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Owing to their excellent exploitation properties, induction motors are of key importance to industrial systems. Therefore, early fault detection in induction motors has recently received increasing attention, encompassing a number of modern technologies such as Internet of Things and Cloud Computing. In this paper, an example of fault detection system will be presented. The system detects a broken rotor bar of induction motors, employing conventional vibration analysis techniques and the Radial Basis Function (RBF) neural network enhanced by the Microsoft Azure cloud platform.

Introduction. Identification of predictive factors for walking ability with a prosthesis, after lower limb amputation, is very important in order to define patient’s potentials and realistic rehabilitation goals, however challenging they are. Objective. The objective of this study was to investigate whether variables determined at the beginning of rehabilitation process are able to predict walking ability at the end of the treatment using support vector machines (SVMs). Methods. This research was designed as a retrospective clinical case series. The outcome was defined as three-leveled ambulation ability. SVMs were used for predicting model forming. Results. The study included 263 patients, average age 60.82 Ѓ} 9.27 years. In creating SVM models, eleven variables were included: age, gender, cause of amputation, amputation level, period from amputation to prosthetic rehabilitation, Functional Comorbidity Index (FCI), presence of diabetes, presence of a partner, restriction concerning hip or knee extension, residual limb hip extensor strength, and mobility at admission. Six SVM models were created with four, five, six, eight, 10, and 11 variables, respectively. Genetic algorithm was used as an optimization procedure in order to select the best variables for predicting the level of walking ability. The accuracy of these models ranged from 72.5% to 82.5%. Conclusion. By using SVM model with four variables (age, FCI, level of amputation, and mobility at admission) we are able to predict the level of ambulation with a prosthesis in lower limb amputees with high accuracy.