Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods

Remaining useful life (RUL) prediction is central to the prognostics and health management (PHM) of lithium-ion batteries. This paper proposes a novel RUL prediction method for lithium-ion batteries based on the Wiener process with measurement error (WPME). First, we use the truncated normal distribution (TND) based modeling approach for the estimated degradation state and obtain an exact and closed-form RUL distribution by simultaneously considering the measurement uncertainty and the distribution of the estimated drift parameter. Then, the traditional maximum likelihood estimation (MLE) method for population based parameters estimation is remedied to improve the estimation efficiency. Additionally, we analyze the relationship between the classic MLE method and the combination of the Bayesian updating algorithm and the expectation maximization algorithm for the real time RUL prediction. Interestingly, it is found that the result of the combination algorithm is equal to the classic MLE method. Inspired by this observation, a heuristic algorithm for the real time parameters updating is presented. Finally, numerical examples and a case study of lithium-ion batteries are provided to substantiate the superiority of the proposed RUL prediction method.

https://www.mdpi.com/1996-1073/7/2/520/pdf

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

Remaining Useful Life Prediction of Lithium-ion Batteries Based on Wiener Process Under Time-Varying Temperature Condition

Accelerated degradation tests (ADT) modeling is an important issue in lifetime assessment to the products with high reliability and long lifetime. Among the literature about the accelerated nonlinear degradation process modeling, the current methods did not consider the product-to-product variation of the products with the same type. Therefore, this paper proposes an accelerated degradation process modeling method with random effects for the nonlinear Wiener process. Firstly, we derive the lifetime distribution of the nonlinear Wiener process with random effects. Secondly, the nonlinear Wiener process is used to model the degradation process of a single stress, and the drift coefficient is considered as a random variable to describe the product-to-product variation. Using the random acceleration model, the random effects are incorporated into the constant stress ADT models and the step stress ADT models. Then, a two-step maximum likelihood estimation (MLE) method is presented to estimate the unknown parameters in the degradation models. Finally, a simulation study and a case study are provided to demonstrate the application and superiority of the proposed model.

https://downloads.hindawi.com/journals/mpe/2014/560726.pdf

Accelerated Degradation Tests Modeling Based on the Nonlinear Wiener Process with Random Effects

Degradation data analysis based on a generalized Wiener process subject to measurement error

Real time remaining useful life (RUL) prediction based on condition monitoring is an essential part in condition based maintenance (CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item’s individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

In order to increase the linear CCD light-spot location accuracy and strengthen the ant-noise ability of centroid algorithm, an improved algorithm is presented. According to the symmetry distribution and high gray value of the object region, squared weight method and linear interpolation are used to enhance the effect of object region. Meanwhile, linear interpolation and iteration algorithm are utilized to improve the resolution and accuracy on signal segmentation. The simulation result shows that it is more accurate and stable than other sub-pixel algorithm, and the satisfying result is obtained.

Zhao-fa Zhou

Papers

Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Improved iteration centroid algorithm based on linear CCD light-spot location