Research on Fault Diagnosis of Hydraulic Turbines Based on Fault Phenomenon Text
This article applies text mining and machine learning techniques to the fault diagnosis of hydraulic tur-bines,and proposes a fault diagnosis model for hydraulic turbine based on fault phenomenon text.Firstly,the model maps the fault text to a vector space using Word2vec.And then,the extracted text features were input into the XG-BOOST classifier for fault diagnosis.In addition,the KmeansSMOTE algorithm was used to compensate for classifica-tion errors caused by data imbalance.The performance of the model was validated on a real dataset.The results show that the proposed fault diagnosis model has better overall performance than other comparative models.
text miningfault diagnosis modeloversampling algorithmhydraulic turbine
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