Non-contact harmonic magnetic field detection for parallel steel pipeline localization and defects recognition. (August 2021)

• Record Type: Journal Article
• Authors: Zhao, Yizhen; Wang, Xinhua; Sun, Tao; Chen, Yingchun; Yang, Lin; Zhang, Tao; Ju, Haiyang
• Is Part Of: Measurement. Volume 180(2021)
• Publication Date: 2021-08
• Subjects: Harmonic magnetic field -- Parallel pipeline localization -- Defects recognition -- Targets extraction algorithm; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109534
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A non-contact parallel pipeline localization and defects identification method. Design of harmonic magnetic field vector differential focusing array detector. A signal preprocessing algorithm to suppress random pulse and noise is proposed. Target extraction algorithm based on time–frequency analysis and machine learning. Abstract: A novel detection method and targets signal extraction algorithm for parallel steel pipeline localization and defects recognition based on harmonic magnetic field are proposed. An orthogonal vector differential focusing array detector was designed, which radiates high-frequency (HF) and ultra-low-frequency (ULF) electromagnetic waves to pipeline based on the principle of Frequency Modulated Continuous Wave (FMCW), and records the signals in parallel using high-sensitivity Tunnel Magneto Resistance (TMR) array sensors. The joint signal pre-processing algorithm of Combination Morphological Pulse Elimination (CMPE) and Quantum Genetic Gaussian Potential Stochastic Resonance (QGGPSR) is proposed to improve signal quality by adaptively eliminating random pulse and noise. The time–frequency analysis algorithm Complementary Ensemble Local Mean Decomposition (CELMD) is used to decompose the pipeline defects signal in detail, and the appropriate components are selected and fused into a correlation array signal cluster. Finally, the targets signal are separated by Limited-memory BFGS Independent Component Analysis (L-BFGS-ICA) based on machine learning optimization to enhance their legibility. The magnetic dipole harmonic simulation model and the 20# steel parallel pipeline experimental platform are established, and compared with the passive detection method to verify the effectiveness and practicality of the proposed method. The results show that this method can accurately locate parallel pipelines and detect different kinds of defects in noisy environment, which is of great significance for the application of non-contact Non-Destructive Testing (NDT) in practical engineering.