Ship engine detection based on wavelet neural network and FPGA image scanning

Abstract

This paper uses wavelet neurons instead of traditional neurons, and uses wavelet multi-resolution analysis to decompose the FPGA image scan of the ship engine. Because the neural network has the approximation ability of arbitrary functions, the wavelet transform is connected to the neural network to form a wavelet neural network. To test ship engines. The hardware design of GigE image acquisition and processing system based on FPGA was started. FPGA was used as the main control chip and Gigabit Ethernet was used as the transmission medium. The hardware circuit of the image data acquisition and image processing system was designed. It mainly includes the FPGA main control circuit and the FPGA Peripheral circuits. The high-speed image acquisition, transmission, storage, and display module circuit design is realized. Real-time monitoring and fault analysis of the engine's condition is performed by the FPGA image scanning method, and data of the engine's running state is pre-processed with the help of step tracking technology to make it a standard signal. The data is transmitted to the computer through NI's data acquisition card. Combining feature extraction such as information entropy, Fourier transform, EMD and wavelet neural network technology. The accuracy of the diagnosis results and the actual fault state is improved. It can enable the staff to monitor the running status of the engine in real time, improve the efficiency of engine fault diagnosis, reduce labour costs and maintenance costs, and thus realize intelligent, real-time and accurate status monitoring of the engine.