@inproceedings{0186ae864f3a40a6b0cd1084ac1b5d34,
title = "Faults detection using Gaussian mixture models, mel-frequency cepstral coefficients and kurtosis",
abstract = "Most machines failures can be associated with mechanical failures on bearing failures. This paper proposes a novel approach to detect and classify three types of common faults in rolling element bearings. The approach proposed here makes use Gaussian Mixture model to classify. Mel-frequency Cepstral Coefficients (MFCC) and Kurtosis are extracted from the bearing vibration signal and are used as features. A classification rate of 95% is obtained when using the MFCC features only while a classification rate improves to 99% when Kurtosis features are added to the MFCC.",
author = "Nelwamondo, {Fulufhelo V.} and Tshilidzi Marwala",
year = "2006",
doi = "10.1109/ICSMC.2006.384397",
language = "English",
isbn = "1424401003",
series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "290--295",
booktitle = "2006 IEEE International Conference on Systems, Man and Cybernetics",
address = "United States",
note = "2006 IEEE International Conference on Systems, Man and Cybernetics ; Conference date: 08-10-2006 Through 11-10-2006",
}