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At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

NASA NTRS1984

NASA: Model development for human factors interfacing

The results of an intensive literature review in the general topics of human error analysis, stress and job performance, and accident and safety analysis revealed no usable techniques or approaches for analyzing human error in ground or space operations tasks. A task review model is described and proposed to be developed in order to reduce the degree of labor intensiveness in ground and space operations tasks. An extensive number of annotated references are provided.

Smith, L. L.↗

NASA NTRS1975

A Method for the Study of Human Factors in Aircraft Operations

A method for the study of human factors in the aviation environment is described. A conceptual framework is provided within which pilot and other human errors in aircraft operations may be studied with the intent of finding out how, and why, they occurred. An information processing model of human behavior serves as the basis for the acquisition and interpretation of information relating to occurrences which involve human error. A systematic method of collecting such data is presented and discussed. The classification of the data is outlined.

Barnhart, W.↗

NASA NTRS2017

Designing and Developing Web-Based Administrative Tools for Program Management

The task assigned for this internship was to develop a new tool for tracking projects, their subsystems, the leads, backups, and other employees assigned to them, as well as all the relevant information related to the employee (WBS (time charge) codes, time distribution, certifications, and assignments). Currently, this data is tracked manually using a number of different spreadsheets and other tools simultaneously by a number of different people; some of these documents are then merged into one large document. This often leads to inconsistencies and loss in data due to human error. By simplifying the process of tracking this data and aggregating it into a single tool, it is possible to significantly decrease the potential for human error and time spent collecting and checking this information. II. Objective The main objective of this internship is to develop a web-based tool using Ruby on Rails to serve as a method of easily tracking projects, subsystems, and points of contact, along with employees, their assignments, time distribution, certifications, and contact information. Additionally, this tool must be capable of generating a number of different reports based on the data collected. It was important that this tool deliver all of this information using a readable and intuitive interface.

Material↗

NASA NTRS1989

Airline Crew Training

The discovery that human error has caused many more airline crashes than mechanical malfunctions led to an increased emphasis on teamwork and coordination in airline flight training programs. Human factors research at Ames Research Center has produced two crew training programs directed toward more effective operations. Cockpit Resource Management (CRM) defines areas like decision making, workload distribution, communication skills, etc. as essential in addressing human error problems. In 1979, a workshop led to the implementation of the CRM program by United Airlines, and later other airlines. In Line Oriented Flight Training (LOFT), crews fly missions in realistic simulators while instructors induce emergency situations requiring crew coordination. This is followed by a self critique. Ames Research Center continues its involvement with these programs.

Source record↗

NASA NTRS2003

Associations between errors and contributing factors in aircraft maintenance

In recent years cognitive error models have provided insights into the unsafe acts that lead to many accidents in safety-critical environments. Most models of accident causation are based on the notion that human errors occur in the context of contributing factors. However, there is a lack of published information on possible links between specific errors and contributing factors. A total of 619 safety occurrences involving aircraft maintenance were reported using a self-completed questionnaire. Of these occurrences, 96% were related to the actions of maintenance personnel. The types of errors that were involved, and the contributing factors associated with those actions, were determined. Each type of error was associated with a particular set of contributing factors and with specific occurrence outcomes. Among the associations were links between memory lapses and fatigue and between rule violations and time pressure. Potential applications of this research include assisting with the design of accident prevention strategies, the estimation of human error probabilities, and the monitoring of organizational safety performance.

Professional Competence↗

NASA NTRS2015

Bayesian Safety Risk Modeling of Human-Flightdeck Automation Interaction

Usage of automatic systems in airliners has increased fuel efficiency, added extra capabilities, enhanced safety and reliability, as well as provide improved passenger comfort since its introduction in the late 80's. However, original automation benefits, including reduced flight crew workload, human errors or training requirements, were not achieved as originally expected. Instead, automation introduced new failure modes, redistributed, and sometimes increased workload, brought in new cognitive and attention demands, and increased training requirements. Modern airliners have numerous flight modes, providing more flexibility (and inherently more complexity) to the flight crew. However, the price to pay for the increased flexibility is the need for increased mode awareness, as well as the need to supervise, understand, and predict automated system behavior. Also, over-reliance on automation is linked to manual flight skill degradation and complacency in commercial pilots. As a result, recent accidents involving human errors are often caused by the interactions between humans and the automated systems (e.g., the breakdown in man-machine coordination), deteriorated manual flying skills, and/or loss of situational awareness due to heavy dependence on automated systems. This paper describes the development of the increased complexity and reliance on automation baseline model, named FLAP for FLightdeck Automation Problems. The model development process starts with a comprehensive literature review followed by the construction of a framework comprised of high-level causal factors leading to an automation-related flight anomaly. The framework was then converted into a Bayesian Belief Network (BBN) using the Hugin Software v7.8. The effects of automation on flight crew are incorporated into the model, including flight skill degradation, increased cognitive demand and training requirements along with their interactions. Besides flight crew deficiencies, automation system failures and anomalies of avionic systems are also incorporated. The resultant model helps simulate the emergence of automation-related issues in today's modern airliners from a top-down, generalized approach, which serves as a platform to evaluate NASA developed technologies

Ancel, Ersin↗

NASA NTRS1981

An investigation of reports of Controlled Flight Toward Terrain (CFTT)

Some 258 reports from more than 23,000 documents in the files of the Aviation Safety Reporting System (ASRS) were found to be to the hazard of flight into terrain with no prior awareness by the crew of impending disaster. Examination of the reports indicate that human error was a casual factor in 64% of the incidents in which some threat of terrain conflict was experienced. Approximately two-thirds of the human errors were attributed to controllers, the most common discrepancy being a radar vector below the Minimum Vector Altitude (MVA). Errors by pilots were of a much diverse nature and include a few instances of gross deviations from their assigned altitudes. The ground proximity warning system and the minimum safe altitude warning equipment were the initial recovery factor in some 18 serious incidents and were apparently the sole warning in six reported instances which otherwise would most probably have ended in disaster.

Porter, R. F.↗

NASA NTRS1985

Pilots of the future - Human or computer?

In connection with the occurrence of aircraft accidents and the evolution of the air-travel system, questions arise regarding the computer's potential for making fundamental contributions to improving the safety and reliability of air travel. An important result of an analysis of the causes of aircraft accidents is the conclusion that humans - 'pilots and other personnel' - are implicated in well over half of the accidents which occur. Over 70 percent of the incident reports contain evidence of human error. In addition, almost 75 percent show evidence of an 'information-transfer' problem. Thus, the question arises whether improvements in air safety could be achieved by removing humans from control situations. In an attempt to answer this question, it is important to take into account also certain advantages which humans have in comparison to computers. Attention is given to human error and the effects of technology, the motivation to automate, aircraft automation at the crossroads, the evolution of cockpit automation, and pilot factors.

Chambers, A. B.↗

NASA NTRS2011

Automatic Facial Expression Recognition and Operator Functional State

The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions.

Blanson, Nina↗

NASA NTRS2012

Automatic Facial Expression Recognition and Operator Functional State

Blanson, Nina↗

NASA NTRSDate not supplied

Human Systems Integration (HSI) Framework and Training - Shifting the View of HSI for Better Implementation

The Implementation of Human Systems Integration (HSI) presents challenges within the acquisition community for two reasons. The first is that misconceptions of HSI still exist, with many Program Managers (PMs) and leadership uncertain of the value or where to begin. The second is due to an unbalanced approach to HSI in its own framework. These implementation challenges lead to barriers in the early prevention of mishaps. Understanding HSI practices and how they should be implemented in the Acquisition Product Life Cycle (PLC) has been a challenge across the government, leaving the value of HSI unknown and misunderstood with Program Managers. In the case for many acquisition programs, HSI is not implemented in early design, losing the perspective on human capabilities and limitations, creating impacts on human-centered design. Expectations in human performance are not clearly set and operations are baselined with no margin for changes in technology and processes that will affect system performance. The HSI framework addresses total system performance holistically using collaboration as the primary tool. The goal is to create a system with efficiencies while minimizing risk to the operators, maintainers, and support personnel, as well as any collateral personnel and systems. To accomplish this, HSI should be implemented as part of preemptive measures to minimize potential human error and mishaps during the operation phase. Investigative and assessment tools exist that consider events, issues, and other outside influences of a system that may not fall under the current construct of the HSI domains, leaving gaps in early HSI implementation and affecting the prevention of human errors and mishaps. This presentation will outline what NASA HSI is doing to support Early HSI implementation and Operational Performance shifts that affect human performance.

Anthony T Thomas↗

NASA NTRSDate not supplied

Short Circuiting the Controller – Missteps in Maintenance and Inspection of Process and Wiring in STS-93

The primary objective of the Space Transportation System mission 93 (STS-93) was to deploy the Chandra X-Ray Observatory. Chandra, the world's most powerful X-Ray telescope, allowed scientists from around the world to study some of the most distant and dynamic objects in the universe. Stripped of nearly 7,000 pounds of its own gear to make room for the payload, the orbiter assigned to this mission was Space Shuttle Columbia. Prior to STS-93, Columbia had flown 25 flights and was NASA's oldest and heaviest orbiter. On July 23, 1999, after two prior launch scrubs, Eileen Collins and her crew of four launched from Kennedy Space Center. About five seconds after launch, Mission Control at Johnson Space Center detected a voltage drop on one of Columbia's electrical buses. As a result of this power fluctuation, a primary and back-up Main Engine controller dropped offline. Given design redundancy, the two remaining controllers supported all three engines. If there had been any other AC bus issues, one engine of the three on the Orbiter would have shut down. The redundant set of digital computer units in each engine controller saved Columbia and her crew from a very risky contingency abort. Post-flight inspection revealed soot on a screw head and a hole in an adjacent Kapton insulated wire. The single strand of polyimide wire was located nearly half-way down the payload bay. The Shuttle Independent Assessment Team (SIAT) reported that the wire had rubbed and chaffed against a burred screw head. The burr was later determined to be the result of overtightening of the screw by a technician during a maintenance refurbishment. Alone, the burr may not have been problematic, but later, during another ground processing event, possibly years after, someone inadvertently stepped on the wiring harness. With the pressure and motion of unintended contact, some of the Kapton insulation rubbed off against the burred screw head. The SIAT suspected the wire damage was pre-existing and was caused 4 or 5 years prior to the flight. Finally, the intense vibrations during the launch sequence allowed contact between the exposed conductor and exposed metal area on the burred screw head, resulting in the arcing and shorting of the wire. Due to the quick turnaround times of Space Shuttle Orbiters, wiring issues caused from multiple maintenance events were often overlooked. Additionally, failing to incorporate thorough and early inclusion of human systems integration (HSI) applications as a crucial part of the decision process can result in these types of misshaps. In order to reduce human error associated with integrated manufacturing, maintenance, refurbishment and flight preparations, wire inspection criteria should be refined and standardized, visual inspection processes should be quantified, and technicians should be certified by specially trained instructors. This case, among many others, unveils why human error management and development of safety metrics is a vital piece in the development of complex systems, and why it should be supported aggressively and implemented program wide.

Human Systems Integration↗

NASA NTRSDate not supplied

Going beyond reliability to achieve robustness

Reliability is the ability to perform well and consistently. More formally, reliability is defined as the mathematical probability that a system does not fail during a specified time period under its specified operating conditions. The specified operating conditions often go beyond the nominal environment to include variations and challenges encountered in operational use. The difficulty is that systems are often operated outside of their specified operating conditions and, if they fail, the designers are in theory blameless. Unanticipated damaging events include internal failures, external disruptions in supporting systems, accidents, and repurposing. The most common explanation of a system failure is human error, which is usually the first assumption of the system designers. Robustness is the capability to perform without failure under a wide range of conditions that go beyond the specified operating conditions. The first step towards improving robustness would be to expand the system’s specified operating conditions to include a wider range of anticipated challenges, especially human error. Beyond this, there is a need for general approach to reduce the impact of unanticipated future events, the unknown unknowns, by improving the system’s general ability to cope. Robustness can be improved by providing additional processing capacity, larger flow control buffers, increased backup storage, more online redundancy, and more capable supervisory monitoring and control.

Harry W Jones↗

DOE OSTI2025

EVALUATION OF HRA METHODOLOGIES FOR APPLICATION IN SDP WORK

This study critically evaluates human reliability analysis (HRA) methodologies applicable to regulatory probabilistic safety assessment (PSA) model, with a particular focus on their role in supporting the significance determination process (SDP) in nuclear safety assessment. Firstly, three widely utilized HRA methods – IDHEAS-ECA, SPAR-H, and ASEP/THERP – were qualitatively and quantitatively assessed. Qualitative assessments were conducted using attributes from the NEA/CSNI/R(2015)1 report, while quantitative evaluations employed regression and correlation analyses to compare predicted human error probabilities (HEPs) against empirical data. Results reveal distinct strengths, for example, IDHEAS-ECA’s robust predictive accuracy and K-HRA’s alignment with operational practices. In addition, dependency analysis and recovery analysis were critically evaluated. For dependency analysis, the methods’ handling of inter-task dependencies and their impact on HEPs were examined, while recovery analysis highlighted strategies for mitigating failure events. Furthermore, strategies were proposed to evaluate performance-shaping factors under conditions of reduced human performance, such as stress, fatigue, or cognitive overload, addressing specific challenges faced in SDP evaluations. Human errors from KINS’s operational performance information system event reports were evaluated as a case study. This study identifies gaps and provides actionable insights to ensure their validity and applicability in SDP HRA applications. This paper is a part of research conducted by KINS, and it should be noted that this result does not represent the regulatory position of KINS.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance

In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.

Reinforcement learning↗

NASA NTRS1981

A review of in-flight emergencies in the ASRS data base

A series of 154 in-flight emergencies as reported to the Aviation Safety Reporting System are described. The various types of emergencies are examined and an attempt is made to determine the human errors and other factors associated with each incident, as well as the measures taken to resolve the emergency. It is concluded that nearly one half of those emergencies reported were related to failure or malfunction of aircraft subsystems. Of all the emergencies, nearly one quarter were associated with power plant failure. Other frequently encountered emergency types are associated with operation in instrument meteorological conditions without appropriate clearance or qualification, and with low fuel state situations. Human error is prominently featured in many of the incidents, appearing in the actions of pilots and air traffic controllers.

Porter, R. F.↗

NASA NTRS1998

Learning from Automation Surprises and "Going Sour" Accidents: Progress on Human-Centered Automation

Advances in technology and new levels of automation on commercial jet transports has had many effects. There have been positive effects from both an economic and a safety point of view. The technology changes on the flight deck also have had reverberating effects on many other aspects of the aviation system and different aspects of human performance. Operational experience, research investigations, incidents, and occasionally accidents have shown that new and sometimes surprising problems have arisen as well. What are these problems with cockpit automation, and what should we learn from them? Do they represent over-automation or human error? Or instead perhaps there is a third possibility - they represent coordination breakdowns between operators and the automation? Are the problems just a series of small independent glitches revealed by specific accidents or near misses? Do these glitches represent a few small areas where there are cracks to be patched in what is otherwise a record of outstanding designs and systems? Or do these problems provide us with evidence about deeper factors that we need to address if we are to maintain and improve aviation safety in a changing world? How do the reverberations of technology change on the flight deck provide insight into generic issues about developing human-centered technologies and systems (Winograd and Woods, 1997)? Based on a series of investigations of pilot interaction with cockpit automation (Sarter and Woods, 1992; 1994; 1995; 1997a, 1997 b), supplemented by surveys, operational experience and incident data from other studies (e.g., Degani et al., 1995; Eldredge et al., 1991; Tenney et al., 1995; Wiener, 1989), we too have found that the problems that surround crew interaction with automation are more than a series of individual glitches. These difficulties are symptoms that indicate deeper patterns and phenomena concerning human-machine cooperation and paths towards disaster. In addition, we find the same kinds of patterns behind results from studies of physician interaction with computer-based systems in critical care medicine (e.g., Moll van Charante et al., 1993; Obradovich and Woods, 1996; Cook and Woods, 1996). Many of the results and implications of this kind of research are synthesized and discussed in two comprehensive volumes, Billings (1996) and Woods et al. (1994). This paper summarizes the pattern that has emerged from our research, related research, incident reports, and accident investigations. It uses this new understanding of why problems arise to point to new investment strategies that can help us deal with the perceived "human error" problem, make automation more of a team player, and maintain and improve safety.

Woods, David D.↗

NASA NTRS1986

Towards automatic Markov reliability modeling of computer architectures

The analysis and evaluation of reliability measures using time-varying Markov models is required for Processor-Memory-Switch (PMS) structures that have competing processes such as standby redundancy and repair, or renewal processes such as transient or intermittent faults. The task of generating these models is tedious and prone to human error due to the large number of states and transitions involved in any reasonable system. Therefore model formulation is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model formulation. This paper presents an overview of the Automated Reliability Modeling (ARM) program, under development at NASA Langley Research Center. ARM will accept as input a description of the PMS interconnection graph, the behavior of the PMS components, the fault-tolerant strategies, and the operational requirements. The output of ARM will be the reliability of availability Markov model formulated for direct use by evaluation programs. The advantages of such an approach are (a) utility to a large class of users, not necessarily expert in reliability analysis, and (b) a lower probability of human error in the computation.

Liceaga, C. A.↗