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NASA NTRSDate not supplied

Dose Error Impacts on A Collection of Realistic Dose-Response Curves Based on A NASA Sonic Boom Community Noise Survey

The National Aeronautics and Space Administration (NASA) plans to conduct surveys of community response to quiet supersonic flight to collect dose-response data for international regulators. Previous models of noise dose versus annoyance response ignored uncertainty in the noise dose experienced by survey respondents. This dose error causes attenuation bias and results in inaccurate dose-response relationships. The impacts of dose errors can be explored by introducing error into the dose estimates of a simulated community noise survey. The simulated population annoyance response is determined by specific noise response characteristics, including the onset of annoyance intercept, rate of increasing annoyance, participant intercept variability, and response rates. This paper explores the effects of dose errors on notional populations created by perturbing the noise response characteristics observed in participants in a recent NASA flight test, QSF18. The QSF18-based population parameters were shifted up to ±20% to create the study populations. For the anticipated dose range of the X-59, changes to the onset of annoyance intercept have the greatest impact on erroneous model results. Quantifying point estimate errors illustrates the impact of dose error, with errors up to 14 dB observed for a fixed high annoyance percentage.

dose-response modeling↗

NASA NTRS2024

Summary and Annotated Bibliography of Measurement Error Corrections with Potential Application in Future Quesst Mission Community Noise Studies

This document is motivated by likely needs of the Quesst mission community response tests, which will culminate in data collection and estimation of dose-response regression relationships for consideration by domestic and international aviation regulators. Furthermore, basic research questions evaluating interactions between rates of community annoyance, dose levels, and indicators of the presence of rattle, vibration, and startle hinge on hypothesis testing in the context of regression models. For a variety of reasons, noise doses may be known only imprecisely and may not reflect the actual level experienced by responding subjects. These differences between true dose and estimated dose, be they systematic or random, constitute covariate measurement error. Available statistics literature speaks to the impacts of measurement error on regression models, both in terms of bias in estimated coefficients and predicted values, and in terms of the loss of statistical power for hypothesis testing. Given the particulars of a categorical annoyance response variable and a continuous noise dose predictor variable subject to measurement error during testing, the emphasis of this report is on findings and methods pertinent to generalized linear (and mixed) models likely to be employed during the Quesst mission community tests. We reach the following conclusions: 1. Of four reviewed methods, structural Bayesian measurement error models and simulation extrapolation (SIMEX) may be the most readily applicable to Quesst mission community noise study objectives. 2. If warranted, a linear measurement model can help model systematic sources of measurement error that the classical measurement error does not. 3. For its ready implementation and small additional input requirements, simulation extrapolation may be ideally suited for addressing secondary research questions involving interactions between annoyance, noise dose, and other factors through hypothesis testing. 4. For their flexibility and ability to propagate uncertainty, structural Bayesian hierarchical models have great appeal for mission purposes; some care may be needed in developing appropriate probability models describing actual noise exposure during testing. An annotated bibliography logs additional papers and resources that may be of value to analysts in other projects and disciplines.

Dose-Response Model↗

NASA NTRS2012

Avoiding Human Error in Mission Operations: Cassini Flight Experience

Operating spacecraft is a never-ending challenge and the risk of human error is ever- present. Many missions have been significantly affected by human error on the part of ground controllers. The Cassini mission at Saturn has not been immune to human error, but Cassini operations engineers use tools and follow processes that find and correct most human errors before they reach the spacecraft. What is needed are skilled engineers with good technical knowledge, good interpersonal communications, quality ground software, regular peer reviews, up-to-date procedures, as well as careful attention to detail and the discipline to test and verify all commands that will be sent to the spacecraft. Two areas of special concern are changes to flight software and response to in-flight anomalies. The Cassini team has a lot of practical experience in all these areas and they have found that well-trained engineers with good tools who follow clear procedures can catch most errors before they get into command sequences to be sent to the spacecraft. Finally, having a robust and fault-tolerant spacecraft that allows ground controllers excellent visibility of its condition is the most important way to ensure human error does not compromise the mission.

guidance and control↗

NASA NTRS2017

Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)

Research results have shown that more than half of aviation, aerospace and aeronautics mishaps/incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.

human error↗

NASA NTRS2017

Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS)

Research results have shown that more than half of aviation, aerospace and aeronautics mishaps incidents are attributed to human error. As a part of Safety within space exploration ground processing operations, the identification and/or classification of underlying contributors and causes of human error must be identified, in order to manage human error. This research provides a framework and methodology using the Human Error Assessment and Reduction Technique (HEART) and Human Factor Analysis and Classification System (HFACS), as an analysis tool to identify contributing factors, their impact on human error events, and predict the Human Error probabilities (HEPs) of future occurrences. This research methodology was applied (retrospectively) to six (6) NASA ground processing operations scenarios and thirty (30) years of Launch Vehicle related mishap data. This modifiable framework can be used and followed by other space and similar complex operations.

mishaps↗

NASA NTRS2016

Optical Error Budgeting Using Linearized Ray-Trace Models

The Root-Sum-Squared, or “RSS” wavefront error model is a simple, scalar tool, commonly used for space telescope error budgeting. At the same time, much more detailed models, combining ray-trace and Fourier optics with optical alignments and wavefront controls, can provide accurate, high -resolution simulations for detailed system and subsystem design. This paper makes a connection between the two modeling approaches by deriving RSS model coefficients from ray-trace models, including the effects of wavefront controls, for computing system performance from component error statistics. It is shown that, properly constructed, the simple RSS error budget is a covariance analysis, and can be as accurate as high-resolution wavefront models for statistical wavefront error prediction. A notional segmented-aperture space telescope is used to illustrate this error modeling process.

Wavefront error↗

NASA NTRSDate not supplied

Human Error Analysis for Human-Rated Space Systems

Humans bring unique capabilities to space systems and contribute to mission success in a manner that cannot be matched by machines. Nevertheless, from time to time, human error can present a threat to system performance, and system designers must anticipate and manage this risk. NASA’s Human-Rating Requirements for Space Systems call for program managers to conduct a human error analysis (HEA) during system development but does not specify how to do this. In 2018, NASA’s Engineering and Safety Center asked the authors to develop a guidance document on HEA. The resulting position paper outlines a suggested method for HEA and makes it clear that error analysis is about identifying and mitigating problems at a system level, and not about finding fault with individuals. Error management strategies must be directed at error-producing conditions, thereby reducing the likelihood of human error, while retaining the positive contribution that humans make to system operations.

human error human-rated space↗

NASA NTRS2024

Historical Aerospace Software Errors Categorized to Influence Fault Tolerance

Since the first use of computers in space and aircraft, software errors have occurred. These errors can manifest as loss-of-life or less catastrophically. As the demand for automation increases, software in mission or safety-critical systems should be designed to be tolerant to the most likely software faults. This paper categorizes a set of 55 historic aerospace software error incidents from 1962 to 2023 to determine trends of how and where automation is most likely to fail, behaving unexpectedly. A distinction between software producing unexpected (erroneous) output versus no output (failsilent) is introduced. Of the historical incidents analyzed, 85% were from software producing wrong output rather than simply stopping. Rebooting was found to be ineffective to clear erroneous behavior, and not reliable to recover from silent failures. Error origin was within the code/logic itself in 58% of cases, 16% from configurable data, 15% from unexpected sensor input, and 11% from command/operator input. A substantial forty percent (40%) of unexpected software behavior was indicated by the absence of code, arising from unanticipated situations and missing requirements, and 16% of incidents were subjectively deemed “unknown-unknowns”. No incidents were found to be the result of programming language, compiler, tool, or operating system; and only sixteen percent (16%) of all incidents were considered errors traditional computer science/programming in nature. These findings indicate that for fault tolerance, erroneous automation behavior must be a primary consideration especially at critical moments, and reboot recoverability may not be viable. Special care should be taken to validate configurable data and commands prior to use. “Test-like-you-fly”, including hardware-in-the-loop combined with robust off-nominal testing should be used to uncover missing logic arising from unanticipated situations not covered by requirements alone. This study uniquely focuses on manifestations of unexpected flight software behavior, independent of ultimate root cause. We characterize software error behavior and origin to improve software design, test, and operations for resilience to the most common manifestations, and provide a rich dataset for further study.

Aerospace↗

NASA NTRSDate not supplied

System-Wide Error Attribution in Multi-Vehicle Operations: Theoretical Explanation, Implications, and Applications

In multi-vehicle aerial operation contexts, a single or several human operators (m) are responsible for managing multiple uncrewed vehicles (N). This is referred to as the m:N operational paradigm. When automation error occurs during m:N operations, especially if multiple vehicles exhibit automation error, a question is raised: What level of system globality will the error be attributed to? Globality refers to the levels within the conceptual hierarchy of a perceptual object. Significant costs could be incurred if the error is falsely attributed to a higher globality level than it should be (e.g., multiple groups of vehicles as opposed to a single vehicle), and those vehicles are subsequently subject to grounding and evaluation/maintenance. Likewise, it could be problematic if error is falsely attributed to a component (i.e., a lower globality level), resulting in ongoing, unsafe operation of vehicles that should be grounded or evaluated. Thus, to meet required safety standards and facilitate commercial viability, multi-vehicle operators must be capable of appropriately attributing the level of globality of automation error within the system they are responsible for.

Error↗

NASA NTRS1972

Analysis of instrumentation error effects on the identification accuracy of aircraft parameters

An analytical investigation is presented of the effect of unmodeled measurement system errors on the accuracy of aircraft stability and control derivatives identified from flight test data. Such error sources include biases, scale factor errors, instrument position errors, misalignments, and instrument dynamics. Two techniques (ensemble analysis and simulated data analysis) are formulated to determine the quantitative variations to the identified parameters resulting from the unmodeled instrumentation errors. The parameter accuracy that would result from flight tests of the F-4C aircraft with typical quality instrumentation is determined using these techniques. It is shown that unmodeled instrument errors can greatly increase the uncertainty in the value of the identified parameters. General recommendations are made of procedures to be followed to insure that the measurement system associated with identifying stability and control derivatives from flight test provides sufficient accuracy.

Sorensen, J. A.↗

NASA NTRS1975

Monte Carlo analysis of inaccuracies in estimated aircraft parameters caused by unmodeled flight instrumentation errors

An output error estimation algorithm was used to evaluate the effects of both static and dynamic instrumentation errors on the estimation of aircraft stability and control parameters. A Monte Carlo error analysis, using simulated cruise flight data, was performed for a high-performance military aircraft, a large commercial transport, and a small general aviation aircraft. The results indicate that unmodeled instrumentation errors can cause inaccuracies in the estimated parameters which are comparable to their nominal values. However, the corresponding perturbations to the estimated output response trajectories and characteristics equation pole locations appear to be relatively small. Control input errors and dynamic lags were found to be in the most significant of the error sources evaluated.

Hodge, W. F.↗

NASA NTRS1975

A Monte Carlo analysis of the effects of instrumentation errors on aircraft parameter identification

An output error estimation algorithm was used to evaluate the effects of both static and dynamic instrumentation errors on the estimation of aircraft stability and control parameters. A Monte Carlo analysis, using simulated cruise flight data, was performed for a high performance military aircraft, a large commercial transport, and a small general-aviation aircraft. The effects of variations in the information content of the flight data, resulting from two different choices of control input maneuvers, were also determined. The results indicate that unmodeled instrumentation errors can cause inaccuracies in the estimated parameters which are comparable to their nominal values. Control input errors and angular accelerometer lags were found to be most significant of the instrumentation errors evaluated, and the perturbations they produce are much larger than those arising from the combined effects of static errors and white noise in the output response measurements.

Bryant, W. H.↗

NASA NTRS1977

Altimeter error sources at the 10-cm performance level

Error sources affecting the calibration and operational use of a 10 cm altimeter are examined to determine the magnitudes of current errors and the investigations necessary to reduce them to acceptable bounds. Errors considered include those affecting operational data pre-processing, and those affecting altitude bias determination, with error budgets developed for both. The most significant error sources affecting pre-processing are bias calibration, propagation corrections for the ionosphere, and measurement noise. No ionospheric models are currently validated at the required 10-25% accuracy level. The optimum smoothing to reduce the effects of measurement noise is investigated and found to be on the order of one second, based on the TASC model of geoid undulations. The 10 cm calibrations are found to be feasible only through the use of altimeter passes that are very high elevation for a tracking station which tracks very close to the time of altimeter track, such as a high elevation pass across the island of Bermuda. By far the largest error source, based on the current state-of-the-art, is the location of the island tracking station relative to mean sea level in the surrounding ocean areas.

Martin, C. F.↗

NASA NTRS1976

The statistical significance of error probability as determined from decoding simulations for long codes

The very low error probability obtained with long error-correcting codes results in a very small number of observed errors in simulation studies of practical size and renders the usual confidence interval techniques inapplicable to the observed error probability. A natural extension of the notion of a 'confidence interval' is made and applied to such determinations of error probability by simulation. An example is included to show the surprisingly great significance of as few as two decoding errors in a very large number of decoding trials.

Massey, J. L.↗

NASA NTRS1978

GCF HSD error control

A selective repeat automatic repeat request (ARQ) system was implemented under software control in the Ground Communications Facility error detection and correction (EDC) assembly at JPL and the comm monitor and formatter (CMF) assembly at the DSSs. The CMF and EDC significantly improved real time data quality and significantly reduced the post-pass time required for replay of blocks originally received in error. Since the remote mission operation centers (RMOCs) do not provide compatible error correction equipment, error correction will not be used on the RMOC-JPL high speed data (HSD) circuits. The real time error correction capability will correct error burst or outage of two loop-times or less for each DSS-JPL HSD circuit.

Hung, C. K.↗

NASA NTRS1977

Error rate information in attention allocation pilot models

The Northrop urgency decision pilot model was used in a command tracking task to compare the optimized performance of multiaxis attention allocation pilot models whose urgency functions were (1) based on tracking error alone, and (2) based on both tracking error and error rate. A matrix of system dynamics and command inputs was employed, to create both symmetric and asymmetric two axis compensatory tracking tasks. All tasks were single loop on each axis. Analysis showed that a model that allocates control attention through nonlinear urgency functions using only error information could not achieve performance of the full model whose attention shifting algorithm included both error and error rate terms. Subsequent to this analysis, tracking performance predictions for the full model were verified by piloted flight simulation. Complete model and simulation data are presented.

Faulkner, W. H.↗

NASA NTRS1978

Inspection error and its adverse effects - A model with implications for practitioners

Inspection error has clearly been shown to have adverse effects upon the results desired from a quality assurance sampling plan. These effects upon performance measures have been well documented from a statistical point of view. However, little work has been presented to convince the QC manager of the unfavorable cost consequences resulting from inspection error. This paper develops a very general, yet easily used, mathematical cost model. The basic format of the well-known Guthrie-Johns model is used. However, it is modified as required to assess the effects of attributes sampling errors of the first and second kind. The economic results, under different yet realistic conditions, will no doubt be of interest to QC practitioners who face similar problems daily. Sampling inspection plans are optimized to minimize economic losses due to inspection error. Unfortunately, any error at all results in some economic loss which cannot be compensated for by sampling plan design; however, improvements over plans which neglect the presence of inspection error are possible. Implications for human performance betterment programs are apparent, as are trade-offs between sampling plan modification and inspection and training improvements economics.

Collins, R. D., Jr.↗

NASA NTRS1979

Optimal estimation of large structure model errors

In-flight estimation of large structure model errors is usually required as a means of detecting inevitable deficiencies in large structure controller/estimator models. The present paper deals with a least-squares formulation which seeks to minimize a quadratic functional of the model errors. The properties of these error estimates are analyzed. It is shown that an arbitrary model error can be decomposed as the sum of two components that are orthogonal in a suitably defined function space. Relations between true and estimated errors are defined. The estimates are found to be approximations that retain many of the significant dynamics of the true model errors. Current efforts are directed toward application of the analytical results to a reference large structure model.

Rodriguez, G.↗