Soft Sensors (SS), Machine Learning (ML), Python Software Development, Decision Trees (DT), Support Vector Machine (SVM)
Abstract
This paper aims to develop a software based on Soft Sensors applied to a real engineering problem, which was defined by AIRBUS in the IFAC World Congress 2020 as a benchmark in aerospace engineering. Such software uses an embedded Soft Sensor to process flight simulation data from Simulink® by using Machine learning methods in Python language to classify Oscillatory Failure Errors. Then, a Systematic Literature Review presented basic idea about Machine Learning, a state of art about Soft Sensors and its key questions to guide the research. Three different machine learning representations have been implemented: Support Vector Machines (SVM), Decision Trees (DT) and Multi-Layer Perceptron (MLP). In such methods, the best was Decision Tree (DT) with 51.56% precision average in four scenarios.
