Vibration Time Series Classification using Parallel Computing and XGBoost

Abstract

This manuscript documents our approach to addressing the data challenge posted by ICPHM23 conference [1]. The task is a time series classification problem. We see two general toolsets can be used to complete the task and produce promising high accuracy for such a large data set. One is deep neural networks, and the other is gradient boosting. We choose gradient boosting. During feature preparation, we developed a customized C++ parallel computing software to extract all desired features. The manuscript includes our thought process and final cross validation results.