Time-based damage detection of underground ferromagnetic pipelines using complexity pursuit based blind signal separation

The measured sensor data of underground ferromagnetic pipelines consist of hidden damage information that can be explored by developing output data identification models due to the availability of only output signal responses. The current research findings elaborate output-only modal identification method Complexity Pursuit based on blind signal separation. An attempt is made to apply the complexity pursuit algorithms, for time-based damage detection of underground ferromagnetic pipelines which blindly estimates the modal parameters from the measured magnetic field signals for targeting the pipeline defect locations. Numerical simulations for multi-degree of freedom systems show that the proposed tested method could precisely identify the structural parameters. Experiments are conducted primarily under well-equipped controlled laboratory conditions followed by confirmation in the real field on pipeline magnetic field data, recorded through high precision magnetic field sensors. The measured recorded structural responses are given as input to the blind source separation model where the complexity pursuit algorithms blindly extracted the least complex signals from the observed mixtures guaranteed to be source signals. The output power spectral densities calculated from the estimated modal responses unveiled elegant physical interpretation of the underground ferromagnetic pipeline structures.