Issue |
ITM Web Conf.
Volume 57, 2023
Fifth International Conference on Advances in Electrical and Computer Technologies 2023 (ICAECT 2023)
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|
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Article Number | 03004 | |
Number of page(s) | 11 | |
Section | Electrical Engineering | |
DOI | https://doi.org/10.1051/itmconf/20235703004 | |
Published online | 10 November 2023 |
LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft Engines
EE Department, Jabalpur Engineering College, Jabalpur
Working machinery has a limited lifespan and occasionally breaks down due to obsolete functioning. A maintenance strategy must be used on the scheduled machinery system in order to prevent the worst scenario (failure) and learn more about the machine’s condition. The ideal maintenance technique is predictive maintenance. Predictive maintenance, whose major goal is to forecast hardware component failures by continually monitoring their status, emphasizes the importance of this requirement so that maintenance operations can be planned far in advance. These observations, which span the lifecycle of the corresponding components, are produced by monitoring systems and often take the form of time series and event logs. The fundamental difficulty of data-driven predictive maintenance is analysing this history of observations in order to create prediction models. Machine learning has become widely used in this direction as a result of its ability to extract knowledge from a range of data sources with the least amount of human involvement. This work aims to investigate and deal with difficult issues in aviation connected to foreseeing component breakdowns on board. Scalability is a crucial component of every suggested strategy due to the vast volume of data related to the operation of airplanes.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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