Large-scale analogue circuit fault diagnosis method based on wavelet neural network

Abstract

The invention discloses a large-scale analogue circuit fault diagnosis method based on a wavelet neural network. The method comprises the following steps: dividing functional modules on a to-be-tested circuit, and performing at least twice intersected-tearing on the to-be-tested circuit so that every two functional modules can be respectively contained in different sub networks at least once in the intersected-tearing, and determining each torn node; sampling a voltage of a sample torn node to obtain a fault feature vector; respectively establishing a wavelet neural network corresponding to each tearing, inputting the voltage feature vectors of the to-be-tested circuit in a normal state and different fault states to the wavelet neural network, and training all wavelet neural networks; and performing the logic diagnosis on the output of the wavelet neural network so as to position a fault module. The method provided by the invention can be used for rapidly and accurately positioning the modular fault about hard fault, soft fault and multi-fault state of the large-scale analogue circuit, and is high in engineering application value.

Description

technical field [0001] The invention relates to a large-scale analog circuit fault diagnosis method, in particular to a wavelet neural network-based fault diagnosis method for large-scale analog circuits. Background technique [0002] With the rapid development of contemporary science and technology, the network scale and structure of analog integrated circuits are increasingly functional and modularized. Once some functional blocks of integrated circuits fail, it is only necessary to find and replace the faulty modules in time to ensure the normal operation of the network. At this time, it is no longer necessary to perform component-level diagnosis inside it. Therefore, using intelligent diagnosis technology to quickly and accurately locate faults at the module-level of large-scale analog circuits is an urgent problem to be solved in current practical engineering, and it is also the key to the practical application of fault diagnosis theory. step. [0003] In recent years,...

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Problems solved by technology

[0003] In recent years, researchers have made a lot of achievements in large-scale analog circuit fault diagnosis and proposed many diagnostic methods, such as interval diagnosis method, network tearing method, neural network method, etc., but most of these existing diagnosis methods only It is suitable for the diagnosis of single fault state and a few hard fault states, and the effect is not ideal. The main reason is that it is limited by network tearing and network scale. So far, there are few literatures on hard faults and soft faults of faulty modules. and an effective diagnosis method for multi-fault conditions

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