Predictive maintenance (PdM) is indicated state of the machine to perform a schedule of maintenance based on historical data, integrity factors, statistical inference methods, and engineering approaches that are currently often applied to aircraft maintenance. The Predictive maintenance on aircraft to avoid the worse event (failure) and get information about the status of aircraft machines by applied on Machine Learning (ML) to get high accuracy and precision. The research aims to look for the method and technique of ML, which is the best applied on PdM for aircraft in accuracy indicators. The techniques of ML have been divided by classification and regression, which are compared on three ML methods: Random Forest (RF), Support Vector Machine (SVM), and simple LSTM. The result of the study for classification technique are LSTM 98,7%, SVM 95,6%, and RF 900,3%. On other hand, Regression technique for ML result on MAE and RMSE are LSTM 13,55 and 22,13, SVM 15,77 and 20,51, RF 15,06 and 19,98. Classify technique is better and faster than regression when calculating the PdM on an aircraft engine. The LSTM method of ML is the best applied to it because of the accuracy higher and time process faster than other methods in this study. Finally, the LSTM method is highly recommended while using with classify technique on ML to determine the PdM on an aircraft engine.
Prognostic and health management (PHM) in the aviation industry is expanding because of its effect on economic and human safety. Advanced maintenance shall be applied to this industry to inform aircraft engine conditions. PdM (Predictive Maintenance) is an advanced maintenance technique that can be applied to the aviation industry because of its high-precision prediction. Combining PdM as a technique to calculate the RUL (Remaining Useful Lifetime ) and ML (Machine Learning) as a tool to make high-accuracy predictions is mixed together that accurately forecasts the state of aircraft machine condition and on the best time to get the maintenance or service. In this work, we use the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set. This work proposes GRU to determine RUL on aircraft engines to implement a Predictive maintenance strategy. For the training parameters tested in this study, we used a batch size of 512, a learning rate with Adam optimizer of 0.001, then epochs of 200. The essence of the results of this experiment is to obtain a new method with a simpler calculation process and the epoch value and a faster prediction process compared to other methods used, and the results obtained can approach the original value from an economic point of view and the RUL prediction process using the GRU.
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