Research on Intelligent Monitoring and Prediction Model of Vibration Direction of Conventional Island Steam Turbine Shafting

During the operation of steam turbine, due to its complex mechanical structure and high-speed rotation characteristics, shafting vibration has become a safety hazard that cannot be ignored. In order to effectively monitor and predict the vibration of steam turbine shafting, an intelligent monitoring and prediction model for the vibration direction of conventional island steam turbine shafting is developed in this study. The model collects and processes the vibration data of steam turbine in real time by constructing an efficient vibration monitoring system and integrating five components: high-precision vibration sensor, data acquisition equipment, data transmission network, data processing and analysis software and early warning and display system. Advanced spectrum analysis techniques, including wavelet denoising and fast Fourier transform (FFT) algorithm, are used to deeply analyze vibration signals and extract vibration features. Based on neural network method, an accurate prediction model is established, which can monitor the vibration of steam turbine shafting in real time and predict potential vibration anomalies. The experimental results show that the developed model performs well in different working conditions and has high prediction accuracy, which provides a strong guarantee for the safe operation of steam turbines.