Analog circuit fault diagnosis method based on differential evolution algorithm and static classification of echo state network

Abstract

The invention relates to an analog circuit fault diagnosis method based on a differential evolution algorithm and static classification of an echo state network, solving the problem of lower diagnosis precision in the methods for diagnosing analog circuit faults by adopting the traditional neural networks. The method comprises the following steps: adopting unit pulse signals to excite an analog circuit to work to obtain circuit-to-be-diagnosed response signals and acquiring unit pulse response output signals of the analog circuit; adopting a method of wavelet transform to process the acquiredunit pulse response output signals of the analog circuit, taking the obtained fault features as the data samples, inputting the fault features into the echo state network, adopting a differential evolution algorithm to train the fault features and building an analog circuit fault diagnosis model; and adopting the method of wavelet transform to process the circuit-to-be-diagnosed response signals to obtain fault data and inputting the fault data into the analog circuit fault diagnosis model to obtain and output the fault diagnosis results. The method is suitable for fault diagnosis 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 traditional neural network,

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