%0 Journal Article
%T Predictive Maintenance of Aircraft Motor Health with Long-Short Term Memory Method
%A B¨¹lent BOLAT
%A Merve Ayy¨¹ce KIZRAK
%J -
%D 2019
%X Predictive maintenance is an important part of applications that require costly engine maintenance, such as automotive, aircraft and factory automation. It is important to anticipate the maintenance periods of the engines and develop a business management strategy accordingly in terms of both work safety and efficiency. For predictive maintenance, the sensor data from the motors were used to determine the wear time and level of the engine. In this study, a solution based on deep learning is proposed as an alternative to traditional regression and classification methods. The NASA Turbofan Engine Corruption Simulation data set was studied by using Long-Short Term Memory (LSTM), one of the deep learning models and known to make successful predictions on time-dependent data such as time series. During the simulations, the highest classification performance and the lowest mean absolute error were obtained by LSTM as 98,876% 1.343 respectively
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%U http://dergipark.org.tr/gazibtd/issue/44915/495730