The possibilities of using artificial neural networks in the task automated troubleshooting aircraft

\nThe widespread development and integration of information technologies leads to fundamental changes in almost all professional spheres of human activity. One of the most relevant directions in the development of this technology segment is the creation of intelligent tools that allow optimizing and rationalizing technological processes. The main purpose of the presented article is to perform an analysis regarding the possibility of using artificial neural networks as part of the task of automated troubleshooting of an aircraft. As a result of the work, the need to automate this task is actualized. The question is raised about the low efficiency of performing manual troubleshooting. The author confirms the need to develop completely new technologies to complete the search, which should be based on the use of intelligent technologies. In the framework of the work, the most appropriate and effective tools and methods are selected for the implementation of this module. Based on the results obtained, a universal algorithm is formed, it is also a methodology for developing a software module designed to automate the process of troubleshooting an aircraft. The key results were the obtaining of a scientific basis for the implementation of the proposed solution, as well as the systematization of the main possibilities and advantages of its use in practice. The materials of the work can be useful for modern researchers and engineers in the field of aviation, directing efforts to create universal intelligent systems for troubleshooting aircraft.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-11-20
DOI
https://doi.org/10.1051/e3sconf/202459206018/pdf
Primary Topic
Aerospace, Electronics, Mathematical Modeling
Type
article
Field-Weighted Citation Impact
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article

The possibilities of using artificial neural networks in the task automated troubleshooting aircraft

Springer Link (Chiba Institute of Technology)
Aerospace, Electronics, Mathematical Modeling
article

The possibilities of using artificial neural networks in the task automated troubleshooting aircraft

article en

Abstract

\nThe widespread development and integration of information technologies leads to fundamental changes in almost all professional spheres of human activity. One of the most relevant directions in the development of this technology segment is the creation of intelligent tools that allow optimizing and rationalizing technological processes. The main purpose of the presented article is to perform an analysis regarding the possibility of using artificial neural networks as part of the task of automated troubleshooting of an aircraft. As a result of the work, the need to automate this task is actualized. The question is raised about the low efficiency of performing manual troubleshooting. The author confirms the need to develop completely new technologies to complete the search, which should be based on the use of intelligent technologies. In the framework of the work, the most appropriate and effective tools and methods are selected for the implementation of this module. Based on the results obtained, a universal algorithm is formed, it is also a methodology for developing a software module designed to automate the process of troubleshooting an aircraft. The key results were the obtaining of a scientific basis for the implementation of the proposed solution, as well as the systematization of the main possibilities and advantages of its use in practice. The materials of the work can be useful for modern researchers and engineers in the field of aviation, directing efforts to create universal intelligent systems for troubleshooting aircraft.\n

Springer Link (Chiba Institute of Technology)
Industry, innovation and infrastructure
Openalex Percentile: Top 60%
Aerospace, Electronics, Mathematical Modeling
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The possibilities of using artificial neural networks in the task automated troubleshooting aircraft · Springer Link (Chiba Institute of Technology) (2024) | TGRS Research Map | TGRS