Study on Distribution Characteristics of Metro Stray Current and Evaluation of Cumulative Corrosion Effect

Stray current directly affects the regular operation of electrical equipment and facilities in the subway DC traction power supply system. Therefore, it is worthwhile to study the stray current distribution characteristics during train operation and the quantitative corrosion of buried pipelines. This paper introduces the traction characteristics of power carriages and power wheelsets of subway vehicles into the DC traction process. A finite element model considering the dynamic distribution of stray current under the actual operation of subway vehicles is established. The interference characteristics of stray current and the contribution of power sources under the multiparticle model are analyzed. The rail insulation damage caused by long service time and the quantitative calculation of rail and buried pipeline corrosion is considered. The model results show that the stray current in the buried pipeline under the multiparticle model is more accurate and more suitable for the protection in the actual subway. The quantitative corrosion of the buried pipeline is stronger than the partial insulation damage environment when the rail is not insulated. The rail and buried pipeline corrosion at both ends of the insulation damage position is relatively severe. The stray current distribution model established in this paper gives full play to the solution advantages of the finite element method and provides a new idea for the quantitative calculation of buried pipeline corrosion.

A Prognostics Method Based on Back Propagation Neural Network for Corroded Pipelines

A method that employs the back propagation (BP) neural network is used to predict the growth of corrosion defect in pipelines. This method considers more diversified parameters that affect the pipeline’s corrosion rate, including pipe parameters, service life, corrosion type, corrosion location, corrosion direction, and corrosion amount in a three-dimensional direction. The initial corrosion time is also considered, and, on this basis, the uncertainties of the initial corrosion time and the corrosion size are added to the BP neural network model. In this paper, three kinds of pipeline corrosion growth models are constructed: the traditional corrosion model, the corrosion model considering the uncertainties of initial corrosion time and corrosion depth, and corrosion model also considering the uncertainties of corrosion size (length, width, depth). The rationality and effectiveness of the proposed prediction models are verified by three case studies: the uniform model, the exponential model, and the gamma process model. The proposed models can be widely used in the prediction and management of pipeline corrosion.

Restoring and Enhancing Degraded Underwater Pipelines for Identifying and Detecting Corrosion

Detection of corrosion from underwater images is necessary for oil and gas pipelines to eliminate the internal leakages and hazards. The tests utilized a broad range of underwater pictures of various situations. A modern technique for estimating subsea pipeline corrosion based on the colour of the corroded pipe. For corrupted underwater videos, an image reconstruction and enhancement algorithm is created as a preliminary phase. The created algorithm reduces blurring and improves picture colour and contrast. The improved colours in the imaging details aid in the method of corrosion estimation. In this work we proposed a underwater corrosion detection using image processing techniques. Some machine learning and deep learning techniques have been used for classification of corrosion. In experimental analysis various features have been evaluated for detection of corrosion and it introduces better classification accuracy than traditional approaches.

Corrosion in underground infrastructures

There is a significant loss due to corrosion of buried infrastructure. Many pipes have failed due to mistreatment happening within them all around the world. Different soil aeration leads to macro corrosion cells that cause critical levels within the path corrosion leading to a loss of structural integrity of the buried pipes underground. This review paper seeks to address and presents a predetermined model developed by using software COMSOL Multiphysics to identify and characterize the areas experiencing a high rate of corrosion beneath the surface due to differential aeration. The pipe surfaces experience electrochemical reactions and reactant transport mechanisms in the soil and the pipes. Porosity and degree of saturation make the closed-form equations used to create the mass transport properties and electrical properties that constitute three-phase medium using standard soil parameters. The current model enables the study of soil property variations and conditions from the external environment pipeline corrosion. The model results conclude and agree well with the literature and case studies done at pipeline failure sites. The model used in this review will then enable water utilities to develop forecasting tools that may be useful for assessment.