Estimating The Health of Helicopter Turbine Engines by Means of Regression and Classification Using a Probabilistic Neural Network

Abstract

This paper presents team Mad SoftMax’s approach to competing in the Conference of the Prognostics and Health Management Society 2024 Data Challenge. The competition tasked participants with estimating the health of helicopter turbine engines by means of regression and classification. Classification was used to categorize each observation within the dataset as belonging to a healthy or faulty engine while probabilistic regression was employed to estimate the torque margin at each measurement. Additionally, teams were challenged to provide a confidence metric to each of their estimations. These metrics reflected the trustworthiness of solutions and added a risk versus reward element to the competition. While the complete dataset for the challenge contained seven engines, four were provided in a training set to encourage robustness of solutions to handle the three withheld engines’ data during the testing and validation phase. The data was scrambled and identifiers to specific engines were removed to not give away patterns specific to a given engine. Open-source libraries such as TensorFlow were utilized to develop classification and regression models and the following paper opines on the process of understanding the data, data cleaning and model evaluation.