Faults detection using gaussian mixture models, mel-frequency cepstral coefficients and kurtosis
FV Nelwamondo, T Marwala - 2006 IEEE International …, 2006 - ieeexplore.ieee.org
2006 IEEE International Conference on systems, man and cybernetics, 2006•ieeexplore.ieee.org
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 …
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 …
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..
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果