Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory
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 occupationa...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/2/518 |
| _version_ | 1797339433265004544 |
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| author | Suleyman Yildirim Zeeshan A. Rana |
| author_facet | Suleyman Yildirim Zeeshan A. Rana |
| author_sort | Suleyman Yildirim |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-08T09:46:57Z |
| format | Article |
| id | doaj.art-b6ade334841c495ca08e4d1c30d15b64 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:46:57Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b6ade334841c495ca08e4d1c30d15b642024-01-29T14:15:49ZengMDPI AGSensors1424-82202024-01-0124251810.3390/s24020518Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term MemorySuleyman Yildirim0Zeeshan A. Rana1Digital Aviation Research and Technology Centre (DARTeC), Cranfield University, Bedford MK43 0AL, UKCentre for Aeronautics, Cranfield University, Bedford MK43 0AL, UKPredictive 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%.https://www.mdpi.com/1424-8220/24/2/518remaining useful lifepredictive maintenanceaircraft health monitoring |
| spellingShingle | Suleyman Yildirim Zeeshan A. Rana Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory Sensors remaining useful life predictive maintenance aircraft health monitoring |
| title | Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory |
| title_full | Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory |
| title_fullStr | Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory |
| title_full_unstemmed | Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory |
| title_short | Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory |
| title_sort | enhancing aircraft safety through advanced engine health monitoring with long short term memory |
| topic | remaining useful life predictive maintenance aircraft health monitoring |
| url | https://www.mdpi.com/1424-8220/24/2/518 |
| work_keys_str_mv | AT suleymanyildirim enhancingaircraftsafetythroughadvancedenginehealthmonitoringwithlongshorttermmemory AT zeeshanarana enhancingaircraftsafetythroughadvancedenginehealthmonitoringwithlongshorttermmemory |