Predicting maintenance through an attention long short-term memory projected model

Tseng, Shih-Hsien; Tran, Khoa-Dang

doi:10.1007/s10845-023-02077-5

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…The comparison results between our proposed method and these methods in terms of RMSE and Score metrics are listed in tables 6 and 7. It can be seen that the proposed method 4.14 SCTA-LSTM [37] 4.15 DLformer [44] 6.79 ALSTMP [45] 5.10 iSTLSTM (Proposed) 4.11…”

Section: Comparisons With Sota Methodsmentioning

confidence: 99%

“…Although the results of iSTLSTM are not optimal under a single operating condition, our method significantly outperforms other methods regardless of the average metric reflecting the comprehensive performance of the model or the typical operating conditions of the aircraft engines. On the N-CMAPSS dataset, we also compare with the related SOTA methods [27,37,[43][44][45] published in recent years. The results are presented in table 8.…”

Section: Comparisons With Sota Methodsmentioning

confidence: 99%

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An interpretable RUL prediction method of aircraft engines under complex operating conditions using spatio-temporal features

Gao,

Wang,

Sun

2024

Meas. Sci. Technol.

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Long short-term memory (LSTM) based prediction methods have achieved remarkable achievements in remaining useful life (RUL) prediction for aircraft engines. However, their prediction performance and interpretability are unsatisfactory under complex operating conditions. For aircraft engines with high hazard levels, it is important to ensure the interpretability of the models while maintaining excellent prediction accuracy. To address these issues, an interpretable RUL prediction method of aircraft engines under complex operating conditions using spatio-temporal features (STFs), referred to as iSTLSTM, is proposed in this paper. First, we develop a feature extraction framework called Bi-ConvLSTM1D. This framework can effectively capture the spatial and temporal dependencies of sensor measurements, significantly enhancing the feature extraction capabilities of LSTM. Then, an interpretation module for STFs based on a hybrid attention mechanism is designed to quantitatively assess the contribution of STFs and output interpretable RUL predictions. The effectiveness of iSTLSTM is evidenced by extensive experiments on the C-MAPSS and N-CMAPSS datasets, confirming the superiority and reliability of our method for aircraft engine RUL prediction.

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Section: Comparisons With Sota Methodsmentioning

confidence: 99%

Section: Comparisons With Sota Methodsmentioning

confidence: 99%

An interpretable RUL prediction method of aircraft engines under complex operating conditions using spatio-temporal features

Gao,

Wang,

Sun

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In this approach, an efficient and powerful AM was specifically designed to increase the estimation capabilities, which was relying on the analysis of the signal characteristics for the RUL prediction task. Tseng and Tran (2023) included the AM block into the LSTM structure, thus combining the information from the previously hidden layers and the input modules into the actual state to effectively discriminate which new feature needs to be added to the storage module.…”

Section: Rul Prediction Methodsmentioning

confidence: 99%

Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes

Simani¹,

Lam²,

Farsoni³

et al. 2023

JDSIS

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The prediction of remaining useful life is critical in predictive health management. This is done to reduce the expenses associated with operation and maintenance by avoiding errors and failures in dynamic processes. Recently, the abilities of feature classification and automated extraction of neural networks in its convolutional forms have shown fascinating performance when used for estimating the remaining useful life of dynamic processes using deep learning structures. This was accomplished by putting these talents to the task of predicting how long the procedures would be beneficial. Existing network topologies, on the other hand, virtually entirely extract features at a single scale while neglecting important information at other sizes. Meanwhile, because of the architecture of a single network path, the comprehensiveness of the features discovered by these tools is limited. To address these concerns, the authors propose a network structure based on a feature fusion strategy on a parallel multiscale architecture. This structure is then utilized to compute the remaining useful life. This prototype is divided into two sections: the first is a multiscale feature extraction module designed to extract local information features, and the second is a causal convolution module designed to extract global information features by combining multi-layer causal convolution with average pooling. The multiscale feature extraction module is intended for the extraction of local information features, while the causal convolution module is intended for the extraction of global information features. Finally, the two distinct paths are joined to create a fully integrated layer. The simulations and results show that this method has the potential to improve the efficiency and accuracy of estimating the remaining useful life index. Furthermore, the advantages of the established strategy are shown by comparing the results obtained with those produced by applying cutting-edge techniques on a well-known data-set depicting a simulated turbofan engine.

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Towards prognostic generalization: a domain conditional invariance and specificity disentanglement network for remaining useful life prediction

Xia,

Huang,

Qin

et al. 2023

J Intell Manuf

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Predicting maintenance through an attention long short-term memory projected model

Cited by 7 publications

References 49 publications

An interpretable RUL prediction method of aircraft engines under complex operating conditions using spatio-temporal features

An interpretable RUL prediction method of aircraft engines under complex operating conditions using spatio-temporal features

Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes

Towards prognostic generalization: a domain conditional invariance and specificity disentanglement network for remaining useful life prediction

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