A Wiener-based degradation model with logistic distributed measurement errors and remaining useful life estimation

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.

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

Recent Patents on Engineering ◽

10.2174/1872212114999200423115526 ◽

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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Distance-Based Embedding Learning for Remaining Useful Life Estimation

2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai) ◽

10.1109/phm-shanghai49105.2020.9280982 ◽

2020 ◽

Author(s):

Yini Zhang ◽

Yuanxiang Li ◽

Yuxuan Zhang ◽

Lei Jia

Keyword(s):

Remaining Useful Life ◽

Life Estimation ◽

Useful Life

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Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model

Applied Sciences ◽

10.3390/app11167175 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7175

Author(s):

Islem Bejaoui ◽

Dario Bruneo ◽

Maria Gabriella Xibilia

Keyword(s):

Principal Component Analysis ◽

Induction Motor ◽

Life Prediction ◽

Principal Component ◽

Component Analysis ◽

Remaining Useful Life ◽

Degradation Model ◽

Broken Rotor Bar ◽

Industrial Sectors ◽

Useful Life

Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.

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