涡扇发动机
可靠性(半导体)
期限(时间)
平均故障间隔时间
可靠性工程
计算机科学
断层(地质)
组分(热力学)
工程类
故障率
汽车工程
热力学
量子力学
物理
地质学
功率(物理)
地震学
作者
Faatih Nuraliah Binti Sohaidan,Amgad Muneer,Shakirah Mohd Taib
出处
期刊:2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)
日期:2021-09-29
被引量:1
标识
DOI:10.1109/3ict53449.2021.9581576
摘要
The aero-engine is a crucial component of the aircraft that provides thrust for the plane. To ensure the safety of the aircraft, it is vital to estimate the remaining useful life (RUL) of the engine. Over the past decades, research regarding Prognostic Health Management (PHM) has gained popularity in the field of engineering due to the machineries' fault. The failure of the machinery systems can cause many incidents, such as delays or an increase in operating costs. Thus, to monitor the reliability and safety of an engineering system, which improves the maximum operating availability and reduces maintenance cost, RUL is used to predict the future performance of the machinery to prevent fault. This study proposes a model for RUL estimation based on Long-Short Term Memory (LSTM), which can fully exploit sensor sequence information and reveal hidden patterns in sensor data. The proposed LSTM model has achieved an accuracy of 0.978 and F1-score of 0.960. While the regression model performance has been evaluated using three evaluation matric, mean absolute error (MAE), coefficient of determination (R2), recall. Lastly, the results achieved for MAE, R2 and recall were 12, 0.7856 and 1, respectively.
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