Web-based tool for predicting the Remaining Useful Lifetime of Aircraft Components

This work aims to present and describe a web-based tool, which enables the simulation of a Prognostics and Health Management (PHM) system where the goal is to predict the Remaining Useful Lifetime (RUL) of specific aircraft components using different machine learning techniques. This tool is accessed online and provides the user with the possibility of creating a specific experimental scenario. The user selects a specific dataset from amongst the proposed datasets and a specific machine learning method to apply to the dataset. When submitting the setup configuration, the results, regarding the calculated RULs of the test dataset, will be displayed in the form of a graph. The suggested datasets are made of synthetic data received from aircraft sensors and the proposed methods represent different methodologies for the RUL calculation. The web interface should be easy to use and be a helpful tool for simulating and comparing different machine learning methods for RUL prediction.