Bi-directional long short-term memory recurrent neural network with attention for stack voltage degradation from proton exchange membrane fuel cells

Wang, Fu‐Kwun; Mamo, Tadele; Cheng, Xiao‐Bin

doi:10.1016/j.jpowsour.2020.228170

Cited by 72 publications

(20 citation statements)

References 35 publications

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“…The CNN-BiLSTM algorithm is compared with SVM (Yu et al, 2020), Support Vector Machines-Radial Basis Function (SVM-RBF) (Sonal Singh and Kant, 2021), Extreme Learning Machine (ELM) (Wei et al, 2020), CNN (Wan et al, 2020), and BiLSTM (Wang F. K. et al, 2020) to verify the performance advantages of the CNN-BiLSTM algorithm reported here.…”

Section: Experimental Analysismentioning

confidence: 99%

Deep Learning Based Emotion Recognition and Visualization of Figural Representation

2022

Front. Psychol.

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This exploration aims to study the emotion recognition of speech and graphic visualization of expressions of learners under the intelligent learning environment of the Internet. After comparing the performance of several neural network algorithms related to deep learning, an improved convolution neural network-Bi-directional Long Short-Term Memory (CNN-BiLSTM) algorithm is proposed, and a simulation experiment is conducted to verify the performance of this algorithm. The experimental results indicate that the Accuracy of CNN-BiLSTM algorithm reported here reaches 98.75%, which is at least 3.15% higher than that of other algorithms. Besides, the Recall is at least 7.13% higher than that of other algorithms, and the recognition rate is not less than 90%. Evidently, the improved CNN-BiLSTM algorithm can achieve good recognition results, and provide significant experimental reference for research on learners’ emotion recognition and graphic visualization of expressions in an intelligent learning environment.

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Section: Experimental Analysismentioning

confidence: 99%

Deep Learning Based Emotion Recognition and Visualization of Figural Representation

2022

Front. Psychol.

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“…A common practice for selecting HI under constant load/operating conditions is to utilize the stack voltage. In fact, as demonstrated in [8,10,11,15,16,18], the stack voltage is suitable for prognostics of constant load FC.…”

Section: Introductionmentioning

confidence: 99%

“…Pure model-driven approaches are often accompanied by strong assumptions specific to FC technical parameters, which affect their generalization ability. Data-driven (such as [10][11][15][16][17]) and hybrid approaches (such as [7][8]13,[17][18][19]) have been receiving more attention. In the data-driven prognostics category, long and short-term memory networks (LSTM), in deep-learning framework, possess powerful time series prediction capabilities, and the PEMFC prognostics based on the LSTM framework have received much attention in recent years [10][11]15].…”

Section: Introductionmentioning

confidence: 99%

“…Data-driven (such as [10][11][15][16][17]) and hybrid approaches (such as [7][8]13,[17][18][19]) have been receiving more attention. In the data-driven prognostics category, long and short-term memory networks (LSTM), in deep-learning framework, possess powerful time series prediction capabilities, and the PEMFC prognostics based on the LSTM framework have received much attention in recent years [10][11]15]. However, the raw LSTM, called Vanilla-LSTM, strongly relies on the historical data (training set) and the precise prediction of the data beyond the training data range is considered as a hard task [20].…”

Section: Introductionmentioning

confidence: 99%

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Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

Wang

Outbib

et al. 2022

Applied Energy

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Fuel cell (FC) is a promising alternative energy source in a wide range of applications. Due to the unsatisfactory durability performance, FC has not yet been widely used. Prognostics and health management (PHM) has been demonstrated to be an effective solution to enhance the FC durability performance by predicting FC degradation characteristics and adopting health condition based control and maintenance. As the primary task of PHM, prognostics seeks to estimate the remaining useful life (RUL) of FC as early and accurately as possible. However, when FC faces dynamic operating conditions, its degradation characteristics are often hidden in the complex system dynamic behaviors, which makes prognostics challenging. To address this issue, a hybrid prognostics approach is proposed in this paper. Specifically, the health indicator of FC is extracted using a degradation behavior model and sliding-window model identification method. Subsequently, a symbolic-based long short-term memory networks (LSTM) is used to predict the health indicator degradation trend and estimate the RUL. The experimental and simulation results show that the proposed model is able to describe the dynamic behavior of the FC stack voltage and the extracted health indicator show a significant degradation trend. Moreover, health indicator prediction and RUL estimation performance can be improved by deploying the proposed symbolic-based LSTM prognostics model. The proposed approach provides a prognostic horizon approaching 50% of the FC life-cycle, and the average relative accuracy of estimated RUL is close to 90%.

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“…In [24], a RNN based on encoder-decoder framework with attention mechanism was proposed for bearing RUL estimation. Reference [34] combined BLSTM with attention mechanism to predict the voltage degradation of the proton exchange membrane fuel cells stack. These researches suggested that improved results can be obtained by using attention-based neural network model compared to other ordinary deep learning models.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation Combining Two-Step Maximal Information Coefficient and Temporal Convolutional Network With Attention Mechanism

Yalan

Yang

et al. 2021

IEEE Access

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Remaining useful life (RUL) estimation has received extensive attention in many fields, which provides improved decision-making for condition-based maintenance (CBM) and enhances the stability of engineered systems. However, the effective construction of the feature set and design of the high-accuracy prediction model are the main challenges in RUL estimation. Consequently, a novel RUL estimation method is proposed in this paper. Initially, to extract the deep characteristics of the raw sensor data, the modified ensemble empirical mode decomposition (MEEMD), combined with the correlation coefficient threshold to select the sensitive intrinsic mode function (IMF) components, is proposed to reconstruct more representative features. Additionally, maximal information coefficient-based two-step feature selection (TSMIC) method is used to select the optimal feature subset. Ultimately, temporal convolutional network with attention mechanism (TCNA) is proposed to capture long-term time-series information and achieves more precise prediction results. The proposed framework is verified through a case study on aircraft turbofan engines, and comparisons with other state-of-the-art methods are presented. The experimental results of the case study show the effectiveness and superiority of the proposed approach.

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Bi-directional long short-term memory recurrent neural network with attention for stack voltage degradation from proton exchange membrane fuel cells

Cited by 72 publications

References 35 publications

Deep Learning Based Emotion Recognition and Visualization of Figural Representation

Deep Learning Based Emotion Recognition and Visualization of Figural Representation

Symbolic deep learning based prognostics for dynamic operating proton exchange membrane fuel cells

Remaining Useful Life Estimation Combining Two-Step Maximal Information Coefficient and Temporal Convolutional Network With Attention Mechanism

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