Remaining useful life estimation methods for predictive maintenance models: defining intervals and strategies for incomplete data

2018 ◽  
Author(s):  
A. Delmas ◽  
M. Sallak ◽  
W. Schon ◽  
L. Zhao
Keyword(s):  
Incomplete Data ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Estimation Methods ◽  
Life Estimation ◽  
Useful Life

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

2021 ◽  
Vol 8 (2) ◽  
pp. 412-422
Author(s):  
Chuang Chen ◽  
Ningyun Lu ◽  
Bin Jiang ◽  
Cunsong Wang
Keyword(s):  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Risk Averse ◽  
Life Estimation ◽  
Useful Life

Lifetime Prediction Considering Nonlinearity in Degradation Progression

2019 ◽  
Author(s):  
Rosmawati Jihin ◽  
Dirk Söffker
Keyword(s):  
Real Time ◽  
Incomplete Data ◽  
Remaining Useful Life ◽  
Health State ◽  
Time Data ◽  
Lifetime Estimation ◽  
Degradation Model ◽  
Life Estimation ◽  
Useful Life ◽  
Real Time Application

Abstract In this paper, the establishment of a prognostic approach based on a nonlinear degradation model for reliability assessment focusing on health state and remaining useful life estimation is considered. A model able to describe the non-linearity of degradation to predict future damage progression for real-time application has to be defined. Real-time data are generated during operation, so incomplete data about failure and usage up to the end of life are expected. For the accurate prediction of system lifetime, estimation of future degradation from the point of assessment is required. At this point, the unavailable data are numerically calculated by integrating linearized gradients adaptively by considering nonlinearity in current degradation. The coefficients used to define future degradation gradients are identified according to different states assuming future linear degradation increments. These coefficients are determined using an optimization-based algorithm simultaneously with the calculation of consumed lifetime by extending the previously established state machine lifetime model. For performance evaluation of the approach, the effectiveness of predicting remaining useful life using tribological experiments data is investigated. The results show the potential of this approach to deal with nonlinearity in the degradation progression.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

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