Due to its robustness and cost-effectiveness, the three-phase induction motor (TIM) has become the most widespread electric machine today. However, like any other equipment, it is vulnerable to a fault, and about 52% of these are related to bearings. This work presents the detection of flaws in the outer bearing’s raceway from the measurement of motor dynamic strain signals collected from sensors based on fiber Bragg grating (FBG). Three different degrees of severity were considered for faults in the outer bearing’s raceway. The tests were carried out on the motor operating under no-load conditions, with 47 different power supply frequencies. This work proposes a support vector machine (SVM) classifier to identify fault severity levels. Feature extraction was performed using two techniques: selecting the four highest peaks in the frequency spectrum and principal component analysis (PCA). The supervised SVM classifiers show that the dataset formed from the PCA presented a higher hit rate than the dataset constituted by the four highest peaks, with 99.82% and 92.73%, respectively. Based on the methodology presented in this work, it was possible to validate the use of FBG to detect bearing faults. Regardless of the degree of severity of the fault analyzed, the sensor detected its characteristic frequency. Based on the methodology presented in this work, it was possible to validate the use of FBG to detect bearing faults. Regardless of the degree of severity of the flaw analyzed, the sensor detected its characteristic frequency.

Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, !-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan—about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR).

In Nondestructive Evaluation (NDE), ultrasound imaging is a useful tool for detecting flaws or measuring corrosion in metal pipes. Phased-array systems are becoming standard in NDE, as well as post-processing imaging techniques such as the Total Focusing Method. These techniques require knowledge of parameters such as distance between transducer elements, times of arrival and sound speed to produce sharp images. A deviation in sound speed, for instance, may compromise image accuracy. In this paper, we propose a sound speed estimator based on image quality metrics. The approach consists in generating several reconstructed images from the same acquisition signals, but using a set of candidate sound speeds. The speed value that yields the sharpest image is then chosen as the estimated sound speed. Estimates made on data obtained by simulation of a typical scenario presented relative combined uncertainties as low as 0.0166%. The results, obtained in both simulated and real scenarios, show good agreement with expected speeds. The proposed method can be considered a reference-free sound speed calibration method aimed at field applications.