Nonlinear Degradation Process Modeling and Remaining Useful Life Estimation Subject to Measurement Error

In this paper, a new gamma-based degradation process with random effect is proposed that allows to account for the presence of measurement error that depends in stochastic sense on the measured degradation level. This new model extends a perturbed gamma model recently suggested in the literature, by allowing for the presence of a unit to unit variability. As the original one, the extended model is not mathematically tractable. The main features of the proposed model are illustrated. Maximum likelihood estimation of its parameters from perturbed degradation measurements is addressed. The likelihood function is formulated. Hence, a new maximization procedure that combines a particle filter and an expectation-maximization algorithm is suggested that allows to overcome the numerical issues posed by its direct maximization. Moreover, a simple algorithm based on the same particle filter method is also described that allows to compute the cumulative distribution function of the remaining useful life and the conditional probability density function of the hidden degradation level, given the past noisy measurements. Finally, two numerical applications are developed where the model parameters are estimated from two sets of perturbed degradation measurements of carbon-film resistors and fuel cell membranes. In the first example the presence of random effect is statistically significant while in the second example it is not significant. In the applications, the presence of random effect is checked via appropriate statistical procedures. In both the examples, the influence of accounting for the presence of random effect on the estimates of the cumulative distribution function of the remaining useful life of the considered units is also discussed. Obtained results demonstrate the affordability of the proposed approach and the usefulness of the proposed model.

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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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An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201965 ◽

2020 ◽

pp. 1-9

Author(s):

Yiming Guo ◽

Hui Zhang ◽

Zhijie Xia ◽

Chang Dong ◽

Zhisheng Zhang ◽

...

Keyword(s):

Neural Network ◽

Degradation Process ◽

Rolling Bearing ◽

Remaining Useful Life ◽

Convolution Neural Network ◽

Dual Channel ◽

Time Domain Data ◽

Channel Input ◽

Deep Convolution Neural Network ◽

Useful Life

The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.

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