Fault Diagnosis Method for Analog Circuits Based on Echo State Network Synchronous Optimization

Abstract

The invention discloses a method for diagnosing faults of an analog circuit based on synchronous optimization of an echo state network, and relates to a method for diagnosing faults of an analog circuit. The problem of lower diagnosis precision by using the traditional neural network to diagnose the faults of the analog circuit is resolved. The method comprises the following steps of: using a unit pulse signal to excite the analog circuit to work; obtaining a response signal to be diagnosed of the circuit; collecting a unit pulse response output signal of the analog circuit; using a wavelet transform method to process the unit pulse response output signal of the analog circuit; obtaining fault characteristics as a data sample; inputting the data sample in the echo state network; using a differential evolution algorithm to perform synchronous optimization selection of parameters and characteristics; establishing a model for diagnosing the faults of the analog circuit; using the wavelettransform method to process the response signal to be diagnosed of the circuit; obtaining fault data; inputting the fault data in the model for diagnosing the faults of the analog circuit; and obtaining and outputting a fault diagnosis result. The method disclosed by the invention is applicable for diagnosing the faults of the analog circuit.

Description

technical field [0001] The invention relates to an analog circuit fault diagnosis method. Background technique [0002] In electronic equipment, analog circuits are the weakest link most likely to fail, and fault diagnosis of analog circuits can improve the maintainability of electronic equipment. Due to the lack of a good fault model for analog circuits, the complex nonlinear relationship between circuit response and component parameters, and the limitation of the number of measuring points, the research on fault diagnosis of analog circuits is not yet mature. In this case, artificial intelligence-based methods are introduced into analog circuit fault diagnosis, which treat analog circuit fault diagnosis as a pattern recognition problem. Due to its good nonlinear mapping ability and self-learning adaptability, neural network is most commonly used in the intelligent diagnosis method of analog circuits. However, the traditional neural network, such as the multi-layer percep...

AI Technical Summary

Problems solved by technology

[0005] The present invention aims to solve the problem of low diagnostic accuracy of analog circuit fault diagnosis using traditiona

Images