Fault diagnosis method based on improved generative adversarial network for small sample features

Abstract

The invention discloses a fault diagnosis method based on an improved generative adversarial network for small sample features, and solves problems that in a fault detection and diagnosis process, cost of manually adding labels to network data is too high, a convergence fluctuation of a generative adversarial network is large, and label loss in actually collected network data is serious. Firstly,reasons of network faults are analyzed, and a semi-supervised fault diagnosis model is provided by improving a loss function of a generator network and an output layer of a discriminator network; andsecondly, the model is further optimized, an algorithm combining a generative adversarial network and a convolutional neural network is provided, the generative adversarial network is responsible forgenerating data of various fault types, and then the convolutional neural network is trained through the data to complete diagnosis of network faults. According to the method, accurate diagnosis of the network faults can also be realized under the condition of a small amount of labeled data.

Description

technical field [0001] The invention relates to the technical field of communication networks, and mainly relates to a small-sample feature-based fault diagnosis method based on an improved generative confrontation network. Background technique [0002] With the advent of the era of big data and the rapid development of technologies such as deep learning, people can use complex neural network models to mine and extract key information from massive data with the support of powerful computing power. Especially in a complex heterogeneous network environment, thousands of network nodes will generate a large amount of network operation information every day. Under the development trend of network convergence and heterogeneity, fault diagnosis is a key research direction. Troubleshooting is one of the main tasks of managing any network. [0003] Traditional network fault diagnosis mainly compares the alarm information of network performance indicators with the expert experience d...

AI Technical Summary

Problems solved by technology

Using the idea of ​​generative confrontation network, a large number of reliable labeled data sets are obtained for the training of network fault diagnosis algorithms, which solves the problem that the historical data obtained from the real network is not rich enough, and the effect of constructing a diagnosis system is not ideal.

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