Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults

Author:

Jamil Mohd Atif,Khan Md Asif Ali,Khanam Sidra

Publisher

JVE International Ltd.

Cited by 18 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Evaluating electrical power yield of photovoltaic solar cells with k-Nearest neighbors: A machine learning statistical analysis approach;e-Prime - Advances in Electrical Engineering, Electronics and Energy;2024-09

2. Health Indicator Effectiveness in Localized Fault Diagnosis: rolling bearing elements;2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT);2024-05-29

3. Enhanced Bearing Fault Diagnosis Through Trees Ensemble Method and Feature Importance Analysis;Journal of Vibration Engineering & Technologies;2024-05-11

4. Diagnosis of Bearing Faults Using Temporal Vibration Signals: A Comparative Study of Machine Learning Models with Feature Selection Techniques;Journal of Failure Analysis and Prevention;2024-02-21

5. Optimal Robust Time-Domain Feature-Based Bearing Fault and Stator Fault Diagnosis;IEEE Open Journal of the Industrial Electronics Society;2024

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