Motivation:
Structural Health Monitoring (SHM) is the process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure. This process involves the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors, the extraction of damage-sensitive features from these measurements, and finally, the statistical analysis of these features is then used to determine the current state of system health. Amongst many SHM specialized techniques, the present research work deals with an SHM approach based on ultrasonic waves on the basis of Acoustic Emission (AE) and Acousto-Ultrasonics (AU). Acoustic Emission based techniques are used for the non-destructive inspection of mechanical structures in order to detect very early stage damage before a structure completely fails. Similarly to AE and ultrasonic inspection, Acousto-Ultrasonic techniques make use of stress waves for damage detection. In contrast to AE, the method requires the stress waves to be introduced externally. The AU method combines aspects of acoustic emission signal analysis with ultrasonic characterization methods. These techniques can be considered as valuable tools in order to obtain information regarding the origin and importance of a discontinuity in a structure for a longer safe live and lower operation costs.
Objectives:
The goal of this research work is the development, use and evaluation of advanced signal processing and pattern recognition techniques in order to accomplish damage detection, damage localization and damage identification using stress waves. This can be achieved by directly analyzing the complex characteristics of the propagated signals or by the generation of a statistical model based on these signals. The first is done by generating complex physical models that must account for all possible relevant physical parameters affecting the accuracy and reliability of the proposed model. The latter is normally accomplished by extracting characteristics of the measured response that are well correlated with damage, so-called features, and building statistical models and procedures that do not only provide an estimate of a quantity, but also a degree of belief in that estimate, i.e. how (un)certain we are in our prediction and/or how much we can trust it. Moreover, energy harvesting with focus on structural vibrations and wireless sensor networks are subject of investigation for their application to autonomous structural health monitoring systems.
Description:
Acoustic Emission (AE) waves are transient elastic waves produced by a sudden redistribution of stress in a material. A release of stored strain energy, which takes place when fracture occurs, is consumed by nucleating new external surfaces which emit this kind of elastic waves. Health monitoring of structures can be done passively by listening to these acoustic waves generated by cracks, impact damage and delaminations by means of interpreting the parameters that characterize the wave travel. The AE signals are detected as dynamic motions at the surface of a material and converted into electric signals (in the most cases a piezoelectric element is used) providing valuable information regarding the origin and importance of a discontinuity in a material. Acousto-ultrasonic (AU) is defined as a non-destructive method that uses stress waves to detect and evaluate diffuse defects, damage, and variations in mechanical properties of materials. As with Acoustic Emission, Acousto-Ultrasonics is a highly sophisticated and advanced technique using digital signal processing and pattern recognition algorithms. A typical system for AE/AU is presented in Figure 1.
Figure 1. A typical AE /AU system consists of signal detection, amplification, data acquisition, processing and analysis.
These techniques are implemented in many fields such as Aircraft and Aerospace, Petrochemical, Chemical, Marine and Civil engineering for applications regarding structural integrity assessment, damage detection, welds quality monitoring, internal valve leakage detection among others. They are extensively used as well like a research tool in the sense of a technique to monitor and study the damage in materials and their mechanical properties (new materials, smart materials, Shape memory alloys (SMA)).
