@inproceedings{02264c337bbd42079e8558dbb849d722,
title = "Na{\"i}ve Bayes classifier for temporary short circuit fault detection in stator winding",
abstract = "This paper is proposing Na{\"i}ve Bayes classifier detection system to identify the symptom of stator winding deterioration. The proposed system is based on probabilistic classifier with strong independence assumption of each fault case. The temporary short circuit case is defined as non permanent short circuit fault with high impedance. This fault case is representing the early stage of stator insulation break down. The laboratory experiment is performed to simulate the fault cases consist of induction motor with stator modification and current measurement system. The detection system is trained to identify the temporary short circuit occurrence consist of transient starting, steady state and ending of temporary short circuit. The system is also tested using non trained data to clarify the detection performance.",
keywords = "Fault detection, Wavelet transforms, bayesian methods, induction motor, kernel, stators",
author = "Asfani, {D. A.} and Purnomo, {M. H.} and Sawitri, {D. R.}",
year = "2013",
doi = "10.1109/DEMPED.2013.6645730",
language = "English",
isbn = "9781479900251",
series = "Proceedings - 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013",
publisher = "IEEE Computer Society",
pages = "288--294",
booktitle = "Proceedings - 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013",
address = "United States",
note = "2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013 ; Conference date: 27-08-2013 Through 30-08-2013",
}