An acousto-ultrasonics pattern recognition approach for damage detection under variable temperature conditions

Structural health monitoring (SHM) has emerged in the last few decades as an essential technology in order to improve the safety and maintainability of critical structures. Among the multiple techniques available for health monitoring, acoustoultrasonics (AU) offers the possibility of inspecting large areas of structures from a piezoelectric active sensor network with a relatively small number of sensors. Nevertheless, several points have to be taken into consideration before a robust method for damage detection can be developed. On the one hand, one of the major tasks for configuring an acousto-ultrasonics system is the selection of appropriate and robust signal processing and pattern recognition algorithms. On the other hand, the increase in complexity of the structures and variability in environmental and operational conditions makes damage detection an extremely challenging problem and points out the necessity for compensating these effects. This paper describes a health monitoring methodology combining the advantages of guided ultrasonic waves together with the compensation for temperature effects, the extraction of defect-sensitive features and sensor data-fusion for the purpose of carrying out a non-linear multivariate diagnosis of damage. Two-well known methods to compensate the temperature effects, namely Optimal Baseline Selection (OBS) and Optimal Signal Stretch (OSS), are investigated within the proposed methodology where the performance is assessed using Receiver Operating Characteristic (ROC) curves. Experimental results in a pipework demonstrate that the proposed methodology is a robust practical solution to compensate for temperature effects and improve the damage detection capabilities within the presented SHM system.