Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes. (15th September 2019)

• Record Type: Journal Article
• Authors: Sen, Debarshi; Aghazadeh, Amirali; Mousavi, Ali; Nagarajaiah, Satish; Baraniuk, Richard; Dabak, Anand
• Is Part Of: Mechanical systems and signal processing. Volume 131(2019)
• Page Start: 524
• Page End: 537
• Publication Date: 2019-09-15
• Subjects: Data-driven structural health monitoring -- Damage detection -- Wave propagation in pipes -- Hierarchical clustering -- Multinomial logistic regression; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.06.003
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A semi-supervised and a supervised learning algorithm for health monitoring of pipes is proposed. The proposed approaches are data-driven, hence circumventing the need of computationally prohibitive model-based approaches. The proposed approaches can perform damage detection in pipes with only two actuator-sensor pairs. Efficacy of the proposed approaches are demonstrated using experimental data from two cast iron pipes. Abstract: 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.