Intelligent Diagnosis of Pipeline Defects Based on Extreme Learning Machine

In order to carry out effective defect diagnosis of in-service oil and gas pipelines to ensure the safe and stable operation of oil and gas pipelines, this article proposes a pipeline defect intelligent diagnosis method based on particle swarm optimization and extreme learning machine. First, extract the defect signal features to form a feature vector set; then use the particle swarm algorithm to optimize the extreme learning machine to obtain hidden node thresholds; finally, divide the feature vector set into a testing set and a training set, and use improved extreme learning The machine is used as a pattern recognition algorithm for defect pattern recognition. Experimental results show that the method proposed in this paper can effectively perform fault pattern recognition, with a recognition accuracy of 92%, and is suitable for intelligent diagnosis of pipeline defects.