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- Title
Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory.
- Authors
Yildirim, Suleyman; Rana, Zeeshan A.
- 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%.
- Subjects
UNITED States. National Aeronautics &; Space Administration; ENGINE maintenance &; repair; TURBOFAN engines; AUTOMATION; AIRPLANE motors; INDUSTRIAL safety; REMAINING useful life; AIRCRAFT accidents; ENGINES
- Publication
Sensors (14248220), 2024, Vol 24, Issue 2, p518
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s24020518