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Title |
Multiple Damage Detection of Pipeline Structures Using Statistical Pattern Recognition of Self-sensed Guided Waves
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Keywords |
유도 초음파 ; 자가 계측 ; 배관 구조물 ; 복합 손상 ; 지도 학습 기반 패턴인식 ; Guided wave ; Self-sensing ; Pipeline structure ; Multiple damage ; Supervised learning based pattern recognition |
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Abstract |
There have been increased economic and societal demands to continuously monitor the integrity and long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. However, it is very difficult to continuously monitor the structural condition of the pipeline structures because those are placed underground and connected each other complexly, although pipeline structures are core underground infrastructures which transport primary sources. Moreover, damage can occur at several scales from micro-cracking to buckling or loose bolts in the pipeline structures. In this study, guided wave measurement can be achieved with a self-sensing circuit using a piezoelectric active sensor. In this self sensing system, a specific frequency-induced structural wavelet response is obtained from the self-sensed guided wave measurement. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented using the damage indices extracted from the guided wave features. Different types of structural damage artificially inflicted on a pipeline system were investigated to verify the effectiveness of the proposed SHM approach.
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