A data-model-fusion prognostic framework for dynamic system state forecasting

Liu, J.; Wang, W.; Ma, Fengying; Yang, Yu-Bin; Yang, Chunsheng

doi:10.1016/j.engappai.2012.02.015

Cited by 166 publications

(72 citation statements)

References 34 publications

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“…To fully utilize the degradation data of congeneric batteries, He et al [18] applied the Dempster-Shafer theory to evaluate the initial model parameters. Other new prognostics-related methods and models, e.g., autoregressive model, Verhulst model, fusion prognostic algorithm, adaptive bathtub-shaped function, relevance vectors, etc., can be found in [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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Abstract: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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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“…Equation (12) can be regarded as an unconstrained quadratic programming. Adding the inequality constraint Equation (10) or Equation (11) to Equation (12), we can get a constrained quadratic programming. Equation (13) guarantees the monotonically increasing relationship between input and output units:…”

Section: En_monesn For Rul Estimationmentioning

confidence: 99%

“…Then the output unit y i and the input unit u j of ESN will have monotonic relationships by adding constrains [Equation (10) or Equation (11)] to the learning process.…”

Section: En_monesn For Rul Estimationmentioning

confidence: 99%

“…In order to achieve a fusion prognostic strategy, Kozlowski [10] proposes a data-driven RUL prediction approach by combining ARMA model, neural networks and fuzzy logic. Liu et al [11] develop a fusion prognostic framework to increase the system long-term prediction performance. An ensemble model for predicting the RUL of a lithium-ion battery is introduced by combining a fused empirical exponential and polynomial regression model and a PF algorithm [12].…”

Section: Introductionmentioning

confidence: 99%

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Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction

et al. 2013

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Prognostics and remaining useful life (RUL) estimation for lithium-ion batteries play an important role in intelligent battery management systems (BMS). The capacity is often used as the fade indicator for estimating the remaining cycle life of a lithium-ion battery. For spacecraft requiring high reliability and long lifetime, in-orbit RUL estimation and reliability verification on ground should be carefully addressed. However, it is quite challenging to monitor and estimate the capacity of a lithium-ion battery on-line in satellite applications. In this work, a novel health indicator (HI) is extracted from the operating parameters of a lithium-ion battery to quantify battery degradation. Moreover, the Grey Correlation Analysis (GCA) is utilized to evaluate the similarities between the extracted HI and the battery's capacity. The result illustrates the effectiveness of using this new HI for fading indication. Furthermore, we propose an optimized ensemble monotonic echo state networks (En_MONESN) algorithm, in which the monotonic constraint is introduced to improve the adaptivity of degradation trend estimation, and ensemble learning is integrated to achieve high stability and precision of RUL prediction. Experiments with actual testing data show the efficiency of our proposed method in RUL estimation and degradation modeling for the satellite lithium-ion battery application.

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“…Algorithms that have been used for measurement generation include LSSVM 73 and neural networks. 74 These models are trained to recursively estimate the future value of each variable.…”

Section: Particle Filtermentioning

confidence: 99%

Multidimensional prognostics for rotating machinery: A review

Duan

Mba

et al. 2017

Advances in Mechanical Engineering

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Determining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and availability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidimensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and drawbacks and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment.

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A data-model-fusion prognostic framework for dynamic system state forecasting

Cited by 166 publications

References 34 publications

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 the Wiener Process with Measurement Error

Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction

Multidimensional prognostics for rotating machinery: A review

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