Machine Learning Models in Predicting Failure Times Data Using a Novel Version of the Maxwell Model

Abstract

This work aims to introduce a novel statistical distribution based on Maxwell distribution that can handle both positive and negative data sets with varying failure rates, including decreasing and bathtub-shaped distributions. The novel statistical distribution can be derived via the log transformation approach with an additional exponent parameter, defining the Transformed Log Maxwell (TLMax) distribution. The numerical investigation reveals that the developed TLMax distribution can effectively fit negative and positive data sets. A data set containing failure times for Kevlar 49/epoxy at a pressure of approximately 90% was employed to compare the proposed model against the traditional Maxwell model, and the results obtained indicated that the novel distribution outperformed the comparator. Finally, for the prediction of failure times in the dataset, we employed a machine learning model, including support vector regression (SVR),  K-nearest neighbors’ regression (KNN), linear regression (LR), and gradient boosting regression (XGBoost).  The findings indicate that the KNN model demonstrates greater prediction robustness than the other models. Beyond practitioners and researchers, this research holds relevance for professionals in physics and chemistry, where the Maxwell distribution is commonly employed.