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

A wavelet neural network, a technology for simulating circuit faults, applied in biological neural network models, electronic circuit testing, etc., can solve problems such as unsatisfactory results, and achieve the effects of improving diagnostic accuracy, reducing input, strong approximation and fault tolerance.

Active Publication Date: 2012-08-15
宁波力斗智能技术有限公司
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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 th

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

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[0033] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0034] figure 1 To enlarge the circuit diagram for the video, the following will figure 1 The video amplifier circuit shown is used as the actual circuit under test for testing and diagnosis.

[0035] For a large-scale network N, assuming it contains 12 functional modules, sequentially use To mark, as shown in Figure 2, here each module can be regarded as a sub-network of network N, assuming that the network N is torn for the first time , the set of subnetworks is , , with , ( , , , represent the four sub-networks obtained during the first tearing respectively), and perform the second tearing , the set of subnetworks is , , with , ( , , , represent the four sub-networks obtained during the second tearing respectively), so that the network can be torn multiple times, then any two functional modules in the n...

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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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Application Information

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IPC IPC(8): G01R31/28G06N3/02
Inventor 何怡刚齐蓓
Owner 宁波力斗智能技术有限公司
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