Damage classification of pipelines under water flow operation using multi-mode actuated sensing technology

In a structure, several types of damage can occur, ranging from micro-cracking to corrosion or loose bolts. This makes identifying the damage difficult with a single mode of sensing. Therefore, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In self-sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this experimental study, a pipeline system under water flow operation was examined to verify the effectiveness and robustness of the proposed structural health monitoring approach. Different types of structural damage were inflicted artificially on the pipeline system. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented by composing a three-dimensional space using the damage indices extracted from the impedance and guided wave features as well as temperature variations. For a more systematic damage classification, several control parameters were optimized to determine an optimal decision boundary for the supervised learning-based pattern recognition. Further research issues are also discussed for real-world implementations of the proposed approach.