scholarly journals Lubricating Oil Remaining Useful Life Prediction Using Multi-Output Gaussian Process Regression

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 128897-128907
Author(s):  
Monika Tanwar ◽  
Nagarajan Raghavan
Keyword(s):  
Gaussian Process ◽  
Life Prediction ◽  
Gaussian Process Regression ◽  
Remaining Useful Life ◽  
Lubricating Oil ◽  
Useful Life

Parameter estimation and remaining useful life prediction of lubricating oil with HMM

Wear ◽  
2017 ◽  
Vol 376-377 ◽  
pp. 1227-1233 ◽  
Author(s):  
Ying Du ◽  
Tonghai Wu ◽  
Viliam Makis
Keyword(s):  
Parameter Estimation ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Lubricating Oil ◽  
Useful Life

scholarly journals Gaussian Process based Remaining useful life Prediction for Electric Energy Metering Equipment

2021 ◽  
Vol 2125 (1) ◽  
pp. 012032
Author(s):  
Ning Li ◽  
Junfeng Duan ◽  
Jun Ma ◽  
Wei Qiu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Extreme Environments ◽  
Electric Energy ◽  
Gaussian Process Regression ◽  
Remaining Useful Life ◽  
Probability Density Estimation ◽  
Gauss Kernel ◽  
Markov Chain Method ◽  
Energy Metering ◽  
Useful Life

Abstract Electric energy metering equipment (EEME) will fail in advance not as designed running in extreme environments. A multi-kernel Gaussian process regression model using measurement error data to perceive remaining useful life (RUL) for EEME is proposed. Firstly, the gauss kernel and periodic kernel are used to match the health index trend of EEME under a variety of typical environmental stresses. Furthermore, the Bayesian method and Monte Carlo Markov chain method are used to solve the model, and the Weibull distribution is used to fit the posterior trajectory to get the probability density estimation of the RUL.

Remaining useful life prediction of lubricating oil with dynamic principal component analysis and proportional hazards model

2019 ◽  
Vol 234 (6) ◽  
pp. 964-971
Author(s):  
Ying Du ◽  
Tonghai Wu ◽  
Shengxi Zhou ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Life Prediction ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Lubricating Oil ◽  
Hazards Model ◽  
Dynamic Principal Component Analysis ◽  
Useful Life

Lubricating oil contains a lot of tribological information of the machine and plays an important role in machine health. Oil degrades with serving time and causes severe wear afterwards, which is a complex dynamic process, and difficult to be accurately described by a single property. Therefore, the main purpose of deterioration prediction is to estimate the remaining useful life that the oil can still fulfill its functions by analyzing oil condition monitoring data. With a large amount of oil condition monitoring data collected, a vector autoregressive model is applied to the original oil data to describe the dynamic deterioration process. Then dynamic principal component analysis, an effective dimensionality reduction method, is employed to obtain the principal components capturing the most information of the oil data. The proportional hazards model is then built to calculate the failure risk of the lubricating oil based on the condition monitoring information, where its baseline function represents the aging process assuming to follow the Weibull distribution and its positive link function represents the influence of covariates (the principal components) on the failure risk. Finally, the remaining useful life prediction of lubricating oil can be obtained by explicit formulas of the characteristics such as the conditional reliability function and the mean residual life function. This work provides an approach to assess the health of lubricating oil, and a guidance for oil maintenance strategy.

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