Aircraft operators maintenance decisions supporting method

A key element of exploitation processes constitutes maintenance operations and tasks. While being conducted in the proper way, they have a crucial effect on achieving the assumed by aircraft designer and operator goals. Properly conducted maintenance operations allow to meet all the technical objects readiness requirements as well as to achieve desired acceptable risk level. Maintenance system effectiveness might be generally a crucial task for company or entity responsible for the maintenance. In this context, particularly relevant become technical object maintenance procedures and tasks developed by their manufacturers. Experience of the article authors quite early shows the need of the mainte-nance programmes modification. Aircraft manufacturers usually are not so eager to develop and implement mainte-nance programme modifications. Presented situation is very much the case in aviation transport. This was the reason why authors of this article decided to prepare and develop this elaboration which might constitute the assistance and supports complex technical objects users in maintenance decision. The main purpose of this article is to present maintenance decisions’ supporting method for the aircraft operators. This article provides guidelines which include a description of risk in the context of aviation maintenance and introduction of some methodologies, tools and criteria that support identification, analysis and evaluation of risk. Authors included idea, how the aircraft preventive maintenance could be used to mitigate aircraft failure risk during flight operations. It also shows how to adopt and develop effective maintenance program using tools for adequate risk analysis, optimal interval assignments, and selection of the most effective maintenance task. Authors presented methodology and de-scribed steps of the logic diagram analysis for the aircraft systems and their components, in order to manage and adopt aircraft maintenance program to fulfil aircraft airworthiness requirements and operational availability. The whole methodology was described on the basis of the F 16 aircraft maintenance system and with reference to the maintenance data. This article might also constitute an introduction to the aircraft maintenance programme development method.

Attention-based YOLOv4 Algorithm in Non-destructive Radiographic Testing for Civic Aviation Maintenance

The aim of this paper is to use deep learning tools to innovate pre-trained object detection models to improve the accuracy of non-destructive testing (NDT) of civil aviation maintenance. First, this thesis classifies object defects for NDT, such as cracks, undercut, etc. Nowadays, thesis surveys innovation deep-learning methods technology is used to improve the defect detection performance inferencing capability, increase the accuracy and efficiency of automatic identification which in enhanced the safety and reliability of aircraft fuselage in future, mark hidden cracks and solve the challenges that cannot be identified by manual inspection. Second, recent mainstream techniques the YOLOv4 neural network to the graphics card GPU core operator to speed up the recognition of defect images is being applied to the non-destructive inspection process of aircraft maintenance on A, C and D-Level, fully validating the deep learning model's powerful defect detection target capability. The attention-based YOLOv4 algorithm is improved by applying a one-stage attention mechanism to the YOLOv4, thereby improving the accuracy of the innovation model. Finally, thesis improved YOLOv4 based on an attention mechanism is proposed for object detection NDT via the deep learning method to effectively improve and shorten the inspection anomaly detection method for automatic detection sensor systems.