基于XGBoost特征提取的数据驱动故障诊断方法

Data Driven Fault Diagnosis Method Based on XGBoost Feature Extraction

针对目前用于故障诊断领域的机器学习方法尚不能够充分挖掘数据中隐含故障特征信息,存在逼近精度不足的问题,提出一种基于XGBoost算法的隐含特征信息提取方法。根据故障数据与故障类型自定义XGBoost算法的损失函数,迭代构建故障分裂树;提取样本在故障树中的叶子节点位置索引向量并进行特征编码重构,得到隐含故障信息的智能化表征;基于该表征矩阵,使用SVM等机器学习算法建立故障诊断模型,实现多故障模式的识别诊断;最后,以某驱动器的故障诊断为例对方法进行了验证,结果表明:与原始特征下的故障诊断模型相比,基于XGBoost算法提取隐含特征下的诊断模型准确度更高,鲁棒性更好,同时能给出特征变量的重要性排序。

Aiming at the problems that the current machine learning methods in fault diagnosis were not possible to fully exploit the hidden fault feature informations in the data and has insufficient approximation accuracy,an implicit feature information extraction method was proposed based on XGBoost.Firstly,the loss function of the XGBoost algorithm was customized according to the fault data and the fault types,and the fault splitting tree was constructed iteratively.Secondly,the leaf node position index vector of the sample in the fault tree was extracted and the feature code was reconstructed to obtain the intelligent representation of the implicit fault informations.Thirdly,based on the characterization matrix,a machine learning algorithm such as SVM was used to establish a fault diagnosis model to realize predictive diagnosis of multiple failure modes.Finally,the proposed method was validated by taking a driver fault diagnosis as an example.The results show that compared with the fault diagnosis model under the original features,the diagnostic model based on XGBoost extraction implicit features has higher accuracy and better robustness,which may give the order of importance of the feature variables.