Percorrer por autor "Goedtel, Alessandro"
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- Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic SignalPublication . Bressan, Glaucia; Azevedo, Beatriz Flamia; Santos, Herman Lucas dos; Endo, Wagner; Agulhari, Cristiano; Goedtel, Alessandro; Scalassara, PauloConsidering the classification of failures in electrical machines, the present paper aims to use supervised machine learning techniques in order to classify faults in electrical machines, using attributes from audio signals. In order to analyze data and recognize patterns, the considered supervised learning methods are: Bayesian Network, together with the BayesRule algorithm, Support Vector Machine and k-Nearest Neighbor. The performances and the results provided from these algorithms are then compared. The main contributions of this paper are the acquisition process of audio signals and the elaboration of Bayesian networks topologies and classifiers structures using the acquired signals, since the algorithms provide the generalization of the classification model by revealing the network structure. Also, the utilization of audio signals as input attributes to the classifiers is infrequent in the literature. The results show that the Support Vector Machine and k-Nearest Neighbor present a high accuracy. On the other hand, the Bayesian approach is advantageous due to the possibility of showing, through graph representations, the generalized structure to represent the trend of faults in real cases on industry applications.
- Photovoltaic water pumping system for small power conventional AC pumpsPublication . Tangerino, Bruno Henrique Boschetti; Roman, Dionizio José Vargas; Leite, V.; Goedtel, Alessandro; Ferreira, Ângela P.; Batista, José; Maidana, WellingtonThe interest of photovoltaic (PV) water pumping systems with standard components is increasing as a low cost and independent solution over dedicated systems. However, one of the challenges of this alternative is the fact that small power systems to drive power pumps require a small number of PV modules and the PV string voltage is, usually, not enough to feed the frequency converter. This paper presents an approach to the drive, using a DC/DC converter with maximum power point tracking (MPPT). The overall system has been tested on a real experimental platform.
- Stator winding fault detection using external search coil and artificial neural networkPublication . Vicente, João; Ferreira, Ângela P.; Castoldi, Marcelo; Teixeira, João Paulo; Goedtel, AlessandroThis paper presents a methodology for winding stator fault detection of induction motors, using an external search coil, which is a noninvasive technique and can be applied during motor operation. The dispersion magnetic flux of the motor operating in abnormal conditions induces a voltage in the search coil that differs from a reference pattern corresponding to the healthy stator winding. Experimental data were obtained in a test bench using a 0.75 kW three-phase squirrel-cage induction motor with the stator winding modified to allow the introduction of short circuits. This work considered short circuits in one phase, involving 1%, 3%, 5% and 10% of the turns, with the motor loaded with a varying torque. Fault diagnosis is obtained through two models of artificial neural networks, implemented with the signals in the time domain. The obtained results demonstrated that the developed methodology presents difficulties in predicting short circuits in incipient stages, but for short circuits of higher severity, the behaviour improved substantially, being 100% successful for faults with 10% turns short-circuited.