Abstract
Vital defect information present in the magnetic field data of oil and gas pipelines can be perceived by developing such non-parametric algorithms that can extract modal features and performs structural assessment directly from the recorded signal data. This paper discusses such output-only modal identification method Complexity Pursuit (CP) based on blind signal separation. An application to the pipeline flaw detection is presented and it is shown that the complexity pursuit algorithm blindly estimates the modal parameters from the measured magnetic field signals. 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 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.
Highlights
Pipelines are important channels of oil and gas transportation in many developing countries
The primary aim of this study is to develop a non-contact geomagnetic probe using basic principles of magnetic gradient tensor [2,3] to detect the geomagnetic field signals
Non-contact geomagnetic detection [4] is a new kind of non-destructive testing (NDT) technique that needs the Earth’s magnetic field as the stimulus source to locate buried ferromagnetic pipelines and achieves structural defect information, i.e., crack, corrosion and dents, etc., without any excavation
Summary
Pipelines are important channels of oil and gas transportation in many developing countries. BSS based algorithms such as independent component analysis (ICA) [15] and second-order blind identification (SOBI) [16,17] are computational methods used for separating a multivariate signal into their individual subcomponents These methods were applied in structural dynamics for the first time in Reference [18] to conduct output-only modal identification of structures. Advances in blind source separation (BSS) techniques for modal identification and damage detection have offered novel opportunities to develop new data-driven methodologies for efficient and effective sensing and processing of large scale health monitoring data. The main contribution of this paper is to apply the CP algorithms to the pipelines noisy magnetic field data, towards an accurate time-based modal identification These non-parametric data driven algorithms has the ability to perform quick even real time, automatic sensing and processing of the large scale pipeline’s magnetic field data sets, that can provide excellent grounds for the inspection of buried ferromagnetic pipelines. A numerical study for multi-degree of freedom systems and detailed indoor and outdoor experimental results show the ability of the non-parametric CP-BSS learning algorithms to accurately extract time-based modal information of the pipeline structures
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