A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification-Reference-Cited by-同舟云学术

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A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification

Published:2020-03-06 Issue:1 Volume:33 Page:161-178
ISSN:0952-813X
Container-title:Journal of Experimental & Theoretical Artificial Intelligence
language:en
Short-container-title:Journal of Experimental & Theoretical Artificial Intelligence

Author:

Kaya Yılmaz¹,Kuncan Melih²,Kaplan Kaplan³,Minaz Mehmet Recep²,Ertunç H.Metin³

Affiliation:

1. Engineering Faculty C Block, Department of Computer Engineering, Siirt University, Kezer Campus, Siirt, Turkey

2. Engineering Faculty C Block, Department of Electrical and Electronics Engineering, Siirt University, Kezer Campus, Siirt, Turkey

3. Engineering Faculty A Block, Department of Mechatronics Engineering, Kocaeli University, Umuttepe Campus, Kocaeli, Turkey

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Theoretical Computer Science,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/0952813X.2020.1735530

Reference30 articles.

1. The effect of bearings faults to coefficients obtaned by using wavelet transform

2. Bearing Condition Monitoring Using Tunable Q-Factor Wavelet Transform, Spectral Features and Classification Algorithm

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4. A lightweight complex-domain acoustic feature extraction method for rotating machinery fault detection;Measurement Science and Technology;2024-06-28

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