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

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