Classification and Analysis of Errors Reported in Aircraft Maintenance Manuals

Previous research has identified maintenance information as one of the primary causal factors of maintenance error. Incorrect maintenance information has also been cited as a contributing factor in a number of recent aircraft mishaps. To date no one has studied the types of errors found in aircraft maintenance manuals published by manufacturers. The purpose of this research is to analyze Publication Change Requests (PCRs) to document the most frequently reported types of errors found in aircraft maintenance manual, to identify how errors vary across Air Transport Association (ATA) chapters, and identify the corrective actions required to address the cited problem. The most common request was for additional procedural information followed by requests to add or change the language to improve clarity. The results show that the majority of PCRs (42%) cited procedures found in Chapters 27 (Flight controls), 32 (Landing gear), and 71 (Powerplant)

A COMPREHENSIVE HUMAN FACTORS ANALYSIS OF OFF-DUTY MOTOR VEHICLE CRASHES IN THE UNITED STATES MILITARY

Researchers have always had great interest in traffic safety and the phenomenon of motor vehicle crashes (MVCs). Though scores of service members are severely injured or killed in off-duty MVCs each year, few studies have addressed the MVC phenomenon within the military population and none have conducted a comprehensive evaluation of the causal factors associated with MVCs involving military personnel. The main purpose of this dissertation was to gain a greater understanding of the causal factors associated with serious and fatal off-duty personal MVCs for military service members with the ultimate goal of preventing future losses. The HFACS-MVC framework was developed based on the established human error framework HFACS and used to classify causal factors from archival narratives from Class A and B off-duty MVCs in the USAF, USN, and USMC. This study identified the human factors trends associated with off-duty military MVCs and compared main trends for four variables of interest, specifically for military branch, vehicle type, paygrade, and age group. The main human factor trends associated with off-duty MVCs were skill based technique errors related to negotiating curves/turns and regaining road positions and procedural violations related to speeding and drunk driving. Significant differences were found between human factors trends associated with MVCs for both vehicle type and military branch. For vehicle type, the human factors trends for 4W MVCs were significantly different from those for 2W MVCs, especially at the preconditions level. However, for military branch, the human factors trends suggest differences in the investigation and reporting processes for the three branches

Data Mining in Promoting Flight Safety

The incredible rapid development to huge volumes of air travel, mainly because of jet airliners that appeared to the sky in the 1950s, created the need for systematic research for aviation safety and collecting data about air traffic. The structured data can be analysed easily using queries from databases and running theseresults through graphic tools. However, in analysing narratives that often give more accurate information about the case, mining tools are needed. The analysis of textual data with computers has not been possible until data mining tools have been developed. Their use, at least among aviation, is still at a moderate level. The research aims at discovering lethal trends in the flight safety reports. The narratives of 1,200 flight safety reports from years 1994 – 1996 in Finnish were processed with three text mining tools. One of them was totally language independent, the other had a specific configuration for Finnish and the third originally created for English, but encouraging results had been achieved with Spanish and that is why a Finnish test was undertaken, too. The global rate of accidents is stabilising and the situation can now be regarded as satisfactory, but because of the growth in air traffic, the absolute number of fatal accidents per year might increase, if the flight safety will not be improved. The collection of data and reporting systems have reached their top level. The focal point in increasing the flight safety is analysis. The air traffic has generally been forecasted to grow 5 – 6 per cent annually over the next two decades. During this period, the global air travel will probably double also with relatively conservative expectations of economic growth. This development makes the airline management confront growing pressure due to increasing competition, signify cant rise in fuel prices and the need to reduce the incident rate due to expected growth in air traffic volumes. All this emphasises the urgent need for new tools and methods. All systems provided encouraging results, as well as proved challenges still to be won. Flight safety can be improved through the development and utilisation of sophisticated analysis tools and methods, like data mining, using its results supporting the decision process of the executives.Lentoliikenne kasvoi huomattavasti 1950-luvulla pääasiassa suihkumatkustajakoneiden myötä, mikä aiheutti poikkeamatietojen järjestelmällisen keräämisen ja tutkimuksen tarpeen. Määrämuotoinen tieto voidaan helposti analysoida tietokantakyselyillä esittäen tulokset käyttäen graafisia työkaluja, mutta tekstianalyysiin, jonka avulla tapauksista saadaan usein tarkempia tietoja, tarvitaan louhintatyökaluja. Tekstimuotoisen tiedon automaattinen analysointi ei ole ollut mahdollista ennen louhintatyökalujen kehittämistä. Silti niiden käyttö, ainakin ilmailun piirissä, on edelleen vähäistä. Tutkimuksen tarkoituksena oli havaita vaarallisia kehityskulkuja lentoturvallisuusraporteissa. 1 200 lentoturvallisuusraportin selostusosiot vuosilta 1994 –1996 käsiteltiin kolmella tekstinlouhintatyökalulla. Yksi näistä oli täysin kieliriippumaton, toisessa oli lisäosa, jossa oli mahdollisuus käsitellä suomen kieltä ja kolmas oli rakennettu alun perin ainoastaan englanninkielisen tekstin louhintaan, mutta espanjan kielellä saavutettujen rohkaisevien tulosten pohjalta päätettiin kokeilla myös suomenkielistä tekstiä. Lento-onnettomuuksien määrä liikenteeseen nähden on vakiintumassa maailmanlaajuisesti katsottuna ja turvallisuustaso voidaan katsoa tyydyttäväksi. Kuitenkin liikenteen kasvaessa myös onnettomuuksien määrä lisääntyy vuosittain, mikäli lentoturvallisuutta ei kyetä parantamaan. Turvallisuustiedon kerääminen ja raportointijärjestelmät ovat jo saavuttaneet huippunsa. Analysoinnin parantaminen on avain lentoturvallisuuden parantamiseen. Lentoliikenteen on ennustettu kasvavan 5 – 6 prosenttia vuodessa seuraavien kahden vuosikymmenen ajan. Samana aikana lentoliikenne saattaa kaksinkertaistua jopa vaatimattomimpien talouskasvuennusteiden mukaan. Tällainen kehitys asettaa lentoliikenteen päättäjille yhä kasvavia paineita kiristyvän kilpailun, polttoaineiden hinnannousun ja liikenteen kasvun aiheuttaman onnettomuuksien määrän vähentämiseksi. Tämä korostaa uusien menetelmien ja työkalujen kiireellistä tarvetta. Kaikilla louhintajärjestelmillä saatiin rohkaisevia tuloksia mutta ne nostivat samalla esille haasteita, jotka tulisi vielä voittaa. Lentoturvallisuutta voidaan vielä parantaa käyttämällä tässä esille tuotuja analyysimenetelmiä ja –työkaluja kuten tiedonlouhintaa ja soveltamalla näin saatuja tuloksia johdon päätöksenteon tukena.Siirretty Doriast

A Study Of Factors Contributing To Self-reported Anomalies In Civil Aviation

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features

Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequence of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art method

Providing Metrics-Based Results To Student Pilots For Critical Phases Of General Aviation Flights

This work details the development of the Critical Phase Analysis Tool (CPAT), a tool for analyzing and grading the quality of approach and landing phases of flight for the National General Aviation Flight Information Database (NGAFID). General Aviation (GA) accounts for the highest accident rates in Civil Aviation, and the approach and landing phases are when a majority of these accidents occur. Since GA aircraft typically lack most of the sophisticated technology that exists within Commercial Aviation, detecting phases of flight can be difficult. Moreover, because of the high variability in GA operations and abilities of the pilot, detecting unsafe flight practices is also not trivial. This thesis details the usefulness of an event-driven approach in analyzing the quality and risk level of an approach and landing. In particular, the application uses several parameters from a flight data recorder (FDR) to detect the phases of flight, detect any safety exceedances during the phases, and assign a metrics-based grade based on the accrued number of risk levels. The goal of this work is to improve the post-flight debriefing process for student pilots and Certified Flight Instructors (CFI) by augmenting the currently limited feedback with metrics and visualizations. By improving the feedback available to students, it is believed that it will help to correct unsafe flying habits quicker, which will also help reduce the GA accident rates in the long-term. The data was collected from a Garmin G1000 FDR glass cockpit display on a Cessna C172 fleet. The developed application is able to successfully detect go-arounds, touch-and-goes, and full-stop landings as either stable or unstable with an accuracy of 98.16%. The CPAT can be used to provide post-flight statistics and user-friendly graphs for educational purposes. It is capable of assisting both new and experienced pilots for the safety of themselves, their organization, and GA as a whole

Civil and Military Airworthiness

Effective safety management has always been a key objective for the broader airworthiness sector. This book is focused on safety themes with implications on airworthiness management. It offers a diverse set of analyses on aircraft maintenance accidents, empirical and systematic investigations on important continuing airworthiness matters and research studies on methodologies for the risk and safety assessment in continuing and initial airworthiness. Overall, this collection of research and review papers is a valuable addition to the published literature, useful for the community of aviation professionals and researchers

Human Error in Commercial Fishing Vessel Accidents: An Investigation Using the Human Factors Analysis and Classification System

The commercial fishing industry is frequently described as one of the most hazardous occupations in the United States. The objective, to maximize the catch, is routinely challenged by a variety of elements due to the environment, the vessel, the crew, and several external considerations and how they interact with each other. The analysis of fishing vessel accidents can be complicated due to the diverse nature of the industry, including the species caught, the type and size of boat that is employed, how far travelled from their homeport, and the adequacy of the support organizations ensuring safe and uninterrupted operations. This study will develop and evaluate a version of Wiegmann and Shappell’s (2003) Human Factors Analysis and Classification System (HFACS), specifically for commercial fishing industry vessels (HFACS-FV), using ten years of data documenting the causes of fatal accidents in the commercial fishing industry. For this study, the accident investigation information will be converted into the HFACS-FV format by independent raters and measured for inter-rater reliability. The results will be analyzed for the frequency of the causal factors identified by the raters, and causal factors will also be evaluated for their relationship with vessel demographic information. Based on the results, the conclusion of the study will determine the efficacy of the HFACS-FV model