Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

Recent research has shown that electromechanical (E/M) impedance-based techniques are an efficient strategy for damage sensing in the context of structural health monitoring (SHM). The basic principle is to excite the lead zirconate titanate (PZT) sensors attached to the structure and to measure the corresponding electrical impedance. A variation in the electrical impedance indicates a variation in the mechanical impedance and, consequently, structural damage. To measure the electrical impedance, most universities and research centers use expensive, bulky, heavy, and high-powered impedance analyzers, which limit new developments and practical applications. To overcome these issues, this paper presents an efficient and inexpensive methodology for electrical impedance measurement. To verify the efficiency and accuracy of the new methodology, practical tests were carried out, and the results were compared with those obtained using a conventional impedance analyzer.

This article presents a simple and versatile measurement system for structural health monitoring (SHM) based on the electromechanical impedance (EMI) technique. The proposed system allows real-time data acquisition from multiple sensors and includes compensation for temperature effects, eliminating one cause of incorrect diagnoses in structural monitoring. Besides the low cost compared to conventional impedance analyzers, the hardware and the software are simple and easier to implement than other measurement systems that have been recently proposed. Tests were carried out on an aluminum beam under different temperatures and on an aluminum plate with nine PZT (lead zirconate titanate) patches. The experimental results show conclusively that the proposed methodology is efficient and feasible for real-time SHM.

Piezoelectric transducers of PZT (lead zirconate titanate) have been widely used in structural health monitoring (SHM) systems based on the electromechanical (E/M) impedance technique. In this technique, damages in the structure are identified by measuring the electrical impedance of the transducer in an appropriate frequency range. Usually, the selection of the correct frequency range to detect and evaluate damage is obtained by trial and error methods. In this paper, it is proposed a formal procedure to determine the frequency ranges in which the PZT transducers are more sensitive for damage detection. Tests were carried out on specimens with different sizes and there was a good match between the theoretical and practical results.