Prediction of the Remaining Useful Life of Aircraft Systems via Web Interface

In this work a web-based tool is presented for the simulation of a Prognostics and Health Management (PHM) system used for exploring and testing different machine learning experimental scenarios with the goal of predicting the Remaining Useful Life (RUL) of aircraft systems. With this tool, the user can select a set of options like the datasets to use, its size, the machine learning method to apply for the RUL prediction and the metrics used for comparing the results. The proposed datasets correspond to public data extracted from a model which aims to simulate a Turbofan Engine dataset of an aircraft. Also, three different State of the Art machine learning techniques are made available to be applied and tested: a Similarity-based, a Neural Network-based and an Extrapolation-based approach. The results obtained by the different approaches can be graphically compared in the web interface. As the methods are executed remotely, the user incurs no computational costs, which constitutes an advantage of using this tool. This web tool aims to be a user-friendly interface used for simulating online experiments regarding the RUL prediction.