Fault diagnosis method of turbine guide bearing based on multi-sensor information fusion and neural network

Abstract This paper discusses the application of multidimensional sensing data fusion and neural networks within the fault identification methodology of the turbine guide bearing. Data fusion technology is used to enhance the precision and dependability of fault diagnosis by analyzing the data obtained from multiple sensors. The study combines BP neural networks and multi-sensor information fusion for fault diagnosis of turbine guide bearings. It verifies the method’s effectiveness via simulation experiments, which provides a better method for diagnosing faults in turbine guide bearings.