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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Why System Safety Professionals Should Read Accident Reports

System safety professionals, both researchers and practitioners, who regularly read accident reports reap important benefits. These benefits include an improved ability to separate myths from reality, including both myths about specific accidents and ones concerning accidents in general; an increased understanding of the consequences of unlikely events, which can help inform future designs; a greater recognition of the limits of mathematical models; and guidance on potentially relevant research directions that may contribute to safety improvements in future systems.

Holloway, C. M.↗
NASA NTRS1989

Multi-media authoring - Instruction and training of air traffic controllers based on ASRS incident reports

This paper discusses the use of computer-assisted instructions and flight simulations to enhance procedural and perceptual motor task training. Attention is called to the fact that incorporating the accident and incident data contained in reports filed with the Aviation Safety Reporting System (ASRS) would be a valuable training tool which the learner could apply for other situations. The need to segment the events is emphasized; this would make it possible to modify events in order to suit the needs of the training environment. Methods were developed for designing meaningful scenario development on runway incursions on the basis of analysis of ASRS reports. It is noted that, while the development of interactive training tools using the ASRS and other data bases holds much promise, the design and production of interactive video programs and laser disks are very expensive. It is suggested that this problem may be overcome by sharing the costs of production to develop a library of materials available to a broad range of users.

Armstrong, Herbert B.↗
NASA NTRS1995

What ASRS incident data tell about flight crew performance during aircraft malfunctions

This research examined 230 reports in NASA's Aviation Safety Reporting System's (ASRS) database to develop a better understanding of factors that can affect flight crew performance when crew are faced with inflight aircraft malfunctions. Each report was placed into one of two categories, based on severity of the malfunction. Report analysis was then conducted to extract information regarding crew procedural issues, crew communications and situational awareness. A comparison of these crew factors across malfunction type was then performed. This comparison revealed a significant difference in ways that crews dealt with serious malfunctions compared to less serious malfunctions. The authors offer recommendations toward improving crew performance when faced with inflight aircraft malfunctions.

Sumwalt, Robert L.↗
NASA NTRSDate not supplied

MIKA: Manager for Intelligent Knowledge Access Toolkit for Engineering Knowledge Discovery and Information Retrieval

Repositories of safety reports are often underutilized and only analyzed manually by trained experts, despite safety management systems requiring reports. These collections of documents contain a wealth of information from past projects and operations that could improve system safety and design. Advances in natural language processing techniques have improved information extraction and retrieval in consumer technology, biomedicine, and finance, for instance, but have not been applied to engineering documents on the same scale. To this end, the Manager for Intelligent Knowledge Access (MIKA) open-source toolkit has been developed for rapid knowledge discovery and information retrieval in safety engineering applications. The MIKA toolkit uses state-of-the-art natural language processing algorithms and allows a user to apply these methods to their own dataset. This paper describes the MIKA toolkit and its two primary capabilities, knowledge discovery and information retrieval, and demonstrates the toolkit via a case study on National Transportation Safety Board (NTSB) reports.

Machine Learning↗
NASA NTRSDate not supplied

MIKA: Manager for Intelligent Knowledge Access Toolkit for Engineering Knowledge Discovery and Information Retrieval

Repositories of safety reports are often underutilized and only analyzed manually by trained experts, despite safety management systems requiring reports. These collections of documents contain a wealth of information from past projects and operations that could improve system safety and design. Advances in natural language processing techniques have improved information extraction and retrieval in consumer technology, biomedicine, and finance, for instance, but have not been applied to engineering documents on the same scale. To this end, the Manager for Intelligent Knowledge Access (MIKA) open-source toolkit has been developed for rapid knowledge discovery and information retrieval in safety engineering applications. The MIKA toolkit uses state-of-the-art natural language processing algorithms and allows a user to apply these methods to their own dataset. This paper describes the MIKA toolkit and its two primary capabilities, knowledge discovery and information retrieval, and demonstrates the toolkit via a case study on National Transportation Safety Board (NTSB) reports.

Systems Engineering↗
NASA NTRSDate not supplied

ASRS for Unmanned Aviation Systems

This presentation provides an overview of NASA's Aviation Safety Reporting System (ASRS) for Unmanned Aviation Systems / drones

Aviation Safety Reporting System↗
NASA NTRS2001

Personal Electronic Devices and Their Interference with Aircraft Systems

A compilation of data on personal electronic devices (PEDs) attributed to having created anomalies with aircraft systems. Charts and tables display 14 years of incidents reported by pilots to the Aviation Safety Reporting System (ASRS). Affected systems, incident severity, sources of anomaly detection, and the most frequently identified PEDs are some of the more significant data. Several reports contain incidents of aircraft off course when all systems indicated on course and of critical events that occurred during landings and takeoffs. Additionally, PEDs that should receive priority in testing are identified.

Ross, Elden↗
NASA NTRSDate not supplied

Resilience, ASRS, and the Narrative about Human Error

We present a study of weather-related incident reports submitted to NASA’s Aviation Safety Reporting System (ASRS) by air carrier pilots in the US. Using specific examples, we examine the relevant aspects of human performance and resilience management exhibited during these incidents. We describe the common narrative about human error and how ASRS data can be used to change it.

resilience↗
NASA NTRS2002

A New Cure for Medical Errors

In May 2000, senior officials of the U.S. Department of Veterans Affairs (VA) and NASA signed an agreement that would commit the two agencies to create the Patient Safety Reporting System (PSRS) to report: events or situations that could have resulted in accident, injury, or illness, but did not, either by chance or through timely intervention (close-calls); unexpected serious occurrences that involved a patient or employee's death, physical injury, or psychological injury; lessens learned; and safety ideas. The VA provided NASA with funding for the initial development of the new system, which automatically removes all personal names, facility names and locations, and other potentially identifying information before entering reports into its database. Designed to complement the VA's current internal reporting systems, the PSRS is modeled after NASA's Aviation Safety Reporting System, which was established in 1975 under a Memorandum of Agreement between the Federal Aviation Administration and NASA and began operation in 1976.

Source record↗
NASA NTRS1993

Altitude deviations: Breakdowns of an error-tolerant system

Pilot reports of aviation incidents to the Aviation Safety Reporting System (ASRS) provide a window on the problems occurring in today's airline cockpits. The narratives of 10 pilot reports of errors made in the automation-assisted altitude-change task are used to illustrate some of the issues of pilots interacting with automatic systems. These narratives are then used to construct a description of the cockpit as an information processing system. The analysis concentrates on the error-tolerant properties of the system and on how breakdowns can occasionally occur. An error-tolerant system can detect and correct its internal processing errors. The cockpit system consists of two or three pilots supported by autoflight, flight-management, and alerting systems. These humans and machines have distributed access to clearance information and perform redundant processing of information. Errors can be detected as deviations from either expected behavior or as deviations from expected information. Breakdowns in this system can occur when the checking and cross-checking tasks that give the system its error-tolerant properties are not performed because of distractions or other task demands. Recommendations based on the analysis for improving the error tolerance of the cockpit system are given.

Palmer, Everett A.↗
NASA NTRSDate not supplied

Extracting Lessons of Resilience Using Machine Mining of the ASRS Database

NASA’s Aviation Safety Reporting System (ASRS) database is the world's largest repository of voluntary, confidential safety information provided by aviation's frontline personnel, including pilots, air traffic controllers, mechanics, flight attendants, dispatchers, and other members of the aviation community and the public. The database contains close to 2 million narratives, many of which describe everyday situations in which people saved the day. In these situations, people’s resilient behavior solved a problem, dealt with a malfunction, and maintained a safe operation despite a serious perturbation. To be able to extract lessons of such resilience from this large database, the use of machine learning algorithms is being explored. In this report, we describe a comparison between two such algorithms: Perilog and Word2Vec. An identical search using both programs was done on a database containing approximately 470,000 ASRS reports submitted between 1988 and 2022. The comparison reveals some of the strength and weaknesses of each algorithm as well as the challenges inherent in using such algorithms to extract lessons of resilience from the ASRS database.

resilience↗
NASA NTRS1998

Joint ASRS and NASA Callback on FANS-1 Datalink Operational Incidents

The recent certification and implementation of the Future Air Navigation System (FANS- 1) was based on the benefits of reduced separation minima, ability to optimize flight plans enroute, and the prompt transmission and reception of messages between oceanic controllers and 747-400 aircraft transiting the Pacific. In addition, FANS was intended to supersede High Frequency (HF) radio which has been the staple, yet problematic, long distance communication link for years. However, in the three initial years of operation, FANS has revealed its own unique operational issues. Although some technical and engineering problems have been studied and addressed, little research has been conducted on human factors issues associated with the use of FANS on the flight deck. This lack of prior data on the operational use of FANS prompted a joint NASA/Aviation Safety Reporting System (ASRS) inquiry into FANS datalink. In addition, two foreign air carriers and their respective safety agencies were recruited to participate. This international FANS research effort consisting of three aviation safety agencies and three international commercial air-carriers has provided a unique opportunity for conducting human factors research in an operational environment. An ASRS 'callback' format was chosen for the study as a practical and viable method for capturing FANS events on the flight deck. Initially, 747-400 pilots were encouraged to submit reports to their respective safety agencies if they had experienced any positive or negative incidents with the use of FANS. Upon receipt of a report, it was evaluated and if deemed pertinent to the study, a telephone interview or 'callback' was conducted on the FANS incident to elicit further details, capture the key events, and gather contextual information. Once the operational data collection phase was completed, the reports and interviews were analyzed with two purposes in mind: primarily to identify and address problematic human factors issues with current FANS technology, and, secondarily, to provide 'lessons learned' for future datalink systems and implementation.

Moses, John↗
NASA NTRS2013

Identification of Crew-Systems Interactions and Decision Related Trends

NASA Vehicle System Safety Technology (VSST) project management uses systems analysis to identify key issues and maintain a portfolio of research leading to potential solutions to its three identified technical challenges. Statistical data and published safety priority lists from academic, industry and other government agencies were reviewed and analyzed by NASA Aviation Safety Program (AvSP) systems analysis personnel to identify issues and future research needs related to one of VSST's technical challenges, Crew Decision Making (CDM). The data examined in the study were obtained from the National Transportation Safety Board (NTSB) Aviation Accident and Incident Data System, Federal Aviation Administration (FAA) Accident/Incident Data System and the NASA Aviation Safety Reporting System (ASRS). In addition, this report contains the results of a review of safety priority lists, information databases and other documented references pertaining to aviation crew systems issues and future research needs. The specific sources examined were: Commercial Aviation Safety Team (CAST) Safety Enhancements Reserved for Future Implementation (SERFIs), Flight Deck Automation Issues (FDAI) and NTSB Most Wanted List and Open Recommendations. Various automation issues taxonomies and priority lists pertaining to human factors, automation and flight design were combined to create a list of automation issues related to CDM.

Jones, Sharon Monica↗
NASA NTRSDate not supplied

Assessing the Use of UAS-Related Terms in ASRS Using Seed Topic Modeling

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential system that disseminates reports received from personnel involved in aviation operations after de-identifying them. These reports are used by the community to improve overall aviation system safety. Aim: We propose and execute an experiment to assess the use of seed term topic modeling over the database narratives to identify Unmanned Aircraft System (UAS) reports. The use of seed term topic modeling enables users to identify groups of conceptually similar narratives associated to a topic of their interest. Method: We use a collection of narratives, expert-selected words, and report metadata that separates UAS from non-UAS reports to assess if seed topic modeling can be used to improve ASRS searches. Results: For simpler queries, seed topic search observes a higher recall and lower precision than the existing DBOL (DataBase OnLine) search in operation. However, the best results are obtained when seed topic search is used as a search suggestion system to be executed on the DBOL. Conclusion: Utilizing a combination of both the existing method and the proposed method, users can expand their search vocabulary about subjects of interest while improving the quality of results.

Text Mining↗
NASA NTRSDate not supplied

Assessing the Use of UAS-Related Terms in ASRS using Seeds for Topic Modeling

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential system that disseminates reports received from personnel involved in aviation operations after de-identifying them. These reports are used by the community to improve overall aviation system safety. Aim: We propose and execute an experiment to assess the use of seed term topic modeling over the database narratives to identify Unmanned Aircraft System (UAS) reports. The use of seed term topic modeling enables users to identify groups of conceptually similar narratives associated to a topic of their interest. Method: We use a collection of narratives, expert-selected words, and report metadata that separates UAS from non-UAS reports to assess if seed topic modeling can be used to improve ASRS searches. Results: For simpler queries, seed topic search observes a higher recall and lower precision than the existing DBOL (DataBase OnLine) search in operation. However, the best results are obtained when seed topic search is used as a search suggestion system to be executed on the DBOL. Conclusion: Utilizing a combination of both the existing method and the proposed method, users can expand their search vocabulary about subjects of interest while improving the quality of results.

LDA↗
NASA NTRS2018

An Analysis of the Role of Safety Nets in the National Airspace System

Safe operations of aircraft in the National Airspace System (NAS) may be attributed to many factors, including the application of a variety of safety nets (SNs) as a last line of defense. In preparation for the Next Generation Air Transportation System (NextGen), a review of Aviation Safety Reporting System (ASRS) reports for incidents with positive outcomes was conducted to investigate the importance of current safety nets. The examination of positive outcomes not only shows what went wrong, but also what went right to prevent accidents and “save the day.” More than 400 incident reports for 2015 from the voluntary ASRS reporting database were studied in detail to create event sequence diagrams (ESDs), illustrating the effectiveness of SNs. The developed ESDs are considered top-level, representative models and are limited with respect to being reliably quantitative because they are based on only reports from a single year. The ESDs could offer insights into human systems integration research, such as strategically using technologies as SNs without human interface or alleviating human workload with new technologies to provide resilient recovery from off-nominal conditions ensuring flight safety.

Geuther, Steven↗
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 NTRS1989

Fatigue, pilot deviations and time of day

The relationships between pilot fatigue, pilot deviations, reported incidents, and time of day are examined. A sample of 200 Aviation Safety Reporting System (ASRS) reports were analyzed from 1985 and 200 reports from 1987, plus 100 reports from late 1987 and early 1988 that were selected because of possible association with fatigue. The FAA pilot deviation data and incident data were analyzed in relation to denominator data that summarized the hourly operations (landings and takeoffs of scheduled flights) at major U.S. airports. Using as numerators FAA data on pilot deviations and incidents reported to the FAA, the rates by time of day were calculated. Pilot age was also analyzed in relation to the time of day, phase of flight, and type of incident.

Baker, Susan P.↗