Carlos Verucchi

Bio: Carlos Verucchi is an academic researcher from National Scientific and Technical Research Council. The author has contributed to research in topics: Induction motor & Fault detection and isolation. The author has an hindex of 11, co-authored 41 publications receiving 453 citations. Previous affiliations of Carlos Verucchi include National University of Central Buenos Aires.

A review on fault diagnosis of induction machines

Abstract: Different alternatives to detect and diagnose faults in induction machines have been proposed and implemented in the last years. These new alternatives are characterised by an on-line and non-invasive feature, that is to say, the capacity to detect faults while the machine is working and the capacity to work sensorless. These characteristics, obtained by the new techniques, distinguish them from the traditional ones, which, in most cases, need that the machine which is being analysed is not working to do the diagnosis. The main purpose of this article is to revise the main alternatives in the detection of faults in induction machines and compare their contributions according to the information they require for the diagnosis, the number and relevance of the faults that can be detected, the speed to anticipate a fault and the accuracy in the diagnosis.

Power transformers monitoring based on electrical measurements: state of the art

Abstract: Faults diagnosis in power transformers has been traditionally based on the insulation resistance measurement, polarisation index, analysis of dissolved gasses in oil, dissipation/power factor measurement, and partial discharges within many other alternatives. Originally, all these techniques presented an offline implementation, that is, with the transformer out of service. Currently, some of them, such as partial discharges measurement or gas analysis (gas chromatography), are carried out online in those cases in which the importance of a machine justifies it. These techniques have been recently complemented with new alternatives, such as frequency response analysis, an offline application technique. At the same time, in recent years, development of online diagnostic strategies has been carried out only based in monitoring of electrical variables. These techniques have the advantage of being economical in relation to traditional ones. Its development are incipient and with high growth potential. This study presents a review of the most important techniques and a critical comparison between them.

Condition Monitoring of the Power Output of Wind Turbine Generators Using Wavelets

Abstract: With an increasing number of wind turbines being erected offshore, there is a need for cost-effective, predictive, and proactive maintenance. A large fraction of wind turbine downtime is due to bearing failures, particularly in the generator and gearbox. One way of assessing impending problems is to install vibration sensors in key positions on these subassemblies. Such equipment can be costly and requires sophisticated software for analysis of the data. An alternative approach, which does not require extra sensors, is investigated in this paper. This involves monitoring the power output of a variable-speed wind turbine generator and processing the data using a wavelet in order to extract the strength of particular frequency components, characteristic of faults. This has been done for doubly fed induction generators (DFIGs), commonly used in modern variable-speed wind turbines. The technique is first validated on a test rig under controlled fault conditions and then is applied to two operational wind turbine DFIGs where generator shaft misalignment was detected. For one of these turbines, the technique detected a problem 3 months before a bearing failure was recorded.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

Abstract: This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART-ANFIS model has potential for fault diagnosis of induction motors.