Hull loss accident model for narrow body commercial aircraft

Accidents with narrow body aircraft were statistically evaluated covering six families of commercial aircraft includingBoeing B737, Airbus A320, McDonnell Douglas MD80, Tupolev TU134/TU154 and Antonov AN124. A risk indicator for eachflight phase was developed based on motion characteristics, duration time, and the presence of adverse weather conditions.The estimated risk levels based on these risk indicators then developed from the risk indicator. Regression analysis indicatedvery good agreement between the estimated risk level and the accident ratio of hull loss cases per number of delivered aircraft.The effect of time on the hull loss accident ratio per delivered aircraft was assessed for B737, A320 and MD80. Equationsrepresenting the effect of time on hull loss accident ratio per delivered aircraft were proposed for B737, A320, and MD80,while average values of hull loss accident ratio per delivered aircraft were found for TU134, TU154, and AN 124. Accidentprobability equations were then developed for each family of aircraft that the probability of an aircraft in a hull loss accidentcould be estimated for any aircraft family, flight phase, presence of adverse weather factor, hour of day, day of week, monthof year, pilot age, and pilot flight hour experience. A simplified relationship between estimated hull loss accident probabilityand unsafe acts by human was proposed. Numerical investigation of the relationship between unsafe acts by human andfatality ratio suggested that the fatality ratio in hull loss accident was dominated primarily by the flight phase media

Exploring Raw Safety Aspects in Aviation Industry

Aviation is the growing industry. Safety in the aviation industry is most important. Safety is affected by many factors such as environmental, economical, technical, and operational and many challenges are in the way of aviation safety to overcome from these hurdles .So this paper tried to explore the different safety aspects for the aviation industry. From the literature different research streams and research issues are discussed which affects the safety of the aviation industry. Keywords: Aviation Safety, Challenges, Safety Aspects, Environmental, Economical, Technica

Classifıcation of Survivor/Non-Survivor Passengers in Fatal Aviation Accidents: A Machine Learning Approach

The safety concept primarily examines the most fatal (resulting in dead passengers) accidents of aviation history in this study. The primary causes of most fatal accidents are; human, technical, and sabotage/terrorism factors. Although the aviation industry started with the first engine flight in 1903, the safety concept has been examined since the 1950s. The safety concept firstly examined the technical factors, and in the late 1970s, human factors started to analyze. Despite these primary causes, there have different factors that affect accidents. So, the study aims to determine the affecting factors of the most fatal accidents to classify the survivor/non-survivor passenger numbers. Logistic regression and discriminant analysis are used as multivariate statistical analyses to compare with the machine learning approaches showing the algorithms’ robustness. In this study, machine learning techniques have better performance than multivariate statistical methods in terms of accuracy, false-positive rate, and false-negative rate. In conclusion, the phase of flight, the primary cause, and total passenger numbers are determined as the most affected factors in machine learning and multivariate statistical models for classifying the accidents’ survivor/non-survivor passenger numbers. Keywords: Machine learning; primary causes; fatal aviation accidents; classification of survivor/non-survivor passengers; multivariate statistical analysis

INTEGRATED RISK ASSESSMENT IN RAMP HANDLING OPERATIONS: RISK MAPPING FOR TURKISH AIRPORTS

Ramp handling acts a vital role in sustainable airport operations. The ramp related services provided to aircraft and their passengers at the airports are related to the landing, take-off, unloading and loading of the aircraft. Human resource is a key component in ramp handling operation and errors by employees cause significant accidents or incidents. The main purpose of the current study is to prioritize critical risk factors in ramp handling operations by using an integrated risk management approach and optimizing human performance while minimizing both failures and errors by employees. In this study, an integrated qualitative and quantitative risk assessment method is carried out by considering the factors that affect the possibility of ramp handling personnel errors. Initially, 113 risk factors are identified by using the academic literature, documents prepared by international organizations, and then by consulting expert opinions. Subsequently, a prioritization by professionals working on the ramp handling operations, based on the principles of the Analytic Hierarchy Process (AHP) method resulted in the final selection of the 41 most important risks. Then, a risk assessment approach is applied by designing a matrix, based on three dimensions; probability, severity (impact) and relation ratio which ultimately resulted in risk index generation and a risk map model is developed. Finally eleven (11) risk factors are identified as they have higher probabilities to occur and possible higher negative consequences. Thanks to the integrated risk assessment applied in this study, it is aimed to ensure that all systems of the organization operate in a safe way and that an efficient safety culture is formed. Allocating a single resource to many risks, instead of facing the risks of the ramp personnel one by one, leads to more efficient use of resources and higher performance of ground handling companies

Agricultural Worker Injury Comparative Risk Assessment Methodology: Assessing Corn and Biofuel Switchgrass Production Systems

Keeping workers safe is a continuing challenge in agricultural production. Risk assessment methodologies have been used widely in other industries to better understand systems and enhance decision making, yet their use in production agriculture has been limited. This article describes the considerations and the approach taken to measure the difference in worker injury risks between two agricultural production systems. A model was developed specifically for the comparison of worker injury risk between corn and biofuel switchgrass production systems. The model is composed of injury and exposure values that were used in a Monte Carlo simulation. The output of this risk assessment shows that approximately 99% of the values from the Monte Carlo simulation rank corn production as a greater worker injury risk than biofuel switchgrass production. Furthermore, the greatest contributing factors for each production system were identified as harvest, and that finding aligns with current literature

The probability of dying in a plane crash or having a safe flight

In the article, an overall study involving a plane crash and the likelihood of having a safe flight is presented. The probability of dying in a plane crash and the probability of getting to the destination safely were calculated within the period 1970–2008. The formula for calculating the average length of a sequence of consecutive safe flights was determined. The probability of dying in a car accident and the probability of dying as a result of a plane crash were compared as a conclusion

A risk assessment model to measure the difference in worker injury risk between corn and biofuel switchgrass production systems

Keeping workers safe presents a continuing challenge in the agricultural industry. Risk assessment methodologies have been used widely to better understand systems and enhance decision making with a goal of reducing injuries and fatalities. This research applies probabilistic risk assessment to human safety in two agricultural production systems, taking into account uncertainties such as equipment variation, working schedules, and weather conditions. A comparative model was developed because it can be scaled up or down based on available data and allow inputs from categories defined broadly or specifically as necessary. In this model, risk is calculated by multiplying the probability of exposure to a hazard and the probability of injury, given that an exposure to the hazard has occurred. The probability of injury and exposure values are derived from the USDA Census and from the Survey and Bureau of Labor Statistics data from 12 states in the Midwest for each year from 1996 to 2011. The exposure and injury data were used to build probability distributions that were randomly sampled using a Monte Carlo simulation. The output of the simulation demonstrates that corn has a higher risk of worker injury than biofuel switchgrass over a ten year period in the Midwest. A Monte Carlo simulation and a sensitivity analysis were run to determine the greatest contributing factors to worker injury risk within each production system. Harvest operations in both corn and biofuel switchgrass production systems were determined to be the greatest contributing factor to worker injury risk

THE EFFECTS OF WEATHER RECOGNITION TRAINING ON GENERAL AVIATION PILOT SITUATION ASSESSMENT AND TACTICAL DECISION MAKING WHEN CONFRONTED WITH ADVERSE WEATHER CONDITIONS

Previous general aviation (GA) accident studies showed that decision errors were more associated with fatal GA accidents than other kinds of human errors, and weather related accidents, especially continued visual flight rules (VFR) flight into instrument meteorological conditions (IMC), remained the major cause of fatal GA accidents. Thus, finding the underlying causes of GA pilots\u27 decision errors and continued VFR flight into adverse weather conditions are needed to reduce weather related GA accidents as well as fatal GA accidents. Causal factors and hypotheses of weather related GA accidents show that knowledge, experience, motivation, and weather information frequently have been referred as causal factors of weather-related GA accidents. Among causal hypotheses, situation assessment and risk assessment hypotheses have been cited frequently as the causes of weather related GA accidents. The purpose of this study is to evaluate the effects of weather recognition training on GA pilots\u27 situation assessment and tactical decision making under gradually aggravating weather conditions. To meet this purpose, WeatherWise and an X-Plane 9 flight simulation program has been used. WeatherWise is a computer based weather training program developed by Wiggins et al. (2000) to improve GA pilot weather-related decision making, and was approved by the Federal Aviation Administration (FAA) for free public use. Pilot situation assessment is a pilot\u27s understanding of a current flight state, and was evaluated in terms of weather assessment and risk assessment. Weather assessment is the pilot\u27s ability to recognize or estimate the changes in visibility, ceiling, and weather condition. Risk assessment is the understanding of the risks associated with flying in adverse weather conditions, and was measured in terms of risk perception and risk tolerance using the Hazardous Event Scale, personal weather minimums, and the Aviation Safety Attitude Scale. Pilot situation assessment was measured by a post experiment questionnaire. Pilot tactical decision making is in flight judgment, and was evaluated in terms of decision accuracy and decision confidence. Decision accuracy was evaluated by measuring the distance that a pilot has flown from an optimal divert point to an actual divert point, and the distance a pilot has flown into adverse weather conditions. Decision confidence is the pilot\u27s confidence level in making diverting decisions when the pilot encounters adverse weather, and was measured by subjective rating method. Findings of the study indicated that the WeatherWise training group exhibited significantly higher weather assessment as measured by ceiling estimation ability and decision accuracy as measured by flown distance into adverse weather condition than the control group, but no significant differences were found in their risk assessment and decision confidence. Although the effects of weather training on the risk assessment were not significantly different between the two groups, participants in the WeatherWise training group was more conservative toward flying into adverse weather condition than the control group. It was hypothesized to find a positive relationship between pilots\u27 situation assessments and their tactical decision-making because situation assessment forms a basis for decision making; however, positive relationship was found only between pilots\u27 ceiling estimation and flown distances into adverse weather in this study. Thus, it can be concluded that the weather training was effective at least in part to pilot situation assessment and tactical decision making. In addition, considering the weather training was just one-time 30 minute training, long-term effects of weather training should be conducted to find further relationship between pilot situation assessment and tactical decision making. The results of this study can be expanded not only to GA pilots but also to commercial airline pilots and military pilots for various reasons. First, all pilots are expected to acquire weather recognition skills and knowledge to ensure a safe flight regardless of their flight types because the nature of weather condition changes is dynamic and hard to predict during the flight. Second, although those aircrafts are well equipped with navigation aid systems and weather display radar, they do not provide real–time weather information, and they sometimes malfunction. In conclusion, it is expected that this study will be helpful for GA pilots to understand the effects of weather recognition training on weather decision making, and eventually help them assess a situation correctly and make a timely in–flight decision. It is believed that this study will help to establish a sound foundation for weather training program and has the potential to reduce weather-related GA accidents by implementing weather training during flight training

System level airborne avionics prognostics for maintenance, repair and overhaul

The aim of this study is to propose an alternative approach in prognostics for airborne avionics system in order to enhance maintenance process and aircraft availability. The objectives are to analyse the dependency of avionic systems for fault propagation behaviour degradation, research and develop methods to predict the remaining useful life of avionics Line Replaceable Units (LRU), research and develop methods to evaluate and predict the degradation performances of avionic systems, and lastly to develop software simulation systems to evaluate methods developed. One of the many stakeholders in the aircraft lifecycle includes the Maintenance, Repair and Overhaul (MRO) industry. The predictable logistics process to some degree as an outcome of IVHM gives benefit to the MRO industry. In this thesis, a new integrated numerical methodology called ‘System Level Airborne Avionic Prognostics’ or SLAAP is developed; looking at a top level solution in prognostics. Overall, this research consists of two main elements. One is to thoroughly understand and analyse data that could be utilised. Secondly, is to apply the developed methodology using the enhanced prognostic methodology. Readily available fault tree data is used to analyse the dependencies of each component within the LRUs, and performance were simulated using the linear Markov Model to estimate the time to failure. A hybrid approach prognostics model is then integrated with the prognostics measures that include environmental factors that contribute to the failure of a system, such as temperature. This research attempts to use data that is closest to the data available in the maintenance repair and overhaul industry. Based on a case study on Enhanced Ground Proximity Warning System (EGPWS), the prognostics methodology developed showed a sufficiently close approximation to the Mean Time Before Failure (MTBF) data supplied by the Original Equipment Manufacturer (OEM). This validation gives confidence that the proposed methodology will achieve its objectives and it should be further developed for use in the systems design process