Output Data Identification Model for buried Pipeline Damage Detection Using Blind Source Separation Technique Complexity Pursuit
Zia Ullah, Xinhua Wang, Yingchun Chen, Hasan Shahzad, Tao Zhang, Haiyang Ju, Yizhen Zhao · 2019
Underground ferromagnetic pipelines require early alarming systems for structural assessment due to high risk of damage. In high traffic areas the measured sensor data of underground ferromagnetic pipelines are often contaminated by several factors, as: parallel communication lines, underground subway passages and overhead communication lines. Such non parametric methods need to be developed than can perform quick real-time assessment of the 3-axis noisy magnetic field data. This paper implements complexity pursuit (CP) based blind signal separation algorithms for time-based damage detection of underground ferromagnetic pipelines. The proposed method is non-parametric that has the ability to identify modal information directly from the measured magnetic field data. Numerical simulations for multi-degree of freedom systems show that the method can precisely identify the structural parameters. Experiments are performed first in a controlled laboratory environment, secondly in the real world, on pipeline magnetic field data, recorded using high precision magnetic field sensors. The measured structural responses are given as input to the blind source separation model where the complexity pursuit algorithm blindly extracted the least complex signals from the observed mixtures that were guaranteed to be source signals. The output power spectral densities calculated from the estimated modal responses exhibit rich physical interpretation of the pipeline structures.