Reliability estimation and remaining useful lifetime prediction for bearing based on proportional hazard model
来源期刊：中南大学学报(英文版)2015年第12期
论文作者：WANG Lu ZHANG Li WANG Xue-zhi
文章页码：4625 - 4633
Key words：prognostics; reliability estimation; remaining useful life; proportional hazard model
Abstract: As the central component of rotating machine, the performance reliability assessment and remaining useful lifetime prediction of bearing are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability. A prognostic algorithm to assess the reliability and forecast the remaining useful lifetime (RUL) of bearings was proposed, consisting of three phases. Online vibration and temperature signals of bearings in normal state were measured during the manufacturing process and the most useful time-dependent features of vibration signals were extracted based on correlation analysis (feature selection step). Time series analysis based on neural network, as an identification model, was used to predict the features of bearing vibration signals at any horizons (feature prediction step). Furthermore, according to the features, degradation factor was defined. The proportional hazard model was generated to estimate the survival function and forecast the RUL of the bearing (RUL prediction step). The positive results show that the plausibility and effectiveness of the proposed approach can facilitate bearing reliability estimation and RUL prediction.
WANG Lu(王鹭), ZHANG Li(张利), WANG Xue-zhi(王学芝)
(School of Information, Liaoning University, Shenyang 110036, China)
Abstract:As the central component of rotating machine, the performance reliability assessment and remaining useful lifetime prediction of bearing are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability. A prognostic algorithm to assess the reliability and forecast the remaining useful lifetime (RUL) of bearings was proposed, consisting of three phases. Online vibration and temperature signals of bearings in normal state were measured during the manufacturing process and the most useful time-dependent features of vibration signals were extracted based on correlation analysis (feature selection step). Time series analysis based on neural network, as an identification model, was used to predict the features of bearing vibration signals at any horizons (feature prediction step). Furthermore, according to the features, degradation factor was defined. The proportional hazard model was generated to estimate the survival function and forecast the RUL of the bearing (RUL prediction step). The positive results show that the plausibility and effectiveness of the proposed approach can facilitate bearing reliability estimation and RUL prediction.
Key words:prognostics; reliability estimation; remaining useful life; proportional hazard model
