Remaining Useful Life Prognostics of Bearings Based on a Novel Spatial Graph-Temporal Convolution Network

Li, Peihong; Liu, Xiaozhi; Yang, Ying

doi:10.3390/s21124217

Cited by 20 publications

(5 citation statements)

References 45 publications

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“…The model achieved the lowest RMSE of 8.8 on the PRONOSTIA dataset. He et al [22] and Li et al [23] obtained excellent results in predicting the RUL of rotating machinery. However, the manual feature selection required sufficient expert knowledge, and some data information was lost during feature selection, reducing the accuracy of RUL prediction.…”

Section: Introductionmentioning

confidence: 98%

“…With a 25% overlapping sampling size, the framework obtained an RMSE of 0.5875 on the IMS bearing dataset and RMSEs of 0.1118 and 0.1692 on two wind turbine datasets. Li et al [23] proposed a prediction model to predict the RUL of bearing by combining a TCN and graph convolutional network. Ten manually selected features were extracted from the raw data as the model's input.…”

Section: Introductionmentioning

confidence: 99%

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Remaining useful life prediction of bearings based on self-attention mechanism, multi-scale dilated causal convolution, and temporal convolution network

Wei¹,

Zhang²,

Gu³

2023

Meas. Sci. Technol.

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Effective remaining useful life (RUL) prediction of bearings is essential for the predictive maintenance of rotating machinery. However, the effectiveness of many existing RUL prediction methods depends on expert experience and signal processing algorithms, which limiting the application of these methods in real-life scenarios. This study proposes a novel end-to-end deep learning framework consisting of a multi-scale attention-based dilated causal convolutional (MADCC) module and a multi-layer temporal convolutional network (MTCN) to predict the RUL of bearings using raw vibration data. First, the MADCC module extracts multi-scale temporal features of the bearing degradation process (BDP) and provides fused feature vectors containing comprehensive BDP information for the MTCN module. Subsequently, the MTCN module mines deep temporal dependencies hidden in the fused feature vector to predict the RUL of bearings. Ablation experiments are conducted to analyze the contribution of the framework’s components. Three evaluation metrics (root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2)) are used to verify the effectiveness of the proposed framework and other state-of-the-art methods on two public bearing datasets. The experimental results show that the proposed framework achieves the lowest RMSE and MAE and the highest R2, demonstrating excellent performance and potential for RUL prediction of bearings.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of bearings based on self-attention mechanism, multi-scale dilated causal convolution, and temporal convolution network

Wei¹,

Zhang²,

Gu³

2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…This method is evaluated on a different dataset than the one presented here, but points towards the importance of interpretable models. Finally, more advanced techniques such as graphical convolutional networks have been used as feature extractors and combined with a temporal CNN for RUL prediction [18]. This last approach is highly relevant as it shows how intermediate feature mapping proves beneficial for RUL models.…”

Section: Related Workmentioning

confidence: 99%

A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

Avendano

Vandermoortele

Soete

et al. 2022

Sensors

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Remaining useful life is of great value in the industry and is a key component of Prognostics and Health Management (PHM) in the context of the Predictive Maintenance (PdM) strategy. Accurate estimation of the remaining useful life (RUL) is helpful for optimizing maintenance schedules, obtaining insights into the component degradation, and avoiding unexpected breakdowns. This paper presents a methodology for creating health index models with monotonicity in a semi-supervised approach. The health indexes are then used for enhancing remaining useful life estimation models. The methodology is evaluated on two bearing datasets. Results demonstrate the advantage of using the monotonic health index for obtaining insights into the bearing degradation and for remaining useful life estimation.

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“…To avoid this situation, it is desirable to fnd a new method to automatically extract degradation feature from monitoring data. Terefore, deep learning-based RUL prediction methods have gained more and more attention in the feld of data-driven RUL prediction [11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN

Liu

Jiang

et al. 2023

Shock and Vibration

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Remaining useful life prediction of a milling tool is one of the determinants in making scientific maintenance decision for the CNC machine tool. Predicting the RUL accurately can improve machining efficiency and the quality of product. Deep learning methods have strong learning capability in RUL prediction and are extensively used. Multiscale CNN, a typical deep learning model in RUL prediction, has a large number of parameters because of its parallel convolutional pathways, resulting in high computing cost. Besides, the MSCNN ignores various influences of different scales of degradation features on RUL prediction accuracy. To address the issue, a pyramid CNN (PCNN) is proposed for RUL prediction of the milling tool in this paper. Group convolution is used to replace parallel convolutional pathways to extract multiscale features without additional large number of parameters. And the channel attention with soft assignment is used to select the key degradation features, considering different sensors and scales. The milling tool wear experiments show that the score value of the proposed method achieved 51.248 ± 1.712 and the RMSE achieved 19.051 ± 0.804, confirming better performance of the proposed method compared with the traditional MSCNN and other deep learning methods. Besides, the number of parameters of the proposed method is reduced by 62.6% and 54.8% compared with the MSCNN with self-attention and the MSCNN methods, confirming its lower computing cost.

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Remaining Useful Life Prognostics of Bearings Based on a Novel Spatial Graph-Temporal Convolution Network

Cited by 20 publications

References 45 publications

Remaining useful life prediction of bearings based on self-attention mechanism, multi-scale dilated causal convolution, and temporal convolution network

Remaining useful life prediction of bearings based on self-attention mechanism, multi-scale dilated causal convolution, and temporal convolution network

A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN

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