Attention-based Gate Recurrent Unit for remaining useful life prediction in prognostics

Lin, Ruiguan; Wang, Huawei; Xiong, Minglan; Hou, Zhaoguo; Che, Changchang

doi:10.1016/j.asoc.2023.110419

Cited by 15 publications

(4 citation statements)

References 42 publications

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“…The proposed DRLSTM-DA method is compared with eight SOTA RUL prediction methods proposed in the previous five years. These include DCNN [26], Deep bidirectional LSTM neural network(BiLSTM) [38], Multi-scale DCNN (MSDCNN) [10], ELSTMNN [12], Deep Bidirectional Recurrent Neural Networks(DBRNN) [11], BiGRU-TSAM [22], Attentionbased Gate Recurrent Unit(Attention-GRU) [39], Attention-LSTM [40]. Each experiment is repeated 10 times, and the final result is the average of these repetitions as shown in table 4.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

DRLSTM-DA: a novel aeroengine remaining useful life prediction method based on deep optimization of multiple sensors information

Suo,

Fu,

Lin

et al. 2024

Meas. Sci. Technol.

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One of the crucial tasks in Remaining useful life prediction is to effectively extract key sensor information from numerous sensor signals. In this article, a novel dual-attention enhanced deep residual LSTM (DRLSTM-DA) is developed to deeply optimize multidimensional sensor signals. First, two LSTM layers are designed to compress and reconstruct representative degradation information from input multidimensional time series data, to generate a new feature space. Second, a novel channel adaptive soft threshold module is designed to assign different weights according to the importance of different sensor information, and simultaneously eliminate the noise information in the signal. Thirdly, a temporal attention mechanism is designed to automatically highlight moments containing important decay information while suppressing unimportant moments. Finally, compared with other SOTA methods, our architecture achieves RMSE of 11.55, 13.74, 11.25, and 14.19 on four sub-datasets of the C-MAPSS dataset, with scores of 234.24, 465.49, 202.23, and 537.66, respectively. Meanwhile, in real aeroengine operation dataset, our architecture achieved the smallest RMSE (8.62). These results all validate the good predictive performance of our model.

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Section: Resultsmentioning

confidence: 99%

“…discussion. To underscore the superiority of DRLSTM-DA, predictions are compared with four SOTA methods, namely MS-DCNN [10], Attention-GRU [39], and Attention-LSTM [40]. Table 6 presents the prediction outcomes of DRLSTM-DA alongside the four SOTA methods using real case datasets.…”

Section: Prognostics and Detection Results Comparison Andmentioning

confidence: 99%

DRLSTM-DA: a novel aeroengine remaining useful life prediction method based on deep optimization of multiple sensors information

Suo,

Fu,

Lin

et al. 2024

Meas. Sci. Technol.

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“…Zhang et al [28] proposed a remaining useful life prediction model based on a transformer, which can simultaneously extract features of different sensors and time steps in parallel and finally verify the model performance using turbofan engine data sets. Lin et al [29] proposed an attention-based gated recurrent unit neural network model to effectively use feature information to predict the remaining useful life of equipment. Liu et al [30] proposed an enhanced encoderdecoder framework, which inputs the time series feature data into the encoder-decoder network model based on LSTM and calculates the RUL value at the end of the acquired signal combined with the linear regression algorithm of the output layer.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

Cao,

Zhang,

Zhao

et al. 2024

Applied Sciences

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For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction of key features. The framework of RUL prediction for rolling bearing is established by integrating multi-domain mixed features and temporal convolutional network (TCN). The variational mode decomposition method based on the dung beetle optimization algorithm is developed to reduce signal noise by determining the optimal parameters adaptively. To construct a health indicator of rolling bearing effectively, an isometric feature mapping algorithm is introduced to reduce the dimensionality of multi-domain mixed features, integrating time-domain, frequency-domain, and entropy features of vibration signals under non-stationary and nonlinear conditions. By considering the advantages of a multi-head attention mechanism (MA) and bidirectional gated recurrent unit (BiGRU), a TCN-based multi-head attention and bidirectional gate (TCNMABG) is developed to predict the RUL of rolling bearing accurately, whose detailed implementation process of TCNMABG is described based on XJTU-SY dataset. To verify the performance of TCNMABG, the FEMTO-ST dataset is introduced to perform the numerical experiments, and the results show that prediction error is reduced by 65.96% on average.

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“…Therefore, in recent years, RNN-based methods and their combination methods with CNN have emerged in the field of RUL prediction. [12][13][14][15] Wilberforce et al studied a method combining CNN and RNN for predicting RUL in cells and developed a dropout method combined with callback techniques to reduce the risk of overfitting. 16 Thakkar et al proposed an RUL estimation technique based on deep RNN to calculate the RUL of turbofan engines based on complete historical data.…”

Section: Introductionmentioning

confidence: 99%

An unsupervised subdomain adversarial network for remaining useful life estimation under various conditions

Wen,

Lyu,

Chen

2023

Quality & Reliability Eng

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Domain adaptation is a transfer learning method that is widely applied for remaining useful life (RUL) prediction under different operating conditions. However, current RUL prediction methods based on domain adaptation only consider minimizing degradation feature differences, while ignoring the correlation between features and labels, which may result in significant errors in the mapping relationship between the two learned by the model, and thus reduce the accuracy of prediction. To address this issue, this paper proposes an unsupervised subdomain adversarial network (USDAN) for RUL estimation under various conditions. On the basis of traditional domain adversarial adaptation networks, the network uses temporal convolutional networks to construct the feature extractor to learn the long‐term dependencies between degradation data, and a multilinear conditioning scheme is designed to combine the features and subdomain labels as input features to the domain discriminator. Finally, with backpropagation and gradient reversal, the feature extractor is prompted to generate cross‐domain invariant features that are correlated with the labels, improving the prediction accuracy and enhancing the transferability of the network. In the experimental section, the public turbofan engine degradation dataset is used to validate the effectiveness of the network, and the comparison with other approaches demonstrates its advantages.

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Attention-based Gate Recurrent Unit for remaining useful life prediction in prognostics

Cited by 15 publications

References 42 publications

DRLSTM-DA: a novel aeroengine remaining useful life prediction method based on deep optimization of multiple sensors information

DRLSTM-DA: a novel aeroengine remaining useful life prediction method based on deep optimization of multiple sensors information

Remaining Useful Life Prediction of Rolling Bearing Based on Multi-Domain Mixed Features and Temporal Convolutional Networks

An unsupervised subdomain adversarial network for remaining useful life estimation under various conditions

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