Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study-Reference-Cited by-同舟云学术

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Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study

Published:2023-09-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Region 10 Symposium (TENSYMP)
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Author:

Sethi Manas Ranjan¹,Parhi Subhransu Sekhar¹,Sahoo Sudarsan¹,Dhanraj Joshuva Arockia²,Sugumaran V³,Mohanty Smruti Ranjan¹

Affiliation:

1. National Institute of Technology, Silchar,Department of Electronics & Instrumentation Engineering,Silchar,India

2. Hindustan Institute of Technology and Science,Department of Mechanical Engineering,Chennai,India

3. Vellore Institute of Technology Chennai,Department of Mechanical Engineering,Chennai,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10223594/10223477/10223618.pdf?arnumber=10223618

Reference9 articles.

1. Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix

2. Comparative Analysis of Machine Learning based Filtering Techniques using MovieLens dataset

3. Fault Diagnosis Method and Application Based on Multi-scale Neural Network and Data Enhancement for Strong Noise

4. Fault Diagnosis of Wind Turbine Blades using Histogram Features through Nested Dichotomy Classifiers

5. A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Rule Classifier

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