Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Rolling Bearing Fault Classification: Multinomial Logistic Regression Approach for Enhanced Efficiency

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Sujit Kumar,
  • Manish Kumar,
  • Ravi Kumar,
  • Pawan Kumar,
  • Ayush Kumar,
  • Priyanshu Raj,
  • Divyanshu Kumar,
  • Sumant Kumar,
  • Santosh Kumar

摘要

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.