Quantitative Research on Signal of Pipeline Coating Defect Detection Based on HHT
Coating is a very important way to ensure the integrity of the pipeline. However, due to environmental or external factors, the coating will has some defects such as peeling defects, holes and so on. This paper is concentrated on axial stripping defects and holes of buried pipeline coated with anti-corrosion coating, using ultrasonic guided wave detection technology to obtain the coating detection signal to do the study of quantification, it provides a feasible method to study the level quantitative description of the anticorrosive layer peeling defects.Doing time-frequency analysis to the operating status of the coating, we will have a conclusion that when the coating has peeling defects, the waveform of the detection signal will has a nonlinear characteristic signal. According to the analysis of time-frequency distribution, we can find out that when the signal has nonlinear characteristic signal, the time-frequency distribution will have a "concave" type frequency distribution, it is the basis to judge the junction of the coating peeling defects. According to the judgment, and obtain the relative length of the coating stripping defect, it will finally obtain the degree of damage caused by peeling defects.Therefore, this paper is a post-treatment method for pipeline coating detection, providing feasible and effective signal processing methods and quantitative research methods for the engineering application of the detection technology of non-linear ultrasonic pipeline coating peeling defects.
Signal processing and defect analysis of pipeline inspection applying magnetic flux leakage methods
Corrosion, stresses and mechanical damage of oil and gas pipelines can result in catastrophic failures, so pipeline safety evaluation is an important problem of oil and gas transmission. To evaluate the pipeline safety, this paper described a novel magnetic flux leakage (MFL) inspection device, and the designing MFL intelligent inspection pig was used to multi-radius pipelines and variational work condition. At the same time, because signal processing and defect recognition technique is one of the most important techniques in offshore pipeline inspection MFL system, the paper also discussed its signal processing procedure. Time-frequency analysis, median and adaptive filter, and interpolation processing are adopted to preprocess MFL inspection signal. In order to obtain high sensitivity and precision, we adopted multi-sensor data fusion technique. A wavelet basis function neural network was used to recognize defect parameters. The main contribution of the article is that we presented a novel method to evaluate and predict oil pipelines’ condition through combining neural network, data fusion and expert system techniques, and through constructing a knowledge-based off-line inspection expert system, the system improved its defect recognition capability greatly.
Micro-mechanic model for cathodic blister growth in painted steel
A micro-mechanic blister growth model is proposed for a coating system consisting of a polymer film applied to a steel substrate exposed to salt solutions. The mechanism of the blister formation is based on corrosion-induced disbondment of the coating at the defect periphery coupled with the stress driven diffusive transport of liquid along the coating/substrate interface. By considering the coating as a semi-double cantilever beam loaded by a moment at the periphery and a distributed load along the beam length due to mass transport, a fifth order ordinary differential equation is derived for the beam deflection. The solution is obtained, which yields the functional relationship between the blister growth rate and applied bending moment. The predicted blister growth velocity compared favorably with experimental observations on a paint coated steel panel immersed in a five percent salt water solution. Model predictions allow the construction of a blistering zone/nonblistering zone map, which should help to design better protective coatings for steel.