Analog Circuit Fault Diagnosis System Of The Bp Neural Network-based Fpga Design And Realization

With the rapid development of the electronics industry, the importance of the analog circuit fault diagnosis is more and more obvious, it has great signification for working orderly and reliable design of electronic equipment. Basing on the characteristics of the analog circuit with tolerance, BP neural network is selected for the system which is much more suitable for fault diagnosis. This system is implemented on a FPGA, since the FPGA is parallel processing and high reliability.Many traditional analog circuit fault diagnosis methods are not very efficient. Due to the impact of component tolerances, the results are far from the application in diagnosis of circuits. The fault tolerance, associative memory and good nonlinear mapping of the neural network make this method much better than the traditional methods. BP neural network includes the essential part of neural network theory; it has many merits for its application in analog circuit fault diagnosis. But the standard BP algorithm has its own inherent flaws, such as slow convergence, easy to fall into the local minimum. This paper focuses on using genetic algorithms to optimize the Levenberg -Marquardt BP algorithm to overcome the inherent flaws of standard BP algorithm. And the application process of this optimized algorithm to the analog circuit fault diagnosis system is described in detail.In the first part of this paper, a program is designed for training optimized algorithm by MATLAB. In the second part, a FPGA chip EP2C8Q208C8 is selected according to the fault diagnosis system design requirements, the interface circuit and the FPGA's internal functional modules is designed. The article put emphasis on introducing the FPGA's internal functional modules, including the AD sampling control module, FIFO module, data pre-processing module, BP neural network module and LCD display driver module design.Finally, an actual circuit is selected to test the system. The results show that,comparing with the conventional fault dictionary method, this system is much more simple, efficient and accurate, and has some development potential and practical value.