Enhancing aircraft safety through advanced engine health monitoring with long short-term memory

Date published

2024-01-14

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MDPI

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Article

ISSN

1424-8220

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Citation

Yildirim S, Rana ZA. (2024) Enhancing aircraft safety through advanced engine health monitoring with long short-term memory. Sensors, Volume 21, Issue 2, January 2024, Article number 518

Abstract

Predictive maintenance holds a crucial role in various industries such as the automotive, aviation and factory automation industries when it comes to expensive engine upkeep. Predicting engine maintenance intervals is vital for devising effective business management strategies, enhancing occupational safety and optimising efficiency. To achieve predictive maintenance, engine sensor data are harnessed to assess the wear and tear of engines. In this research, a Long Short-Term Memory (LSTM) architecture was employed to forecast the remaining lifespan of aircraft engines. The LSTM model was evaluated using the NASA Turbofan Engine Corruption Simulation dataset and its performance was benchmarked against alternative methodologies. The results of these applications demonstrated exceptional outcomes, with the LSTM model achieving the highest classification accuracy at 98.916% and the lowest mean average absolute error at 1.284%.

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Github

Keywords

remaining useful life, predictive maintenance, aircraft health monitoring

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Attribution 4.0 International

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