基于并行计算和XGBoost的振动时间序列分类

Peng Liu
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引用次数: 1

摘要

本文记录了我们解决ICPHM23会议[1]发布的数据挑战的方法。该任务是一个时间序列分类问题。我们看到两个通用的工具集可以用来完成任务,并为如此大的数据集产生有希望的高精度。一个是深度神经网络,另一个是梯度增强。我们选择梯度增强。在特征准备过程中,我们开发了一个定制的c++并行计算软件来提取所有需要的特征。手稿包括我们的思考过程和最终的交叉验证结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vibration Time Series Classification using Parallel Computing and XGBoost
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.
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