The long-term operation of steam turbines inevitably leads to thermal efficiency degradation. Accurately predicting the trend of steam turbine thermal efficiency degradation is crucial for optimizing maintenance cycles and enhancing operational economy and safety. Given the complexity of steam turbine operation

especially the influence of variable working conditions and environmental factors

this study proposes a comprehensive machine learning method for predicting the degradation of steam turbine thermal efficiency. This method integrates random forest (RF)

support vector regression (SVR)

and autoregressive neural networks (AR-Net). Specifically

RF is used to standardize the collected data

SVR is employed to simulate the operational parameters under healthy conditions and establish a degradation index

and finally

AR-Net is applied to construct a predictive model for the trend of efficiency degradation. The results indicate that the RF model effectively mitigates the impact of environmental fluctuations

the SVR model accurately predicts the operational parameters under healthy conditions

and the AR-Net model precisely forecasts the long-term degradation trend. In summary

by integrating historical and real-time data

the model effectively captures the trend of thermal efficiency degradation

providing guidance for predictive maintenance of steam turbines.