一种基于改进极端梯度提升决策树的轴承故障诊断方法

针对传统故障诊断模型面向海量故障数据时诊断准确度低的问题，首先，提出了一种局部均值分解与固定点算法联合降噪方法，以消除轴承振动信号中的噪声；其次，为了避免原始信号中敏感特征难以提取的问题，提出了一种基于核主成分分析的降维方法；再次，构建了一种基于改进极端梯度提升决策树的故障诊断模型，采用GS-PSO算法优化SVM性能，进而运用改进极端梯度提升决策树思想修正分类模型的残差以提升模型分类精度，应用Spark-大数据平台，通过并行处理技术进行科学计算；最后，采用CWRU提供的滚动轴承数据进行训练与仿真，证明构建的模型能实现对不同类型滚动轴承的识别诊断，并保证诊断结果的准确率。通过对4种不同故障诊断模型的对比分析，表明本文模型具有可行性和优越性。

A Bearing Fault Diagnosis Method Based on Improved XGBoost

Aiming at the problem of low accuracy of traditional fault diagnosis models facing massive fault data, a joint denoising method of local mean decomposition and fixed point algorithm was proposed to eliminate the noise in bearing vibration signals. Secondly, in order to solve the problem of difficult extraction of sensitive features from original signals, a dimension reduction method based on kernel principal component analysis is proposed. Thirdly, a fault diagnosis model based on the improved eXtreme Gradient Boosting decision tree was constructed. The GS-PSO algorithm was used to optimize the performance of SVM, and then the residual error of classification model was modified by using the improved XGBoost idea to improve the classification accuracy of the model. Spark- Big data platform was used to carry out scientific calculation by parallel processing technology. Finally, the rolling bearing data provided by CWRU is used for training and simulation, which proves that the established model can realize the identification and diagnosis of different types of rolling bearings and ensure the accuracy of diagnosis results. Through comparative analysis of four different fault diagnosis models, the results show that the model is feasible and advantageous.