Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes

The use of guided ultrasonic waves (GUWs) for SHM of pipelines has been a popular method for over three decades. The superiority of GUWs over traditional vibration-based techniques lie in its ability to detect small damages (cracks and corrosion) over a satisfactory length of a pipeline. The physics of the system, however, is extremely involved that renders model-based techniques computationally prohibitive. Data-driven approaches, based on statistical learning algorithmsare far more suitable in such scenarios. In this paper, we propose two data-driven techniques, involving a semi-supervised and a supervised learning approach, for damage detection in pipes. In addition to circumventing the use of a model-based approach, the proposed approaches also aid in reducing the number of sensors deployed, leading to reductions in maintenance costs. The semi-supervised learning-based approach detects the presence of damage using a hierarchical clustering-based algorithm. The supervised learning-based approach performs damage localization in a multinomial logistic regression framework. We validate the proposed algorithms by acquiring guided wave responses from experimental pipes in a pitch-catch configuration using low-cost piezoelectric transducers. We demonstrate that our fully data-driven techniques accurately detect and localize cracks on two cast iron pipes of different lengths using a combination of two sensors.