Application of XGboost Algorithm in Bearing Fault Diagnosis

Abstract

This paper applies the XGboost(eXtreme Gradient Boosting) algorithm to the fault diagnosis of rolling bearing. XGboost is the realization of GBDT(gradient boosting decision tree). Generally speaking, the realization of GBDT(gradient boosting decision tree) is slow. XGBoost is characterized by fast computation and good performance of the model. At the end of this paper, we compare with other tree algorithms, and the results show that the XGboost algorithm is superior to other algorithms in accuracy and time.