Buried pipeline cathode protection system fault diagnosis method based on convolutional neural network

Abstract

The invention relates to a buried pipeline cathodic protection system fault diagnosis method based on a convolutional neural network. The method comprises the steps: collecting historical operation data of a potentiostat and a test pile of a cathodic protection system, carrying out data preprocessing, and constructing a buried pipeline cathodic protection system fault diagnosis model based on theconvolutional neural network; and performing data preprocessing on the real-time operation data, and finally transmitting the real-time operation data to the convolutional neural network fault diagnosis model for fault diagnosis. According to the method, the fault identification and diagnosis capability is improved, the fault state of the buried pipeline cathode protection system can be rapidly and accurately identified through the learning and identification capability of the convolutional neural network, the fault diagnosis and identification accuracy is improved, and the method is of greatsignificance for timely processing faults and maintaining normal operation of the buried pipeline cathode protection system.

Description

Technical field [0001] The present invention relates to the field of system fault diagnosis, in particular, a fault diagnosis method based on a buried pipeline cathode protection system based on convolutional neural network. Background technique [0002] The modern industrial system is showing large-scale, complicated direction, and the operational status of facilities is closely related to the economic benefits of enterprises. The development of the complexity of enterprise production facilities has enabled a series of problems in advance prediction of fault diagnosis and failure of industrial equipment. Once a large system is malfunction, it will definitely bring serious consequences, threatening life safety and property loss. The fault diagnosis technology is often capable of quick diagnosis of faults in the time interval, even predicting the occurrence of faults in advance, guiding the relevant staff to process and repair the relevant faults. [0003] The occurrence and many ...

AI Technical Summary

Problems solved by technology

[0006] 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 method for fault diagnosis of buried pipeline cathodic protection system based on convolutional neural network to solve the artificial discrimination results in the existing fault diagnosis methods Low efficiency, poor accuracy and untimely problems

Images