An Assessment on Condition Monitoring of Automobile Gearbox through Naïve Bayes Approach using Statistical Features-Reference-Cited by-同舟云学术

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An Assessment on Condition Monitoring of Automobile Gearbox through Naïve Bayes Approach using Statistical Features

Published:2022-03-16 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Electronics and Renewable Systems (ICEARS)
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Author:

Dhanraj Joshuva Arockia¹,Sangeethalakshmi K.²,Kumar T. Senthil³,Nagarajan P.⁴,Bharathi P. Shyamala⁵

Affiliation:

1. Hindustan Institute of Technology and Science,Department of Mechanical Engineering,Chennai,Tamil Nadu,India

2. R.M.K. College of Engineering and Technology,Department of ECE,Puduvoyal

3. GRT Institute of Engineering and Technology,Department of ECE,Tiruttani

4. Rajalakshmi Institute of Technology,Department of CCE,Chennai,Tamilnadu,India

5. SIMATS Chennai,Saveetha School of Engineering,Department of ECE,Tamilnadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9751779/9751722/09752115.pdf?arnumber=9752115

Reference38 articles.

1. Improvement in wind energy production through condition monitoring of wind turbine blades using vibration signatures and ARMA features: a data-driven approach;joshuva;Progress in Industrial Ecology, An International Journal,2019

2. Comparative Study on Tree Classifiers for Application to Condition Monitoring of Wind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach

3. Predicting Wind Turbine Blade Fault Condition to Enhance Wind Energy Harvest Through Classification via Regression Classifier

4. A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features

5. Investigating the performance of Hadoop and Spark platforms on machine learning algorithms

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