Fault diagnosis method of equipment analog circuit based on probabilistic neural network

Abstract

The invention discloses a probabilistic neural network-based equipment analog circuit fault diagnosis method, comprising the following steps: S1: constructing a probabilistic neural network model for analog circuit fault diagnosis; Decompose and screen out the useful components; S3: calculate the BCM value of the useful components, and construct the corresponding fault feature vector; S4: use the fault feature vector based on the training samples to train the probabilistic neural network model, and set The minimum optimal probability value Z, optimize the three types of parameters until the fault state set output by the model meets the predetermined requirements, and save the training results; S5: Construct the corresponding fault feature vector of the test samples according to the operations of steps S2 and S3, and input Fault diagnosis is carried out in the probabilistic neural network model trained in step S4. The circuit fault diagnosis method in the present invention provides a technical approach for the selection of fault categories in fault diagnosis.

Description

technical field

[0001] The invention relates to the technical field of analog circuit fault diagnosis, in particular to a probabilistic neural network-based fault diagnosis method for equipment analog circuits. Background technique

[0002] Fault diagnosis of analog circuits is essentially equivalent to the problem of pattern recognition. Pattern recognition is usually concerned with the recognition range and recognition accuracy, the two are interrelated and opposite to each other. However, it seems that many scholars often ignore or deliberately omit the selection of fault categories when conducting research on this type of subject. In the case that the screening method of the fault category is not clearly explained, it has tried to prove that its research results have a very high diagnostic coverage and accuracy rate, and its experimental data and results will inevitably have a certain degree of subjective color. In fact, with the current technical means, it is impossib...

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