We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Rolling bearing fault feature selection based on standard deviation and random forest classifier using vibration signals.
- Authors
Imane, Moussaoui; Rahmoune, Chemseddine; Benazzouz, Djamel
- Abstract
The precise identification of faults is vital for ensuring the reliability of the bearing's performance, and thus, the functionality of rotary machinery. The focus of our study is on the role that feature selection plays in improving the accuracy of predictive models used for diagnosis. The study combined the Standard Deviation (STD) parameter with the Random Forest (RF) classifier to select relevant features from vibration signals obtained from bearings operating under various conditions. We utilized three databases with different bearings' health states operating under distinct conditions. The results of the study were promising, indicating that the proposed method was not only effective but also consistent, even under time-varying conditions.
- Subjects
RANDOM forest algorithms; ROLLER bearings; STANDARD deviations; FEATURE selection; OPTIMIZATION algorithms; PREDICTION models
- Publication
Advances in Mechanical Engineering (Sage Publications Inc.), 2023, Vol 15, Issue 4, p1
- ISSN
1687-8132
- Publication type
Article
- DOI
10.1177/16878132231168503