In nuclear power circulating water pump systems, gears, as critical transmission components, operate in low-speed, heavy-load environments and are crucial for maintaining the stable operation of the pump. To significantly enhance the efficiency and accuracy of fault diagnosis, this paper proposes a gear fault diagnosis technology based on deep neural networks. Efficient Fast Fourier Transform (FFT) techniques are employed to extract frequency domain samples containing rich fault information. Subsequently, a deep neural network model is designed and trained to automatically extract deep features from the frequency domain signals and accurately identify different fault types. By comparing the performance of various deep neural network models, precise diagnosis of gear faults in nuclear power circulating water pumps is achieved. This method is of significant importance for improving nuclear power safety, optimizing plant operational efficiency, and enhancing economic benefits.
Wei et al. (Fri,) studied this question.