Browse Topic: Flight recorders

Items (109)
The hippocampus plays a crucial role in brain function and is one of the important areas of concern in closed head injury. Hippocampal injury is related to a variety of factors including the strength of mechanical load, animal age, and helmet material. To investigate the order of these factors on hippocampal injury, a three-factor, three-level experimental protocol was established using the L(3) orthogonal table. A closed head injury experiment regarding impact strength (0.3MPa, 0.5MPa, 0.7MPa), rat age (eight- week-old, ten-week-old, twelve-week-old), and helmet material (steel, plastic, rubber) were achieved by striking the rat's head with a pneumatic-driven impactor. The number of hippocampal CA3 cells was used as an evaluation indicator. The contribution of factors to the indicators and the confidence level were obtained by analysis of variance. The results showed that impact strength was the main factor affecting hippocampal injury (contribution of 89.2%, confidence level 0.01
Wang, PengSong, XueweiZhu, XiyanQiu, JinlongYang, ShuaijunZhao, Hui
In the aerospace industry, competition is high and the need to ensure safety and security while managing costs is paramount. Furthermore, stakeholders—who gain the most by working together—do not necessarily trust each other. Now, mix that with changing enterprise technologies, management of historical records, and customized legacy systems. This issue touches all aspects of the aerospace industry, from frequent flyer miles to aircraft maintenance and drives tremendous inefficiency and cost.Technology that augments, rather than replaces, is needed to transform these complex systems into efficient, digital processes. Blockchain technology offers collaborative opportunities for solving some of the data problems that have long challenged the industry.This SAE EDGE™ Research Report by Rhonda D. Walthall examines how blockchain technology could impact the aerospace industry and addresses some of the unsettled concerns surrounding its implementation.{"uri":[{"xlink:href":"https://www.sae.org
Walthall, Rhonda
Spatial Disorientation (SD) mishaps account for the greatest loss of lives in both military and civilian aviation worldwide. When no mechanical cause of a mishap is identified, mishap investigators can use flight data recorder information to populate perceptual models with aircraft flight parameters in order to confirm or deny that pilot SD was the probable cause of the mishap. Current perceptual model weaknesses include the inability to analyze hover and hover-transition mishaps and not accounting for sensory inputs from the auditory and somatosensory systems. The authors have conducted in-flight helicopter perceptual threshold studies to extend the model envelop to include hover as well as a series of tactile cueing in-flight studies in fixed-wing aircraft to permit the inclusion of somatosensory information into the model. This expanded model, by including all sensory modalities, now provides a probable solution to prevention of SD mishaps by continuously maintaining spatial
Rupert, AngusBrill, J.McGrath, BradenMortimer, Bruce
Rotorcrafts are generally subject to a higher fatal accident rate than other segments of aviation, including commercial and general aviation. The safety improvement for rotorcrafts would directly improve the efficiency of air traffic control, since rotorcrafts operate primarily within low-level airspace; an area that is becoming increasingly complex with new entrants, such as unmanned aircraft systems and urban air mobility. The recent impact of artificial intelligence and deep learning algorithms on various aspects of our lives has led to the investigation of the application of these algorithms in the aviation domain; as it may offer a prime opportunity to enhance safety within the aviation community. In this research, we explore the efficacy, reliability, and, more importantly, the explainability of modern deep learning algorithms. We use machine learning models to predict the attitude (pitch and yaw) of rotorcrafts using video data recorded with ordinary cameras. The cameras were
Khan, HikmatJohnson, CharlesBouaynaya, NidhalRasool, GhulamTravis, TylerThompson, Lacey
This SAE Aerospace Recommended Practice (ARP) provides recommendations for design and test requirements for a generic “passive” side stick that could be used for fly-by wire transport and business aircraft. It addresses the following: The functions to be implemented The geometric and mechanical characteristics The mechanical and electrical interfaces The safety and certification requirements
A-6A3 Flight Control and Vehicle Management Systems Cmt
Over the last 100 years, the automobile has become integrated in a fundamental way into the broader economy. A broad and deep ecosystem has emerged, and critical components of this ecosystem include insurance, after-market services, automobile retail sales, automobile lending, energy suppliers (e.g., gas stations), medical services, advertising, lawyers, banking, public planners, and law enforcement. These components - which together represent almost $2 trillion of the U.S. economy - are in equilibrium based on the current capabilities of automotive technology. However, the advent of autonomous vehicles (AVs) and technologies like electrification have the potential to significantly disrupt the automotive ecosystem. The critical cog governing the rate and pace of this shift is the management of the test and verification of AVs. In this SAE EDGE™ report, six senior industry leaders in the impacted ecosystems essay articles which describe sectors of the current automotive ecosystem and
Razdan, Rahul
The CH146 Griffon helicopter is a Federal Aviation Administration (FAA) and Transport Canada certified commercial helicopter used by Canadian Armed Forces in military role, which is distinguished from the original design intent of the helicopter for commercial use. A regime based Structural Usage Monitoring (SUM) program is developed by Bell to meet the Canadian Government requirements documented in the Technical Airworthiness Manual (TAM). As part of the CH146 SUM, helicopter usage data recorded by the Flight Data Recorder (FDR) system is collected and flight conditions (regimes) are defined by using dedicated software tools. Helicopter usage is then compared with the baseline and by using the actual helicopter data on selected critical components and fatigue damage accumulation is performed. Finally, the calculated damage is evaluated to define recommended structural maintenance actions. Development of the CH146 SUM is completed and more than 50,000 flight hours accumulated FDR data
Turkdogan, AdemOuellet, MarcBernier, Simon
As the premier agency for promoting and insuring aviation safety, the Federal Aviation Administration (FAA) continues to promote and highlight the importance of participating in aviation Flight Data Monitoring (FDM) programs to improve flight safety and operational efficiency. Indeed, recorder safety is one of the agency's top 10 most wanted list of safety improvements in 2017-2018. The FAA, National Transportation Safety Board (NTSB), and the United States Helicopter Safety Team (USHST) are strong proponents of recorder use. These organizations and other industry partners are working together to implement a helicopter safety enhancement that promotes the use of flight data recorders as a mechanism to reduce the helicopter fatal accident rate. However, despite these best efforts to reduce the fatal accident rate with this lifesaving technology, barriers to implementation exist. These include initial costs of flight data recorders which can range from 9,000 - 50,000, on average. These
Khan, HikmatJohnson, CharlesRasool, GhulamBouaynaya, Nidhal
This paper presents the results from several load estimation methods developed at the National Research Council Canada (NRC) which enable the estimation of helicopter loads and tracking load exceedances and fatigue damage for a targeted component using computational intelligence techniques. The approach relies only on flight state and control system (FSCS) parameters, such as those recorded by a flight data recorder (FDR), and can also be applied to legacy aircraft or to those aircraft not equipped with HUMS. The methodologies adapt to the input data available so are not constrained to one particular system or platform, and enable the estimation of loads through the duration of a manoeuvre instead of assuming a constant load for an entire manoeuvre. So far, the three methods have been tested on data obtained from two different helicopter platforms, the S-70A-9 Australian Black Hawk and the CH-146 Griffon (Bell 412). Significant improvements are made over previous results presented for
Cheung, CatherineRocha, BrunoValdés, JulioPuthuparampil, Jobin
ABSTRACT The US Army Condition Based Maintenance program collects data from Health and Usage Monitoring Systems, Flight Data Recorders, Maintenance Records, and Reliability Databases. These data sources are not integrated, but decisions regarding the health of aircraft components are dependent upon the information stored within them. The Army has begun an effort to bring these data sources together using Machine Learning algorithms. Two prototypes will be built using decision-making machines: one for an engine output gearbox and another for a turbo-shaft engine. This paper will discuss the development of these prototypes and provide the path forward for implementation. The importance of determining applicable error penalty methods for machine learning algorithms for aerospace applications is explored. The foundations on which the applicable dataset is built are also explored, showing the importance of cleaning disparate datasets. The assumptions necessary to generate the dataset for
Wade, DanielLugos, RamonAntolick, LanceAlbarado, KevinVongpaseuth, ThongsayAyscue, JefferyWilson, AndrewBrower, NathanKrick, StevenSzelistowski, Matthew
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