Temperature variation effect compensation in impedance-based structural health monitoring using neural networks

Abstract

In this article, a new method for temperature compensation on the basis of artificial neural networks (ANNs) in impedance-based structural health monitoring (ISHM) has been introduced. ISHM using piezoelectric wafer active sensors (PWAS) has been extensively developed to provide detection of fault in structure. The principle of this method is based on the electromechanical coupling effect of PWAS materials. Any change in structure leads to changes in mechanical impedance of structure. The electrical impedance of PWAS can sense this change by the electromechanical coupling effect of PWAS. Therefore, the difference in this electrical impedance for undamaged and damaged structures can be considered as a damage index to detect the damage in structure. Since physical and mechanical properties of structure also PWAS materials are temperature dependent, so this electrical impedance of PWAS will be affected by temperature changes. Consequently, the variation in environmental or service temperatures can be detected erroneously as damage in ISHM method. In this article, a new method using ANN based on radial basis function (RBF) has been proposed and developed to compensate the temperature effect on the damage index. A steel plate and gas pipe with bolted joints are considered as two case studies for the performance evaluation of the proposed fault detection methodology. Results confirm that the proposed method using the ANN can be effectively utilized to compensate temperature variation for damage detection in different structures.