Pipeline fault diagnosis method based on convolutional neural network

Abstract

The invention provides a pipeline fault diagnosis method based on a convolutional neural network. The method is characterized in that external force is applied to a to-be-detected pipeline, and measurement excitation response signals are collected at test points of the pipeline; after the collected measurement signals are subjected to noise reduction, pipeline fault feature signals are extracted and are subjected to standard treatment; and then the fault signals are divided into a training set and a test set to be input to a pipeline fault diagnosis model based on the convolutional neural network for fault identification and classification. The fault identification and diagnosis capability can be improved. The pipeline fault diagnosis model method can quickly and accurately identify the fault state of the pipeline according to the learning and identifying capabilities of the convolutional neural network, and the great significance is achieved for accurate monitoring and early warning of pipeline leakage.

Description

technical field [0001] The invention relates to the technical field of pipeline state monitoring, in particular to a pipeline fault diagnosis method based on a convolutional neural network. Background technique [0002] Most of the pipelines are laid underground, and the geological structure is complex and difficult to observe, which makes it difficult to detect leakage and other problems, and it is easy to induce safety accidents and cause serious losses to personnel, the environment and the economy. [0003] At present, there are a variety of pipeline leakage diagnosis and identification methods at home and abroad, mainly including BP network, fuzzy neural network, and D-S evidence theory. In practical applications, the learning convergence speed of BP network is relatively slow, and the objective function is easy to fall into local minimum. In addition, the BP network can only select the size of the two parameters of the learning rate and the momentum item based on exper...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: in order to overcome the deficiencies in the prior art, the present invention provides a pipeline fault diagnosis method based on convolutional neural network to solve the technical problems of low efficiency of traditional pipeline fault detection and high misjudgment

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