# A Novel Intelligent Method for Fault Diagnosis of Steam Turbines Based on T-SNE and XGBoost
###### Abstract  
Since failure of steam turbines occurs frequently and can causes huge losses for thermal plants, it is important to identify a fault in advance. A novel clustering fault diagnosis method for steam turbines based on t-distribution stochastic neighborhood embedding (t-SNE) and extreme gradient boosting (XGBoost) is proposed in this paper. First, the t-SNE algorithm was used to map the high-dimensional data to the low-dimensional space; and the data clustering method of K-means was performed in the low-dimensional space to distinguish the fault data from the normal data. Then, the imbalance problem in the data was processed by the synthetic minority over-sampling technique (SMOTE) algorithm to obtain the steam turbine characteristic data set with fault labels. Finally, the XGBoost algorithm was used to solve this multi-classification problem. The data set used in this paper was derived from the time series data of a steam turbine of a thermal power plant. In the processing analysis, the method achieved the best performance with an overall accuracy of 97% and an early warning of at least two hours in advance. The experimental results show that this method can effectively evaluate the condition and provide fault warning for power plant equipment. fault diagnosis; steam turbine; t-distribution stochastic neighborhood embedding (t-SNE); extreme gradient boosting (XGBoost); clustering +
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original fault feature vector was extracted for reconstruction. The improved SVM algorithm was used to diagnose the fault based on the new fault feature vector. [PERSON] et al. [14] proposed a deep transfer learning method based on the hybrid domain adversarial learning (HDAL) strategy for rotating machines in nuclear power plants. Since failure of steam turbines occurs frequently and causes huge losses in thermal plants, it is important to identify the fault in advance. Thermal power plants use big data technology to deeply mine data value [15; 16; 17; 18], which also makes itself more optimized, safer, and more economical. The steam turbine is one of the most important equipment in thermal power plants [19; 20]. A large amount of steam turbine data, such as condition monitoring data, fault data and so on, has been accumulated in power plant automation systems, which contain characteristic data about the steam turbine fault condition. Accurate fault diagnosis can find the fault in time, repair it in advance, and ensure normal production. However, due to data acquisition and artificial records, the fault records cannot be directly related to the automatic acquisition of time series data. More seriously, due to the low efficiency and low quality of manual recording, the sample data with a large number of labels cannot be directly obtained. In addition, the turbine has high reliability and is in normal operation for a long time, which makes it difficult to provide a large amount of faulty sample data. Since the signals collected by the automatic system are nonlinear and non-stationary, the fault features are often drowned by external factors such as noise and the traditional signal processing; thus, analysis technology is severely limited. Therefore, an effective method for feature extraction and fault diagnosis for steam turbines is needed for this condition. After analyzing the recent progress, a novel fault diagnosis method based on t-distribution stochastic neighborhood embedding (t-SNE), K-means clustering, synthetic minority over-sampling technique (SMOTE) and extreme gradient boosting (XGBoost) is proposed in this paper. Since the vibration signal collected by samplers had a high dimensional feature and the data could not be visualized, t-SNE was used to map the high-dimensional data to the low-dimensional space. Most of the data collected from the thermal plant was unlabeled, so the data clustering method of K-means was used in the low-dimensional space to distinguish the fault data from the normal data for automatic fault identification.