Remaining Useful Life Prediction of Equipment Based on XGBoost

Zhiyang Jia, Zhibo Xiao, Yijin Shi
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引用次数: 1

Abstract

Remaining Useful Life (RUL) prediction is an essential task in the practice of predictive maintenance which aims at repairing equipment before it fails based on data received about it from sensors. Our simulation experiments use the Turbofan engine degradation dataset CMAPSS Data, which gained historical data to predict the remaining useful life and does not require participants to consider the underlying physical factors. RUL prediction is performed by machine learning methods including Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and XGBoost after data pre-processing and feature selection. XGboost is a kind of ensemble learning algorithm that can generate a series of weak learners by continuous training and then combine these weak learners to become a strong learner. Experimental results reveal that the performance of XGBoost based model is effective in such dataset comparing with the traditional machine learning models.
基于XGBoost的设备剩余使用寿命预测
剩余使用寿命(RUL)预测是预测性维修实践中的一项重要任务,其目的是根据从传感器接收到的数据在设备发生故障之前进行维修。我们的模拟实验使用了涡扇发动机退化数据集CMAPSS Data,该数据集获得了预测剩余使用寿命的历史数据,并且不需要参与者考虑潜在的物理因素。在数据预处理和特征选择后,采用决策树(DT)、随机森林(RF)、支持向量回归(SVR)和XGBoost等机器学习方法进行RUL预测。XGboost是一种集成学习算法,它可以通过不断的训练生成一系列弱学习器,然后将这些弱学习器组合成一个强学习器。实验结果表明,与传统的机器学习模型相比,基于XGBoost的模型在此类数据集上的性能是有效的。
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