@inproceedings{63de294c40524bcab5bfbbfcb904a3cf,
title = "Rolling Bearing Fault Classification: Multinomial Logistic Regression Approach for Enhanced Efficiency",
abstract = "Rotating machines are commonly used in industries, and rolling bearings are important parts of these machines. However, they can get damaged over time. Detecting these damages quickly and accurately is crucial for maintenance. Nowadays, machine learning is a powerful tool for this task. Multinomial logistic regression is one such technique that categorizes faults effectively. In this research, a smart system for classifying bearing faults using the multinomial logistic regression algorithm is introduced. The proposed model accurately identifies various fault conditions in rolling bearings. Our proposed model achieves superior results and has been compared with existing methods.",
keywords = "Fault diagnosis, Fault diagnosis, Machine learning, Multiclassification, Multinomial logistic regression",
author = "Sujit Kumar and Manish Kumar and Ravi Kumar and Pawan Kumar and Ayush Kumar and Priyanshu Raj and Divyanshu Kumar and Sumant Kumar and Santosh 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 = "10.1007/978-981-97-9037-1\_23",
language = "English",
isbn = "9789819790364",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "329--338",
editor = "Akhtar Kalam and Saad Mekhilef and Williamson, \{Sheldon S.\}",
booktitle = "Innovations in Electrical and Electronics Engineering - Proceedings of ICEEE 2024",
address = "Germany",
}