Ground Penetrating Radar is a complex nondestructive evaluation technique where the antenna is the most critical part. The antenna is responsible for transmission and reception of waves at the proper level and frequencies defined in the GPR system specifications. Important GPR features such as resolution and penetration depth depend on its characteristics. In this context, this work outlines the fundamental GPR system theory in order to discuss procedures for improving antennas to GPR applications. Additionally, recent works regarding GPR antenna optimization are reviewed and placed in the context of a framework for GPR antenna design and optimization.

The diagnosis of failures in rotating machines has been subject to studies because of its benefits to maintenance improvement. Condition monitoring reduces maintenance costs, increases reliability and availability, and extends the useful life of critical rotating machinery in industry ambiance. Machine learning techniques have been evolving rapidly, and its applications are bringing better performance to many fields. This study presents a new strategy to improve the diagnosis performance of rotating machines using machine learning strategies on vibration orbital features. The advantage of using orbits in comparison to other vibration measurement systems is the simplicity of the instrumentation involved as well as the information multiplicity contained in the orbit. On the other hand, rolling element bearings are prevalent in industrial machinery. This type of bearing has less orbital oscillation and is noisier than sliding contact bearings. Therefore, it is more difficult to extract useful information. Practical results on an industry motor workbench with rolling element bearings are presented, and the algorithm robustness is evaluated by calculating diagnosis accuracy using inputs with different signal-to-noise ratios. For this kind of noisy scenario where signal analysis is naturally tough, the algorithm classifies approximately 85% of the time correctly. In a completely harsh environment, where the signal-to-noise ratio can be smaller than −25 dB, the accuracy achieved is close to 60%. These statistics show that the strategy proposed can be robust for rotating machine unbalance condition diagnosis even in the worst scenarios, which is required for industrial applications.

Based on a multiscale modelling of the anhysteretic magnetization taking into account mechanical stress and crystallographic texture effects, an extension of the Jiles-Atherton (J-A) hysteresis model is proposed. The magnetization and the volume fractions given by the multiscale approach are advantageously used in the J-A model to modify the anhysteretic magnetization and the pinning parameter. The parameters of the proposed model are identified in order to fit with characterization results under compressive stress using two sets of experimental data. Using the same optimized parameters, the model is tested for the representation of loops under other stress conditions to evaluate its prediction capabilities.