## 1 Introduction
Additionally, since the existing monitoring system may not be perfect, it is easy to form an information island, leading to a lot of redundancy between monitoring data [23]. In other fields, many researchers have applied support vector machine (SVM), deep learning, and other machine learning methods to similar research and achieved many results [24; 25; 26]. However, none of the models perform well in all cases, e.g., slow training speed for a large sample set, sensitivity to noise, and difficult to determine parameters. With the increasing number of layers and nodes of the neural network model, the larger the data set needs to be trained, the higher the complexity of the model and the training process is difficult to understand. Compared with support vector machine and neural network, the model established by the Gaussian process has fewer parameters that can adaptively deduce super parameters and realize noise separation according to data characteristics, which is very suitable for data processing with noise. In order to address the shortcomings mentioned above, this paper proposes a method of health assessment of hydropower units based on Gaussian process regression (GPR). GPR, as a new machine learning method based on Bayesian theory and statistical learning theory, has achieved good results in other fields of health monitoring [27; 28; 29]. Firstly, the paper uses the Pearson correlation coefficient (PCC), maximum information coefficient (MIC), and grey correlation degree (GCD) to select the appropriate operation state parameters from the massive data stored in the hydropower unit's system to construct the input eigenvector. Then the relevant multi-dimensional information is fully used to establish the health assessment of the hydropower units based on the GPR model using the historical data of no-fault records at the beginning of the unit operation. Finally, a condition monitoring directive (CMD) based on the MD is designed to evaluate the operation state of the unit in real-time. In this paper, the basic process of the ideal framework of hydropower unit health assessment based on GPR is as follows. Firstly, in the off-line phase, for the problem of monitoring parameter selection, the correlation and redundancy between monitoring parameters are analyzed by using the historical monitoring data, and the parameter variables with strong correlation are selected to further remove the redundant variables. Then, based on the above selected monitoring parameters, the health assessment model based on GPR model is constructed by using the data without fault records during the initial operation of hydropower units, and the model is used as the benchmark model for online evaluation. Finally, in the on-line stage, combined with the online monitoring data, an index for evaluating the operation status of hydropower units is designed based on MD, and the reasonable alarm threshold is determined, to realize the on-line condition monitoring of hydropower units, as shown in Figure 1.  
This paper is organized as follows. Section 2 introduces the correlation analysis methods for mining the appropriate input variables by using the correlation among health parameters. Section 3 describes the process of GPR modeling and the method of designing state evaluation indicators. Section 4 presents results from case studies, and Section 5 summarizes the conclusions.