Cited By
View all- Zhang WGao QTian X(2023)Research on Airfield Navigation Lamps Pollution Imaging and Its Detection AlgorithmAdvances in Guidance, Navigation and Control10.1007/978-981-19-6613-2_309(3182-3193)Online publication date: 31-Jan-2023
Application of Artificial Intelligence Technology in the Field of Airport Navigation Lamp Detection
Authors: 
Ning He, Qingyang Yu, Weijia YeAuthors Info & Claims
Ning He, Qingyang Yu, Weijia YeAuthors Info & ClaimsArticle No.: 132, Pages 1 - 6
Abstract
With the rapid development of civil aviation industry, maintaining airport operation normally is a big workload every day. The navigation lamp system plays an important role on the operation of the airport. According to each airport's scales, there are considerable lamps install in airport running way and each lamps need to keep working well especially when airplane taking off or landing, however, inspecting each lamp's statues is a heavy works, the present solution is arranging special workers to check all navigation lamps, and airplane keep staying before examine finished. Due to the inspect time is very limited and the number of the navigation lamps is large, causing the intensity and difficulty increased. If staff found the faulty lamps in the process of inspection, only the second time to repair or replace. In order to improve airport operational efficiency. This paper will use the AI technology build an Expert system prototype to predict evaluation of lamps faulty based on lamp electrical characteristics, time of use, structure characteristics, thermal properties and others indicator, which is useful for staff to improve the work efficiency.
References
[1]
Li Qingyun (2010). Research on present situation and development trend of navigation aid light monitoring system in civil airport. Value Engineering, 20, 149.
[2]
Qingji Zhou, Vinh V (2016). Thai. Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction. Safety Science, 74, 79.
[3]
Mohammad Reza Khosravani, Sara Nasiri, Kerstin Weinberg (2019). Application of case-based reasoning in a fault detection system on production of drippers. Applied Soft Computing Journal. 227, 232.
[4]
Yang Hao, Yang Jianhong, He Ning, Yang Xiaolong (2017). Design of single lamp detection and control module based on passive optical communication [J]. Automation & Instrumentation, 73, 76.
[5]
Jing Wei (2018). Fault diagnosis of airport navigation lights based on convolutional neural network [J]. Software Guide, 113, 115.
[6]
Wang Bingyuan,Tian Kun,Zhang Dandan (2017). Study on fault prediction of airport navigation light system [J]. Computer Simulation, 453, 459.
[7]
Sara Nasiri, Mohammad Reza Khosravani, Kerstin Weinberg (2017). Facture mechanics and mechanical fault detection by artificial intelligence methods: A review. Engineering Failure Analysis, 270, 293.
[8]
Jose Carlos M. Oliveira, Karen V. Pontes, lsabel Sartori, Marcelo Embirucu(2017). Fault Detection and Diagnosis in dynamic system using Weightless Neural Networks. Expert System with Application. 200, 201.
[9]
Liu Bailin, Fan Yuehua (2004). Design of Inference Engine in Fault Diagnosis Expert System [J]. Journal of Xi'an Institute of Technology, 240, 243.
[10]
KHWAJA A S, ZHANG X, ANPALAGAN A, et al (2017). Boosted neural networks for improved short-term electric load forecasting[J]. Electric Power Systems Research, 431, 437.
[11]
KORDANULI B, BARJAKTAROVI L, JEREMI L (2017), et al. Appraisal of artificial neural network for forecasting of economic parameters[J]. Physica A: Statistical Mechanics and its Applications, 515, 519.
[12]
Zheng Wei, Yang Xuemeng, Ge Ning (2016). Design and Implementation of Satellite Fault Diagnosis System Based on Expert System and Neural Network [J]. Journal of Yanshan University, 75, 77.
[13]
Zheng Wei, Yang Xuemeng, Ge Ning (2016). Design and Implementation of Satellite Fault Diagnosis System Based on Expert System and Neural Network [J]. Journal of Yanshan University, 75, 77.
[14]
Zhang Pan, Wang Bo, Qing Xiaoxia (2004), et al. Integrated Application of Multiple Knowledge Representation Methods in Expert Systems [J]. Microcomputer Applications, 4:5, 18.
[15]
Liu Fangmei, Wang Koujin, Cai Zengyu, Meng Yingjie (2015). A Computer Fault Diagnosis Expert System Based on Credibility [J]. Computer Age, 12, 15.
[16]
Liu Bailin, Fan Yuehua(2004). Design of Inference Engine in Fault Diagnosis Expert System [J]. Journal of Xi'an Institute of Technology, 240, 243.
[17]
Monhammad Mahdi Mousavi, Jamal Ouenniche, Kaoru Tone (2019). A comparative analysis of two-stage distress prediction models. Expert System with Applications. 322:341.
[18]
Ni Songsong, Yang Ning, Li Li, Ding Enjie (2015). Research on Fault Diagnosis Expert System of Coal Cutter Based on Extended Fault Tree [J]. Mining and Metallurgical Engineering, 16, 18.
[19]
YüCALAR F, KILINC D, BORANDAG E, et al (2016). Regression Analysis Based Software Effort Estimation Method[J]. International Journal of Software Engineering and Knowledge Engineering, 807, 826.
Index Terms
- Application of Artificial Intelligence Technology in the Field of Airport Navigation Lamp Detection
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Published: 22 October 2019
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CSAE 2019
CSAE 2019: The 3rd International Conference on Computer Science and Application Engineering
October 22 - 24, 2019
Sanya, China
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Cited By
View all- Zhang WGao QTian X(2023)Research on Airfield Navigation Lamps Pollution Imaging and Its Detection AlgorithmAdvances in Guidance, Navigation and Control10.1007/978-981-19-6613-2_309(3182-3193)Online publication date: 31-Jan-2023
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