TY - GEN
T1 - Rolling Element Bearing Fault Diagnosis Using Hybrid Machine Learning Models
AU - Antunović, Mario
AU - Braut, Sanjin
AU - Žigulić, Roberto
AU - Štimac Rončević, Goranka
AU - Lovrić, Mario
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - Rolling element bearings are widely employed in a variety of mechanical equipment. An effective fault diagnosis can limit the occurrence of accidents, maximize the operating profile of the bearing, and save maintenance costs. Faults in a rolling element bearing commonly include the faults in inner race, outer race, rolling elements, and cage. In this work, an approach based on machine learning algorithms was used. For testing, a public data set from the Case Western Reserve University bearing center (CWRU) was used. After testing different combinations for feature extraction and selection, the best method of processing the original data according to the machine learning model was chosen to be used for the multi-class classification problem. The developed diagnostic methods demonstrated the ability to classify and detect damage to roller bearings. The logistic regression, support vector machine, and random forest algorithms were tested, and the best classification result was obtained using the random forest algorithm on selected variables using the permutation importance of features from the time, frequency, and time-frequency domains. The algorithm based on the Random Forest method achieved the highest average value of accuracy in predicting faults in the roller bearing.
AB - Rolling element bearings are widely employed in a variety of mechanical equipment. An effective fault diagnosis can limit the occurrence of accidents, maximize the operating profile of the bearing, and save maintenance costs. Faults in a rolling element bearing commonly include the faults in inner race, outer race, rolling elements, and cage. In this work, an approach based on machine learning algorithms was used. For testing, a public data set from the Case Western Reserve University bearing center (CWRU) was used. After testing different combinations for feature extraction and selection, the best method of processing the original data according to the machine learning model was chosen to be used for the multi-class classification problem. The developed diagnostic methods demonstrated the ability to classify and detect damage to roller bearings. The logistic regression, support vector machine, and random forest algorithms were tested, and the best classification result was obtained using the random forest algorithm on selected variables using the permutation importance of features from the time, frequency, and time-frequency domains. The algorithm based on the Random Forest method achieved the highest average value of accuracy in predicting faults in the roller bearing.
KW - Fault diagnosis
KW - Logistic regression
KW - Machine learning
KW - Random forest
KW - Rolling element bearing
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85172727970&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-40455-9_33
DO - 10.1007/978-3-031-40455-9_33
M3 - Conference paper
AN - SCOPUS:85172727970
SN - 9783031404542
T3 - Mechanisms and Machine Science
SP - 406
EP - 421
BT - Proceedings of the 11th IFToMM International Conference on Rotordynamics - Volume 1
A2 - Chu, Fulei
A2 - Qin, Zhaoye
PB - Springer Science and Business Media B.V.
T2 - 11th IFToMM International Conference on Rotordynamics
Y2 - 18 September 2023 through 21 September 2023
ER -