Browse Topic: Flight recorders
With the advancement of automotive industries, the need for wireless connectivity between vehicle and smartphone is increasing. To meet the demand for wireless connectivity, Bluetooth plays a vital role. Testing Bluetooth systems is challenging and complex when development cycles of the system involve multiple partners. The system under test must fulfil consumers expectation of Bluetooth functionality paired with their personal devices. Despite many advances and existence of a few reliable systems, hardware limitation, and lack of standardization in Bluetooth test system are some of the prolonged issues. Throughout the course, various capabilities and existing traditional Bluetooth testing system practice were researched, which majorly at a system level (Black box). The gap of such testing is the escape of defect which involves the interoperability of multiple profiles like AVRCP, HFP, and A2DP. This paper focuses on a reliable testing approach which is based on packet level testing
Machine learning is used for the research and development of ITS services and the rider assistance for on-road motorcycle racing. Meanwhile, rider assistance systems for off-road motorcycles have yet to be developed, partly due to the complexity of the measurement conditions, as described in the previous paper. This research aims to create a reliable AI which is capable of classifying typical jump behaviors in off-road riding by machine learning to create a rider assistance system for off-road motorcycles. Motorcycle manufacturers and certain research institutes use motion sensors to collect data, but the data is obtained from a limited number of vehicles and riders. The creation of a rider assistance system requires a large amount of validation data. Furthermore, it is desirable to achieve the target with data that can be measured in mass-produced vehicles, which will make it possible to collect data even from general users. In addition, recent machine learning models are black boxes
ABSTRACT 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
ABSTRACT 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
ABSTRACT 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
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
This AS covers ULD utilized in finding flight data recorders, cockpit voice recorders or aircraft. Such ULDs are installed adjacent to the recorders in a manner that they are unlikely to become separated during crash conditions.
This standard covers three (3) basic types of flight recorders as defined below: All requirements specified in Sections 3, 4, 5, 6 and 7 of this standard shall be applicable to all recorder types unless otherwise noted.
Panoramic detection systems (PDSs) are developmental video monitoring and image-data processing systems that, as their name indicates, acquire panoramic views. More specifically, a PDS acquires images from an approximately cylindrical field of view that surrounds an observation platform. In example of a major class of intended applications, a PDS mounted on top of a motor vehicle could be used to obtain unobstructed views of the surroundings (see Figure 1). In another such example, a PDS could be mounted above a roadway intersection for monitoring approaching and receding vehicles in order to provide image data on the vehicles as input to an automated traffic-control system. In either application, a running archive of the image data acquired by the PDS could be maintained as a means of reconstructing the events leading up to a vehicular collision: used in this way, a PDS would be analogous to an aircraft "black box" data recorder.
A computational method and software to implement the method have been developed to sift through vast quantities of digital flight data to alert human analysts to aircraft flights that are statistically atypical in ways that signify that safety may be adversely affected. On a typical day, there are tens of thousands of flights in the United States and several times that number throughout the world. Depending on the specific aircraft design, the volume of data collected by sensors and flight recorders can range from a few dozen to several thousand parameters per second during a flight. Whereas these data have long been utilized in investigating crashes, the present method is oriented toward helping to prevent crashes by enabling routine monitoring of flight operations to identify portions of flights that may be of interest with respect to safety issues.
A spacecraft may be unable to communicate critical data associated with a serious or catastrophic failure. A brief report proposes a system, somewhat like a commercial aircraft "black box," for retrieving these data. A microspacecraft attached to the prime spacecraft would continually store recent critical data from that spacecraft. If either spacecraft detected certain serious conditions of the prime spacecraft, the microspacecraft would separate from the prime spacecraft and independently transmit the stored data to Earth. Supplemental data, acquired from sensors onboard the microspacecraft, could be added to this transmission. For example, the orientation and angular rates of the prime spacecraft immediately before separation as well as pictures taken of the prime spacecraft after separation could be included. Functional enhancements over aircraft black boxes include the separation from the prime vehicle (which gains independence from the fate of that vehicle), wireless transmission
This AS covers Underwater Locating Devices (ULD) to assist in finding flight recorders, cockpit recorders or aircraft or both. Such ULDs are to be installed adjacent to the recorders in a manner that they are unlikely to become separated during crash conditions.
This paper describes a forward looking on-board vehicle detection and driver alert system that provides a distance indication and alert tone to the driver. The system, called VORAD (Vehicular On-board Radar), is the first of its kind to be fielded. The system can be programmed to function in different operating modes, allowing customization to the user's requirements. Some possible operating modes include simply providing an alert to the driver, providing following distance indication measured in seconds based on the vehicles' speeds, or providing following distance measured in feet. The VORAD System also has optional features to enhance its usefulness, including a blind spot alert system and a built-in event recorder. The blind spot system provides additional information to the driver regarding the presence of vehicles in his blind spot for use in making lane changes. The event recorder can act as a “flight recorder” for accident reconstruction, safety training and fleet management
This standard covers three (3) basic types of flight recorders as defined below: All requirements specified in sections 3, 4, 5, 6 & 7 of this standard shall be applicable to all recorder types unless otherwise noted.
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