Fuzzy Cluster Analysis Methods Applied to Impedance Based Structural Health Monitoring for Damage Classification

Abstract

Impedance-based structure health monitoring technique is performed by measuring the variation of the electromechanical impedance of the structure caused by the presence of damage. The impedance signals are collected from patches of piezoelectric material bonded to the surface of the structure (or embedded into it). Through these piezoceramic sensor-actuators, the electromechanical impedance, which is directly related to the mechanical impedance of the structure, is obtained. Based on the variation of the impedance signals, the presence of damage can be detected. A particular damage metric is used to quantify the damage. Distinguishing damage groups from a universe containing different types of damage is a major challenge in structural health monitoring. There are several types of failures that can occur in a given structure, such as cracks, fissures, loss of mechanical components (rivets, for example), corrosion, and wear, among others. It is important to characterize each type of damage from the impedance signals considered. In the present paper, fuzzy cluster analysis methods are used for identification, localization and classification of two types of damage, namely crack and rivet loss. The results show that fuzzy cluster analysis methods are useful for identification, localization and classification of these types of damage.