@inproceedings{3eeefcd7aebd486585c24f0b6519542e,
title = "Current envelope analysis for defect identification and diagnosis in induction motors",
abstract = "Increasing demand in reliable manufacturing systems has been accelerating research in condition monitoring and defect diagnosis of vital machine components. This paper investigates defect diagnosis of induction motors, which are widely used in manufacturing systems as a source of actuation. A new approach, based on feature extraction from the envelope of the motor current instead of the motor current itself, has been investigated. This is based on the consideration that motor current envelope is effective in revealing the amplitude-modulated nature of the motor current signal. Three pattern classifiers - Na{\"i}ve Bayes, k-nearest neighbor, and Support Vector Machine, have been investigated for defect classification. Experimental results have demonstrated that the new feature extraction and selection method yields a higher degree of accuracy than the traditional method for motor defect classification.",
keywords = "Autoregressive model, Current envelope, Feature selection, Induction motor diagnosis",
author = "Jinjiang Wang and Shaopeng Liu and Gao, \{Robert X.\} and Ruqiang Yan",
year = "2012",
language = "英语",
isbn = "9781622762477",
series = "Transactions of the North American Manufacturing Research Institution of SME",
pages = "157--165",
booktitle = "40th North American Manufacturing Research Conference 2012 - Transactions of the North American Manufacturing Research Institution of SME",
note = "40th Annual North American Manufacturing Research Conference, NAMRC40 ; Conference date: 04-06-2012 Through 08-06-2012",
}