M. F. S. V. D’Angelo, R. M. Palhares, R. H. C. Takahashi, R., Loschi, L. M. R. Baccarini and W. Caminhas, “Incipient Fault Detection in Induction Machine Stator-Winding Using a Fuzzy-Bayesian Change Point Detection Approach,” Applied Soft Computing, Vol. 11, No. 1, 2011, pp. 179-192. http://dx.doi.org/10.1016/j.asoc. 2009.11.008

has been cited by the following article:

• TITLE: Intelligent System Design for Stator Windings Faults Diagnosis: Suitable for Maintenance Work

  AUTHORS: Lane M. Rabelo Baccarini, Vinícius S. Avelar, Valceres Vieira R. E. Silva, Gleison F. V. Amaral

  KEYWORDS: Fault Diagnosis; Support Vector Machines; Maintenance Work; Software Tool; Winding Short-Circuit

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.6 No.10, October 4, 2013

  ABSTRACT: The short circuit is a severe fault that occurs in the stator windings. Therefore, it is very important to diagnose this type of failure in its beginning before it causes unscheduled stop and the machine loss. In this context, the Support Vector Machine (SVM) is a tool of considerable importance for standard classification. From some training data, it can diagnose whether or not there is a short circuit beginning, and which is important for predictive maintenance. This work proposes a technique for early detection of a short circuit between the turns aiming at its implementation in a real plant. The paper shows simulation and experimental results, and validates the proposed technique.