Abstract:The hydropower big data algorithm platform can calculate the real-time monitoring data of the hydropower station through its embedded data computing function component, and then find its characteristic value, characteristic difference and ramp rate. The fault diagnosis model predicts the fault by logical judgment of its value, so the scientific selection of fault characterization is particularly important. In this paper, through the method of mathematical statistical analysis, first use MATLAB to carry out T test to check whether the monitoring quantity is normal distribution, discuss which correlation analysis is more scientific, and then use SPSS for bivariate correlation analysis to analyze the fault in the hydropower big data platform. Whether the selected quantities of the model are strongly correlated with each other, the strong correlation means that the two have the same trend, so that a fault can be judged and the rationality of the fault model can be verified. After the failure model verification of this paper, the conclusions support the scientificity of the fault model of the hydropower big data algorithm platform, and the correlation quantity selection is reasonable. The correlation model established by the selected correlation quantity can judge and predict whether the fault will occur.