Machine Fault Diagnosis Using EMD-Gammatone Texture Representation and A Lightweight Self-Attention SqueezeNet-Reference-Cited by-同舟云学术

Machine Fault Diagnosis Using EMD-Gammatone Texture Representation and A Lightweight Self-Attention SqueezeNet

Published:2024-02-18 Issue: Volume: Page:
ISSN:
Container-title:2024 IEEE International Conference on Big Data and Smart Computing (BigComp)
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Author:

Zabin Mahe¹,Binte Kabir Anika Nahian²,Kabir Muhammad Khubayeeb²,Choi Ho-Jin¹,Uddin Jia³

Affiliation:

1. School of Computing, Korea Advanced Institute of Science & Technology, KAIST,Daejeon,South Korea

2. Brac University,Department of Computer Science and Engineering,Dhaka,Bangladesh

3. Woosong University,Artificial Intelligence and Big Data Department,Daejeon,South Korea

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10488103/10488104/10488220.pdf?arnumber=10488220

Reference40 articles.

1. Acoustic emission sensor network -based fault diagnosis of induction motors using a gabor filter and multiclass support vector machines;Islam;Adhoc & Sensor Wireless Networks,2016

2. CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection

3. Fault diagnosis for induction generator-based wind turbine using ensemble deep learning techniques

4. Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals

5. A survey of mechanical fault diagnosis based on audio signal analysis

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