[2F1-GS-10f] AI応用：安全

[2F1-GS-10f-01] Predicting Remaining Useful Life Curve of Rolling Bearings Under Defect Progression Based on Hierarchical Bayesian Regression

〇Masashi Kitai^1,2,3, Yoshinobu Akamatsu³, Hiroki Fujiwara³, Ryoji Tani³, Masayuki Numao⁴, Ken-ichi Fukui⁴ (1. Graduate School of Information Science and Technology, Osaka University, 2. NTN Next Generation Research Alliance Laboratories, Osaka University, 3. NTN Corporation, 4. The Institute of Scientific and Industrial Research, Osaka University)

Keywords:Hierarchical Bayesian Regression, Random Forest, Remaining Useful Life, Rolling Bearing

In order to improve Remaining Useful Life (RUL) prediction accuracy for rolling bearings under defect progression, robustness for individual difference and fluctuation of vibration features are challenging issues.
In this paper, we propose a novel RUL prediction method that uses a hierarchical Bayesian method to consider the individual difference of RUL, and uses an intermediate variable indicating the defect condition instead of predicting RUL directly from vibration features.
The proposed method can perform a monotonous RUL prediction curve and improved prediction accuracy especially for early stage of defect progression.