Next Article in Journal
Analysis of an H∞ Robust Control for a Three-Phase Voltage Source Inverter
Next Article in Special Issue
A Low-Cost Robotic Camera System for Accurate Collection of Structural Response
Previous Article in Journal
Current Advances in Ejector Modeling, Experimentation and Applications for Refrigeration and Heat Pumps. Part 2: Two-Phase Ejectors
Previous Article in Special Issue
Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
Open AccessArticle

A Semi-Supervised Based K-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study

1
Institut de Soudure, Plateforme RDI CND, 4 Bvd Henri Becquerel, 57970 Yutz, France
2
Laboratory of Rammal Hassan Rammal, Research Team PhyToxE, Lebanese University, Faculty of Sciences, Nabatieh 00961, Lebanon
*
Author to whom correspondence should be addressed.
Inventions 2019, 4(1), 17; https://doi.org/10.3390/inventions4010017
Received: 31 December 2018 / Revised: 21 February 2019 / Accepted: 27 February 2019 / Published: 8 March 2019
(This article belongs to the Special Issue Structural Health Monitoring and Their Applications Across Industry)
This paper concerns the health monitoring of pipelines and tubes. It proposes the k-means clustering algorithm as a simple tool to monitor the integrity of a structure (i.e., detecting defects and assessing their growth). The k-means algorithm is applied on data collected experimentally, by means of an ultrasonic guided waves technique, from healthy and damaged tubes. Damage was created by attaching magnets to a tube. The number of magnets was increased progressively to simulate an increase in the size of the defect and also, a change in its shape. To test the performance of the proposed method for damage detection, a statistical population was created for the healthy state and for each damage step. This was done by adding white Gaussian noise to each acquired signal. To optimize the number of clusters, many algorithms were run, and their results were compared. Then, a semi-supervised based method was proposed to determine an alarm threshold, triggered when a defect becomes critical. View Full-Text
Keywords: structural health monitoring; guided waves; pipelines; k-means clustering; alarm threshold structural health monitoring; guided waves; pipelines; k-means clustering; alarm threshold
Show Figures

Graphical abstract

MDPI and ACS Style

Bouzenad, A.E.; El Mountassir, M.; Yaacoubi, S.; Dahmene, F.; Koabaz, M.; Buchheit, L.; Ke, W. A Semi-Supervised Based K-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study. Inventions 2019, 4, 17.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop