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For fault failures of a steam turbine occur frequently and cause huge losses, it is important to identify the fault category. A steam turbine clustering fault diagnosis method based on t-distribution stochastic neighborhood embedding (t-SNE) and extreme gradient boosting (XGBoost) is proposed. Firstly, the t-SNE algorithm is used to map high-dimensional data to low-dimensional space, and data clustering is performed in low-dimensional space. Combined with the fault records of the power plant,doi:10.21203/rs.3.rs-36099/v1 fatcat:gr2ysqqvhfebjeqwbpkftckshe