Abstract
Thrust bearing plays a significant role in the smooth & efficient running of turbines. Its overheating is one of the major problems for the continuous operations of hydropower plants. A reliable forecast of thrust bearing temperature helps designers in preparing future thrust bearings and setting up the operating range of thrust bearing temperatures. In this study, a multiple regression model using SPSS software to forecast thrust bearing temperature of 100 MW Francis turbine was developed. The model integrates fifteen important independent variables. Data sets of all variables were collected followed by formulation of statistical model for a period of one year ranging from May 2012 to May 2013 used for the training whereas the proposed model was tested against the real dataset for June to July 2014. The predicted thrust bearing temperature values were compared with the actual thrust bearing temperature values in order to verify the performance of the model. The model offers a good predictive power with an adjusted R² value of 0.98 and a RMSE of 2.78˚C.
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