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Atmani et al., 2020 - Google Patents

Enhancement in bearing fault classification parameters using Gaussian mixture models and Mel frequency cepstral coefficients features

Atmani et al., 2020

View PDF
Document ID
15044029196283317624
Author
Atmani Y
Rechak S
Mesloub A
Hemmouche L
Publication year
Publication venue
Archives of Acoustics

External Links

Snippet

Last decades, rolling bearing faults assessment and their evolution with time have been receiving much interest due to their crucial role as part of the Conditional Based Maintenance (CBM) of rotating machinery. This paper investigates bearing faults diagnosis …
Continue reading at acoustics.ippt.gov.pl (PDF) (other versions)

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    • G06K9/6228—Selecting the most significant subset of features
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