@inproceedings{e69aa9f35a6346b4a1a293fd0bfc7948,
title = "Condition Based Monitoring of Rolling Bearing by Naive Bayes Classifier",
abstract = "Rolling bearings are the main components of rotating machines which are mostly damaged. Therefore, correct and quick fault diagnosis of rolling bearings is very necessary for maintenance. Nowadays, machine learning has emerged as a very effective artificial intelligence technique for fault diagnosis. The Naive Bayes classifier is one of the machine learning techniques that effectively classifies faults. In this work, intelligent fault classification of bearing faults based on Naive Bayes is proposed. The proposed model correctly classifies the different types of fault conditions of rolling bearings. The proposed model has achieved the best results and has been compared with existing methods.",
keywords = "Fault diagnosis, Machine learning, Multiclassification, Naive Bayes",
author = "Sujit Kumar and Alka Kumari and Durgesh Nandani and Manish Kumar",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 5th International Conference on Electrical and Electronics Engineering, ICEEE 2024 ; Conference date: 11-09-2024 Through 12-09-2024",
year = "2025",
month = jan,
day = "1",
doi = "10.1007/978-981-97-9112-5_35",
language = "English",
isbn = "9789819791118",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "589--599",
editor = "Akhtar Kalam and Saad Mekhilef and Williamson, {Sheldon S.}",
booktitle = "Innovations in Electrical and Electronics Engineering - Proceedings of ICEEE 2024",
address = "Germany",
}