Mechanical product fault mode identification method based on evidential neural network model

Abstract

The invention relates to a mechanical product fault mode identification method based on an evidential neural network model. The mechanical product fault mode identification method defines a novel evidential neural network model based on an evidence theory and a radial basis function neural network model, performs mode determination on signal features of a mechanical product based on the evidentialneural network model for identifying fault modes, gives consideration to the features of limited sample amounts and high precision requirement, is based on the evidence theory and the neural networkmodel, utilizes a basic belief assignment function of the evidence theory to evaluate uncertainty in the neural network training process, utilizes an evidence fusion method for carrying out information fusion, and can improve the precision of fault mode identification by means of an evidential neural network.

Description

technical field [0001] The present invention is a mechanical product fault pattern recognition method based on the evidence neural network model, which utilizes evidence information fusion in evidence theory to quantify and optimize the parameters in the neural network training process, and to judge and analyze fault state information in mechanical products. Identification belongs to the technical field of reliability. Background technique [0002] With the rapid development of science and technology, the functional requirements of modern mechanical equipment are becoming more and more complex, and the requirements for work efficiency are getting higher and higher. And due to the complexity of the structure and function of the mechanical equipment, the harsh working environment, the increase in maintenance costs and other factors, the requirements for the performance indicators of the mechanical equipment are getting higher and higher. Once mechanical equipment fails, it wi...

AI Technical Summary

Problems solved by technology

However, the neural network model is based on a large number of samples and the limitation of accuracy, which affects the application of fault identification in mechanical products to a certain extent.

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