A New Method for Evaluating Natural Gas Pipelines Based on ICEEMDAN-LMS: A View of Noise Reduction in Defective Pipelines

Authors:
Open Access
Publisher website
Google Scholar
Abstract
As the main means of energy transportation, pipelines have the characteristics of 24 h uninterrupted transportation, low cost, strong transmission capacity, and low risk, and they have become an important device for natural gas transportation. However, due to defects caused by the pipeline itself and external forces, it is easy for natural gas leakage to cause major accidents, serious environmental pollution, and huge losses of life and property. Traditional pipeline defect detection technology can be interfered with by a large number of noise signals when collecting defect signals, and the noise reduction method is simple but has poor accuracy, so it cannot directly obtain effective pipeline defect information from the signal and cannot effectively improve the emergency intensity and emergency level of pipeline operation and maintenance management. Therefore, a new noise reduction method, ICEEMDAN-LMS, for natural gas pipelines is proposed in this paper, and the method is calculated and tested in detail. The effectiveness of the new method is verified through the analysis of a pipeline defect signal and normal signal. Moreover, the proposed method can reduce noise more effectively than conventional methods, which has clear value in engineering applications. Importantly, this noise reduction method not only provides a reliable basis for the intelligent diagnosis of pipeline defect signals, but can also be an important reference for helping management departments to make decisions and emergency plans and formulate on-site treatment programs.
Funding Information
  • National Natural Science Foundation of China (41877527, 2022SJYB1931, 21KJB620008)
  • Philosophy and Social Science Foundation of the Jiangsu Higher Education Institutions of China (41877527, 2022SJYB1931, 21KJB620008)
  • Natural Science Foundation of the Jiangsu Higher Education Institutions of China (41877527, 2022SJYB1931, 21KJB620008)

This publication has 33 references indexed in Scilit: