This paper examines the use of machine learning and deep learning algorithms in the aviation industry, with a specific emphasis on aircraft diagnosis/prognosis, predictive maintenance, feature selection, and flight data monitoring (FDM). This study highlights the potential use of these algorithms in enhancing the efficacy and effectiveness of various aircraft operations. In the field of aviation prognosis and diagnosis, many designs have been acknowledged as efficient for defect identification, calculation of remaining usable life, and prediction of excessive vibration in aero-engines. The architectural models discussed in this paper include deep autoencoders, deep belief networks, long short-term memory networks, and convolutional neural networks. The use of feature selection and scalar feature selection methodologies has been seen to augment the efficacy of FDM (Feature Detection and Matching) algorithms by means of identifying noteworthy features and detecting highly linked features. The application of machine learning algorithms in the domain of predictive maintenance enables real-time assessment of equipment health, hence reducing possible hazards and improving overall equipment performance. The research results emphasize the importance of flight data monitoring in improving safety and operational efficiency in the field of civil aviation. The application of machine learning approaches, namely classification algorithms, facilitates the analysis of flight data for the aim of identifying unsafe behaviors or violations from established operational standards.
Keywords
Artificial Intelligence, Machine Learning, Deep Learning, Aircraft Prognosis Diagnosis, Feature Selection, Predictive Maintenance, Flight Data Monitoring.
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Malene Helgo
Malene Helgo
Federal University of Maranhão, Vila Bacanga, Sao Luís - MA, 65080-805, Brazil.
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Cite this article
Malene Helgo, “Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis”, Journal of Robotics Spectrum, vol.1, pp. 090-099, 2023. doi: 10.53759/9852/JRS202301009.
