Nondestructive testing techniques for the diagnosis of defects in solid materials can follow three steps, i.e., detection, location, and characterization. The solutions currently on the market allow for good detection and location of defects, but their characterization in terms of the exact determination of defect shape and dimensions is still an open question. This paper proposes a method for the reliable estimation of crack shape and dimensions in conductive materials using a suitable nondestructive instrument based on the eddy current principle and machine learning system postprocessing. After the design and tuning stages, a performance comparison between the two machine learning systems [artificial neural network (ANN) and support vector machine (SVM)] was carried out. An experimental validation carried out on a number of specimens with different known cracks confirmed the suitability of the proposed approach for defect characterization.

In a smart grid (SG) scenario, accurate and realtime information about key grid parameters (such as voltage and current in each node of the net) are strategic requirements. In order to grant the best tradeoff between the measurement accuracy and the economic sustainability of SG distributed measurements, the design and the development of suitable lowcost current/voltage sensors are required. To this aim, this paper proposes a three-phase contactless current sensor based on anisotropic magnetoresistance effect. A suitable sensors placement strategy is used to compensate the uncertainty due to the interfering magnetic fields (i.e., magnetic fields generated from other electrical apparatus near to the sensors or from the other wires of the three-phase system). After a preliminary characterization of the proposed solution in a simulated environment, an experimental validation is carried out and reported in this paper.

In many industrial application fields as manufacturing, quality control, and so on, it is very important to highlight, to locate, and to characterize the presence of thin defects (cracks) in conductive materials. The characterization phase tries to determine the geometrical characteristics of the thin defect namely the length, the width, the height, and the depth. The analysis of these characteristics allows the user in accepting or discarding realized components and in tuning and improving the production chain. The authors have engaged this line of research with particular reference to non-destructive testing applied to the conductive material through the use of eddy currents. They realized methods and instruments able to detect, locate, and characterize thin defects. In this paper, a novel measurement method able to improve the characterization of the crack depth is proposed. It is based on the use of a suitable multi-frequency excitation signals and of digital signal processing algorithms. Tests carried out in an emulation environment have shown the applicability of the method and have allowed the tuning of the measurement algorithm. Tests carried out in a real environment confirm the goodness of the proposal.

The paper presents a methodology to reduce the energy consumption of visual sensor node in wireless sensor networks. This methodology, based on a balancing between processing and transmission tasks, can be applied in all those situations where subjective image based measurements are required. Considering that in a sensor node, the communication task is the most energy consumption, the proposed method tries to find the best compression ratio able to give a significant reduction of transmission time, returning adequate and objective image quality and compression time. Some results are presented applying the proposed methodology to the particular case study: the use of wireless visual sensors to the remote metering of water counters.

Eddy current testing (ECT) has three main tasks: detection, location, and characterization of defects. The characterization task, which means the ability to find the geometrical characteristics of the defect, is still in the research domain, although in many industrial applications this task has to be carried out with good accuracy to allow reliable acceptance or rejection decision indispensable to save costs and even human lives. This paper proposes an ECT measurement method that allows the reliable estimation of the geometrical characteristics of thin defects (i.e., length, height, and depth) by using a combination of a multifrequency excitation and an optimized support vector machine for regression. The proposed solution is tested on real specimens with known cracks by using a suitable measurement setup comprising a giant magnetoresistance-based biaxial ECT probe.

The development of eddy current nondestructive techniques and instruments is moving toward new challenges in detecting buried and smaller defects and minimizing the amplitude of excitation currents. The smaller the size of the detected defect, the greater is the diagnostic capability of these techniques. Moreover, the lower the excitation current required, the longer the battery discharge time of the portable instruments, the greater the possibility of implementing broadband excitation strategies with reliable results. This feature is also important when portable instruments with array of probes have to operate for a large amount of time. Following the actual trends based on the use of double-coil excitations, this paper proposes the investigation of the performance of these kinds of probes, when small and buried defects are involved, and low amplitudes of the excitation current are adopted. To allow these activities a suitable double-coil-based excitation probe has been assembled. The obtained performance has been also compared with a traditional single-coil excitation probe based on the same sensing element. The experimental analysis demonstrated the appreciable performance of the proposed solution in a number of test cases.