Abstract
Pipeline operational safety is the foundation of the pipeline industry. Inspection and evaluation of defects is an important means of ensuring the safe operation of pipelines. In-line inspection of Magnetic Flux Leakage (MFL) can be used to identify and analyze potential defects. For pipeline MFL identification with inspecting in long distance, there exists the issues of low identification efficiency, misjudgment and leakage judgment. To solve these problems, a pipeline MFL inspection signal identification method based on improved deep residual convolutional neural network and attention module is proposed. A improved deep residual network based on the VGG16 convolution neural network is constructed to automatically learn the features from the MFL image signals and perform the identification of pipeline features and defects. The attention modules are introduced to reduce the influence of noises and compound features on the identification results in the process of in-line inspection. The actual pipeline in-line inspection experimental results show that the proposed method can accurately classify the MFL in-line inspection image signals and effectively reduce the influence of noises on the feature identification results with an average classification accuracy of 97.7%. This method can effectively improve identification accuracy and efficiency of the pipeline MFL in-line inspection.
Highlights
Oil and gas pipeline transportation is one of the five major transportation industries, along with railway, highway, aviation, and water transportation [1]
Long-distance oil and gas pipelines are an important lifeline of the national economy
Periodic inspection and maintenance of pipelines using Magnetic Flux Leakage (MFL) in-line inspection and other methods are important for maintaining the integrity of the pipeline
Summary
Oil and gas pipeline transportation is one of the five major transportation industries, along with railway, highway, aviation, and water transportation [1]. Oil and gas pipelines have been built in land, sea, mountains, and other geographical environments around the world on a large scale, being the lifeblood of modern industry and national economy [3]. With the increase of service age and the change of geological conditions, the pipeline is vulnerable to welds, oil and gas corrosion, and man-made damage, resulting in pipeline failure. If it is not discovered and performed effective maintenance in time, oil and gas leakage or explosion accidents will occur which endanger people’s lives and result in environmental pollution and serious social impacts [4]. Many years of pipeline operation practice at home and abroad show that the whole-life inspection and evaluation of pipelines is one of the most critical methods to ensure the integrity and
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