A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

Li, Bing; Wu, Kunping; Li, Pengcheng; Li, Meng

doi:10.3390/app12010432

Cited by 27 publications

(8 citation statements)

References 30 publications

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“…Meraghni et al [15] constructed a data-driven digital twin system for predicting the remaining service life of PEMFC. Long et al [16] established a gated cycle unit (GRU) neural network model for predicting the remaining service life of PEMFC. Ma et al [17] proposed a data fusion method based on long short-term memory (LSTM) and autoregressive integral moving average method to predict PEMFC performance.…”

Section: Introductionmentioning

confidence: 99%

Performance degradation trend prediction of proton exchange membrane fuel cell based on GA-TCN

Zhao,

Shen,

Wang

2024

Meas. Sci. Technol.

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To improve the prediction accuracy of the performance degradation trend of proton exchange membrane fuel cell (PEMFC), this paper proposes a temporal convolutional network (TCN) model based on genetic algorithm (GA) optimization to predict the performance degradation trend of PEMFC. Firstly, variational mode decomposition (VMD) and wavelet threshold denoising (WTD) algorithms are used to denoise the original data. Then the hyperparameters of the TCN model are optimized by GA, and the GA-TCN model for predicting the performance degradation trend of PEMFC is constructed. Finally, this paper uses the PEMFC stack degradation experimental dataset disclosed in the IEEE PHM 2014 Data Challenge to verify, and compares the proposed model with the backpropagation neural networks model (BP), the long short-term memory model (LSTM) and the classical temporal convolutional network model. The results show that the proposed method has the highest performance degradation trend prediction accuracy. In particular, when the training dataset accounts for 30%, i.e. the training samples are small, the root mean square error (RMSE) and mean absolute error (MAE) of the GA-TCN model are 0.004726 and 0.003119, respectively, which are 14.48% and 20.05% lower than that of the classical TCN model. Consequently, this methodology can forecast the degradation trend of PEMFC with high accuracy.

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Section: Introductionmentioning

confidence: 99%

Performance degradation trend prediction of proton exchange membrane fuel cell based on GA-TCN

Zhao,

Shen,

Wang

2024

Meas. Sci. Technol.

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“…5 In practice, the knowledge of the RUL allows for avoiding unscheduled shutdowns of productions, thus enhancing overall availability and safety while reducing the overall maintenance and operational expenses. In this regard, fault prognostics is of interest across multiple industries and various engineering applications, including automotive (Electric Vehicles (EVs), 6 lithium-ion batteries, 7,8 aluminium electrolytic capacitors [9][10][11][12] ), renewable energy systems (wind turbines, 13 Hydrogen fuel cells 14 ), nuclear power plants (seawater filter, 15 electric gate valves 16 ), aircrafts (engines 17,18 ) and several others. 2,19,20 Model-based and data-driven are two broad categories of methods for estimating the RUL of industrial equipment.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for remaining useful life prediction of high‐reliability equipment based on long short‐term memory and multi‐head self‐attention mechanism

Al‐Dahidi,

Rashed,

Abu‐Shams

et al. 2023

Quality & Reliability Eng

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Accurate prediction of the Remaining Useful Life (RUL) of components and systems is crucial for avoiding an unscheduled shutdown of production by planning maintenance interventions effectively in advance. For high‐reliability equipment, few complete‐run‐to‐failure trajectories may be available in practice. This constitutes a technical challenge for data‐driven techniques for estimating the RUL. This paper proposes a novel data‐driven approach for fault prognostics using the Long‐Short Term Memory (LSTM) model combined with the Multi‐Head Self‐Attention (MHSA) mechanism. The former is applied to the input signals, whereas the latter is used to extract features from the LSTM hidden states, benefiting from the information from all hidden states rather than utilizing that of the final hidden state only. The proposed approach is characterized by its capability to recognize long‐term dependencies while extracting features in both global and local contexts. This enables the approach to provide accurate RUL estimates in various stages of the equipment's life. The proposed approach is applied to an artificial case study simulated to mimic the realistic degradation behaviour of a heterogeneous fleet of aluminium electrolytic capacitors used in the automotive industry (under variable operating and environmental conditions). Results indicate that the proposed approach can provide accurate RUL estimates for high‐reliability equipment compared to four benchmark models from the literature.

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“…However, CNNs’ learning rules as originally proposed lack the element of adaptive learning. Contrariwise, LSTMs and their variants such as gated units are very popular adaptive learning and time series networks [ 19 ]. Unlike CNNs, their robustness resembled each other in considering the mathematical correlation between samples behaving similarly.…”

Section: Introductionmentioning

confidence: 99%

Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis

Berghout

Benbouzid

Bentrcia

et al. 2022

Entropy

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The green conversion of proton exchange membrane fuel cells (PEMFCs) has received particular attention in both stationary and transportation applications. However, the poor durability of PEMFC represents a major problem that hampers its commercial application since dynamic operating conditions, including physical deterioration, have a serious impact on the cell performance. Under these circumstances, prognosis and health management (PHM) plays an important role in prolonging durability and preventing damage propagation via the accurate planning of a condition-based maintenance (CBM) schedule. In this specific topic, health deterioration modeling with deep learning (DL) is the widely studied representation learning tool due to its adaptation ability to rapid changes in data complexity and drift. In this context, the present paper proposes an investigation of further deeper representations by exposing DL models themselves to recurrent expansion with multiple repeats. Such a recurrent expansion of DL (REDL) allows new, more meaningful representations to be explored by repeatedly using generated feature maps and responses to create new robust models. The proposed REDL, which is designed to be an adaptive learning algorithm, is tested on a PEMFC deterioration dataset and compared to its deep learning baseline version under time series analysis. Using multiple numeric and visual metrics, the results support the REDL learning scheme by showing promising performances.

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A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network

Cited by 27 publications

References 30 publications

Performance degradation trend prediction of proton exchange membrane fuel cell based on GA-TCN

Performance degradation trend prediction of proton exchange membrane fuel cell based on GA-TCN

A novel approach for remaining useful life prediction of high‐reliability equipment based on long short‐term memory and multi‐head self‐attention mechanism

Exposing Deep Representations to a Recurrent Expansion with Multiple Repeats for Fuel Cells Time Series Prognosis

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