Document Type
Conference Proceeding
Publication Date
2022
Publisher
AHFE International
Abstract
Fault diagnosis of bearings is essential in reducing failures and improving functionality and reliability of rotating machines. As vibration signals are non-linear and non-stationary, extracting features for dimension reduction and efficient fault detection is challenging. This study aims at evaluating performance of decision tree-based machine learning models in detection and classification of bearing fault data. A machine learning approach combining the tree-based classifiers with de-rived statistical features is proposed for localized fault classification. Statistical features are extracted from normal and faulty vibration signals though time do-main analysis to develop tree-based models of AdaBoost (AD), classification and regression trees (CART), LogitBoost trees (LBT), and Random Forest trees (RF). The results confirm that machine learning classifiers have satisfactory performance and strong generalization ability in fault detection, and provide practical models for classify running state of the bearing.
Editor
Tareq Ahram
Waldemar Karwowski
Pepetto Di Bucchianico
Redha Taiar
Luca Casarotto
Pietro Costa
Recommended Citation
Moghadam, A., & Kakhki, F. D. (2022). Comparative Study of Decision Tree Models for Bearing Fault Detection and Classification. Intelligent Human Systems Integration (IHSI 2022): Integrating People and Intelligent Systems, 22(22). https://doi.org/10.54941/ahfe100968
Comments
Published in the proceedings of the 5th International Conference on Intelligent Human Systems Integration (IHSI2022),22-24 February 2022, Venice, Italy
The authors of papers published in the AHFE Open Access Proceedings will retain full copyrights as specified by the provisions of the Creative Commons: http://creativecommons.org/licenses/by/4.0/