Browse Topic: Electronic equipment

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This document is one of a set covering the whole spectrum of aircraft interaction with lightning. This document is intended to describe how to conduct lightning direct effects tests and indirect system upset effects tests. Indirect effects upset and damage tolerance tests for individual equipment items are addressed in DO-160/ED-14. Documents relating to other aspects of the certification process, including definition of the lightning environment, zoning, and indirect effects certification are listed in Section 2. This document presents test techniques for simulated lightning testing of aircraft and the associated systems. This document does not include design criteria nor does it specify which items should or should not be tested. Acceptable levels of damage and/or pass/fail criteria for the qualification tests must be approved by the cognizant certification authority for each particular case. When lightning tests are a part of a certification plan, the test methods described herein
AE-2 Lightning Committee
As per Committee/Henry E. Harschburger recommendations
A-6B1 Hydraulic Servo Actuation Committee
Verifying large alternate product code for an JA GV document - JA1124XX
Active Safety Systems Standards Committee
Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing. The results show that the proposed material
Gao, YajieWang, GuotaoJiang, AipingYan, Huizhen
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