Remaining Useful Life Prediction Using a Novel Two-Stage Wiener Process With Stage Correlation

Wang, Hongyu; Zhao, Yu; Ma, Xiaobing

doi:10.1109/access.2018.2877630

Cited by 23 publications

(26 citation statements)

References 36 publications

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“…A new feature extraction method was presented to obtain the spectrum-principal-energy-vector. To solve the problem that the two stages are mutually independent, Wang et al [21] proposed a new model with stage correlation for RUL prediction. Many other methods to predict bearing RUL using various feature extraction methods and DL architectures have been reported [22], [23].…”

Section: Introductionmentioning

confidence: 99%

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

2019

IEEE Access

193

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Accurate and timely prediction of remaining useful life (RUL) of a machine enables the machine to have an appropriate operation and maintenance decision. Data-driven RUL prediction methods are more attractive to researchers because they can be deployed quicker and cheaper compared to other approaches. The existing deep neural network (DNN) models proposed for the applications of RUL prediction are mostly single-path and top-down propagation. In order to improve the prognostic accuracy of the network, this paper proposes a directed acyclic graph (DAG) network that combines long short term memory (LSTM) and a convolutional neural network (CNN) to predict the RUL. Different from the existing prediction models combined with CNN and LSTM, the method proposed in this paper combines CNN and LSTM organically instead of just using CNN for feature extraction. Moreover, when a single timestamp is used as an input, padding the signals in the same training batch would affect the prediction ability of the developed model. To overcome this drawback, the proposed method generates a short-term sequence by sliding the time window (TW) with one step size. In addition, based on the degradation mechanism, the piece-wise RUL function is used instead of the traditional linear function. In the experimental test, the turbofan engine degradation simulation dataset provided by NASA is used to validate the proposed RUL prediction model. By comparing with the existing methods using the same dataset, it can be concluded that the prediction method proposed in this paper has better prediction capability. INDEX TERMS Remaining useful life prediction, long-short-term memory network, convolutional neural networks, turbofan engine.

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Section: Introductionmentioning

confidence: 99%

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

2019

IEEE Access

193

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“…Considering the nonlinear characteristic of the degradation process during the actual use for lithium-ion batteries, Si et al [46] predicted the RUL by establishing a nonlinear Wiener process to model the capacity degradation. Wang et al [47] predicted the RUL by developing a two-stage Wiener process method to model the degradation process for lithium-ion batteries. Feng et al [48] proposed a RUL prediction method for lithium-ion batteries based on a two-dimensional Wiener process.…”

Section: Literature Reviewmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

et al. 2019

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Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.

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“…In addition, when k is large, the determinant of the covariance matrix could be too small or too large, and thus produce an overflow error. As the logarithm of the determinant is only needed in the MLE, we can calculate the logarithm in (18). And thus the overflow error is removed.…”

Section: Parameters Estimation a Parameters Estimation For A Simentioning

confidence: 99%

“…Remark 3: In the program of the modified EM algorithm presented in this paper, we use the presented simple method to calculate the determinant and the inverse matrix of the covariance matrix as shown in (18) and (16). If the function library of Matlab is used directly, the Matlab software could output error message, i.e.…”

Section: A Numerical Examplementioning

confidence: 99%

A Note on Parameters Estimation for Nonlinear Wiener Processes With Measurement Errors

et al. 2019

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Remaining Useful Life Prediction Using a Novel Two-Stage Wiener Process With Stage Correlation

Cited by 23 publications

References 36 publications

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect

A Note on Parameters Estimation for Nonlinear Wiener Processes With Measurement Errors

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