Impedance-based structural 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 on the surface of the structure (or embedded). 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 (e.g., rivets), corrosion, and wear. It is important to characterize each type of damage from the impedance signals considered. In the present paper, probabilistic neural network and fuzzy cluster analysis methods are used for identification, localization, and classification of two types of damage, namely, cracks and rivet losses. The results show that probabilistic neural network and fuzzy cluster analysis methods are useful for identification, localization, and classification of these types of damage.

Resumo Abstract: Vision, the human being's favorite sense, and its great capacity to obtain, to process and to interpret great amount of visual nature data has been throughout the years a great inspiration for development of techniques and technological devices that reproduce it into a computational system. In welding processes, vision can supply information in inspection and welded joint's quality, in the parameters ' monitoring, in trajectory correction and even, finally,

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The impedance-based structural health monitoring technique uses measured signatures changes to identify incipient damages in structures. The purpose is to perform a correlation of these changes with the physical phenomena. However, since electromechanical coupling exists, some environmental influences such as temperature changes may lead to false decision regarding the condition of the structure. As a result, innovative machine learning tools have been extensively investigated to avoid errors in structural prognosis and, in this sense, recent applications of convolutional neural networks (CNN) have emerged within the scope of SHM research, focusing mainly on vibration analysis. However, studies that aim to combine neural architectures with intelligent materials for structural monitoring purposes have been poorly evaluated. Consequently, its integration with the electromechanical impedance method is still considered as being a new application of CNN. Thus, in order to contribute to the SHM area, this work presents a combination of the CNN architecture and the EMI methodology. In the present contribution, three aluminum beams subjected to three different steady temperature levels (0 °C, 10 °C and 20 °C) were studied. For this aim, a test chamber was used for humidity and temperature control. Artificial damages such as mass addition were taken into account so that impedance signatures related to both pristine and damaged conditions can be analyzed. Thus, a one-dimensional Convolutional Neural Network (1D CNN) was designed, trained and used for damage prediction purposes. In this context, a temperature robust model that is able to identify damage independently of environmental condition was developed.

The electromechanical impedance (EMI) method has been regarded as a promising tool for structural health monitoring (SHM) in real time. Usually, massive, high-cost, single-channel impedance analyzers are used to process the time domain data, aiming at obtaining the complex, frequency-dependent, EMI functions, from which features related to the presence, position, and extent of damage can be extracted. However, for large structures, it is desirable to deploy an array of piezoelectric transducers over the area to be monitored and interrogate these transducers successively so as to increase the probability of successful detection of damage in an early phase. In this context, a miniaturized, low-cost, highly expandable SHM architecture for monitoring an array of multiplexed piezoelectric transducers is proposed. Each logical block of the proposed architecture is presented in detail. The proposed architecture does not use costly fast Fourier transform analyzers/algorithms nor requires a digital computer for processing. A personal computer is only necessary for user interfacing. It has been verified that the system can work for frequencies ranging from 0 to 400 kHz with high accuracy and stability. A prototype using inexpensive integrated circuits and a digital signal processor was built and tested for two different types of structures: an aluminum beam and an aircraft aluminum panel. Simulated damages were introduced to each structure and the detection performance of the prototype was tested. The actual prototype uses a universal serial bus connection to communicate with a personal computer; however, a WiFi® connection is also available.

ABSTRACT:The electromechanical impedance method has been seen as a promising tool for structural health monitoring regarding different types of structures and purposes. Most importantly, this method can be used in real-time applications. Frequently, massive, high-cost, single-channel impedance analyzers are used to process the time domain data, aiming at obtaining the complex, frequency-dependent electromechanical impedance functions and therefore infer about the presence, position and extent of an existing damage. However, for large structures, it is desirable to deploy an array of piezoelectric transducers over the area to be monitored and interrogate these transducers successively, in order to increase the probability of successful detection of damage at an early phase. The literature describes many miniaturized systems that can monitor large structures, however, presenting serious restrictions on data acquisition capabilities. This paper presents a hardware that is not limited to any data acquisition restriction, exhibiting an innovative way to measure the electromechanical impedance of multiplexed bonded transducers. Each logical block of the proposed architecture is presented in detail. The proposed system not only avoids costly fast Fourier transform analyzers/ algorithms, but also evades high-speed data acquisition hardware. A prototype using inexpensive integrated circuits and a digital signal processor was built and tested for two different types of structures: an aluminum beam and an aircraft aluminum panel. Simulated damages were introduced to each structure, and the detection performance of the prototype was tested. The actual prototype uses a universal serial bus connection to communicate with a personal computer.

The impedance-based structural health monitoring method has become a promising and attractive tool for damage identification and is considered a nondestructive evaluation technique. However, conventional impedance-based structural health monitoring studies have mainly focused on structural damage identification but not so much on statistical modeling approaches in order to determine a threshold for the decision making of the damage detection system. In this study, the impedance-based structural health monitoring technique is used in a damage detection problem considering temperature variation effects. For this aim, three aluminum 2024-T3 plates were instrumented with small lead zirconate titanate patches close to their borders, and damage was introduced in the central position of the plates, with temperature ranging from −10°C to 60°C. This article proposes a method to statistically determine a threshold for damage detection purposes using concepts of statistical process control, as well as confidence intervals and normality tests in order to obtain a diagnosis with a previously determined confidence level. Thus, this work presents a sensitivity evaluation of the impedance-based structural health monitoring technique as applied to aluminum plates under varying temperature. With the technique proposed, damage threshold levels are determined so that lead zirconate titanate patches placed approximately 280 mm from the damage inserted were able to detect saw cuts of approximately 7 mm long, with 95% confidence intervals inside the temperature range considered.