever possible. To facilitate this transition, a Handbook on Methodology for Physics of Failure Based Reliability Assessments has been developed by NASA to assist in applying physics experiences or experimental physics for empirical analysis and conceptualized physics exposures or theoretical physics for deterministic analysis, to develop and aggregate realistic likelihoods of failure leading to more credible forecasts of item performance and longevity. In addition, since it is NASA’s intention that this document continues to evolve based on community lessons learned and the introduction of new assessment methodologies, NASA is encouraging and appreciates the contributions of current and future authors to maintain and enhance this handbook and its supporting case studies.

PoF↗