Browse Topic: Commercial aircraft
As per Committee/Henry E. Harschburger recommendations
The Primary Author has been involved in Army Aviation Development and Acquisition since the Utility Tactical Transport Aircraft System (UTTAS), Advanced Attack Helicopter (AAH), Army Helicopter Improvement Program (AHIP), and Light Helicopter Experimental (LHX) Programs in the mid-1970s to the mid-1980s. The first three of these programs successfully made it to production aircraft, while the LHX became the RAH-66 Comanche and was canceled primarily due to technical problems and cost overruns. The initiation of the next phase by the Army Aviation Development (ADD) Directorate for Future Vertical Lift (FVL) did not occur until the beginning of the 2015-2000 timeframe. This was 35 years since the last Army Aviation Development in 1980. To help sustain this FVL development, the Primary Author led, oversaw, and helped conduct a program through the National Rotorcraft Technology Center (NRTC) in the 2015-2016 timeframe. It was called the Development Assurance Value-Based Acquisition (DAVBA) Program1. It included the following team members: Georgia Tech, University of Alabama Huntsville (UAH), Dassault Systèmes, and Clausewitz Technology. The Army ADD plan funded it for FY2015- 2016 through the NRTC. The objectives were to provide the Future Vertical Lift (FVL) Program with a Development Assurance for Airworthiness Qualification and a Value-Based Acquisition Overall Evaluation Criterion (OEC) for FARA and FLRAA concepts.. However, Army Aviation only funded the first phase in 2015, as FVL funds were then transferred to the new Army Futures Command. This paper will illustrate how DAVBA could have saved the Future Attack and Reconnaissance Aircraft Program (FARA) Program as well as providing a more cost effective Future Long Range Assault Aircraft (FLRAA) Program.
The rotorcraft community faces significantly higher accident rates compared to fixed-wing commercial aircraft, underscoring the critical need for enhanced safety measures. While Helicopter Flight Data Monitoring programs hold promise in improving safety, their widespread adoption remains limited, partly due to challenges associated with the acquisition and analysis of flight data. This paper proposes a Deep Learning (DL) solution to address safety concerns within the rotorcraft community by efficiently acquiring and analyzing flight data for a more automated and comprehensive safety assessment. Specifically, we leverage data obtained with cost-effective off-the-shelf cameras, and process it through Convolutional Neural Networks for automated detection and classification of gauges from several helicopters' cockpits. Our DL pipeline integrates a classifier for helicopter identification, an object detector for cockpit gauges detection and classification, and a network to infer the reading of each detected gauge. The contribution of this work is two-fold: (1) enhance rotorcraft safety by developing a DL framework capable of detecting, classifying, and inferring gauge readings for different helicopter types, and (2) boost research in the field by constructing a curated dataset valuable for aviation and machine learning communities.
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