Multi-fault intelligent diagnosing method for artificial circuit utilizing quantum Hopfield neural network

Abstract

The invention provides a multi-fault intelligent diagnosing method for an artificial circuit utilizing quantum Hopfield neural network and aims at multi-fault coupling of the artificial circuit. The multi-fault intelligent diagnosing method includes the steps of data acquisition, feature extraction, feature quantization, fault cause probability analysis and the like. Ideal single-fault response and actual measured multi-fault response of the artificial circuit are obtained through SPICE simulation and a data acquisition board respectively. After wavelet packet decomposition, fault response wavelet coefficient defined by a new energy function realizes construction of an energy feature space. Elements in the energy feature space is submitted on the basis of quantization to a quantum Hopfield neural network model. Neuron states and connecting weight matrix in the network are expressed in quantum states. By calculating probability value of occurrence of related weight element in measurement matrix, the occurrence probability of quantum key input mode in forms of specific quantum memory prototype at specific time, and accordingly occurrence probability of multiple faults relative to specific single fault is obtained to judge fault types.

Description

technical field [0001] The invention relates to a fault diagnosis method of an analog circuit, in particular to an intelligent diagnosis method for multiple faults of an analog circuit using a quantum Hopfield neural network. [0002] Background technique [0003] Fault diagnosis for analog circuits has always been a research hotspot in the field of electronic engineering. The reliability of analog circuits has an important impact on the stability of many complex industrial systems. After more than 20 years of development, analog circuits have proposed many intelligent fault diagnosis methods for single fault research at the system level, circuit board level, and chip level, and realized commercial practical applications. These intelligent methods can automatically help operators perform fault diagnosis and give correct maintenance suggestions for the current fault situation, such as Y. Huang, Abductive reasoning network based diagnosis system for fault section estimation ...

AI Technical Summary

Problems solved by technology

[0009] Aiming at the above-mentioned deficiencies in the prior art, the present invention solves the problems of long time consumption and poor identification ability of the existing analog circuit multi-fault diagnosis, and provides an analog circuit multi-fault intelligent diagnosis method using quantum Hopfield neural network

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