Tribology and Materials | Volume 4 | Issue 2 | 2025 | 100-115
|
https://doi.org/10.46793/tribomat.2025.009
|
|
Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings
Harshal Aher
,
Nilesh Ghuge
Matoshri College of Engineering and Research Center, Eklahare, India
Abstract: This paper investigated the implementation of machine
learning algorithms for health monitoring and fault detection of tapered
roller bearings (TRBs) (30205 J2/Q, 30206 J2/Q and 30207 J2/Q). Three
defect models were considered: inner race defect, outer race defect and
roller defect, in addition to data from the healthy bearings condition.
An L27 orthogonal array design was used to generate a comprehensive
dataset for each defect model, considering various operational
parameters such as load, unbalance, defect type, bearing type and speed.
Kurtosis was extracted as the sole feature from the vibration signals
for fault classification. Several machine learning models, including
artificial neural network (ANN), decision tree, support vector machine
(SVM), random forest, adaptive boosting (AdaBoost), extreme gradient
boosting (XGBoost), gradient boosting and categorical boosting
(CatBoost), were employed to predict fault severity. The results show
that the ANN model accurately predicts faults based on the kurtosis
metric. This study illustrates the capability of machine learning,
particularly ANN, in enhancing the predictive maintenance strategies for
TRBs, thereby enabling early fault detection under varying operational
conditions.
Keywords: tapered rolling bearings, bearing defect, ANN,
vibration, condition monitoring.
Received: 14-04-2025, Revised: 19-05-2025, Accepted: 27-05-2025
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, which allows users to distribute, remix, adapt,
and build upon the material in any medium or format for non-commercial purposes only, and only so long as attribution is given to the creator.
Abstract: This paper investigated the implementation of machine learning algorithms for health monitoring and fault detection of tapered roller bearings (TRBs) (30205 J2/Q, 30206 J2/Q and 30207 J2/Q). Three defect models were considered: inner race defect, outer race defect and roller defect, in addition to data from the healthy bearings condition. An L27 orthogonal array design was used to generate a comprehensive dataset for each defect model, considering various operational parameters such as load, unbalance, defect type, bearing type and speed. Kurtosis was extracted as the sole feature from the vibration signals for fault classification. Several machine learning models, including artificial neural network (ANN), decision tree, support vector machine (SVM), random forest, adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), gradient boosting and categorical boosting (CatBoost), were employed to predict fault severity. The results show that the ANN model accurately predicts faults based on the kurtosis metric. This study illustrates the capability of machine learning, particularly ANN, in enhancing the predictive maintenance strategies for TRBs, thereby enabling early fault detection under varying operational conditions.
Keywords: tapered rolling bearings, bearing defect, ANN, vibration, condition monitoring.
Received: 14-04-2025, Revised: 19-05-2025, Accepted: 27-05-2025
|
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, which allows users to distribute, remix, adapt, and build upon the material in any medium or format for non-commercial purposes only, and only so long as attribution is given to the creator.