The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data

Cheng, Huang; Kong, Xianwen; Wang, Qibin; Ma, Hongbo; Yang, Shengkang

doi:10.1016/j.ress.2022.108581

Cited by 41 publications

(8 citation statements)

References 28 publications

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“…Ding et al [12] introduced deep convolutional neural network (DCNN) to perform RUL prediction by extracting frequency domain features. Cheng et al [13] presented two-stage RUL prediction through using deep transfer learning (DTL) network according to insufficient degraded dataset. Huang et al [14] employed the DCNN-MLP model for extracting 1D sequence and 2D image characteristics at the same time, and the RULs for bearings were forecasted.…”

Section: Introductionmentioning

confidence: 99%

A novel vision transformer network for rolling bearing remaining useful life prediction

Hu,

Zhu,

Liu

et al. 2023

Meas. Sci. Technol.

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The accurate predictions of remaining useful life (RUL) have become a key and extremely challenging problem. Due to the limitations of the classical convolutional neural network (CNN) and recurrent neural network (RNN) structure, the attention mechanism has been introduced to improve feature representation of the long-term bearing degradation data. Transformer network based on attention mechanism is successfully applied in many fields and recognized as an excellent creation for deep learning models. In this paper, a novel lightweight mobile vision transformer (MobileViT) architecture based on deep networks is proposed for the RUL predictions. This new network is named prognostics separable vision Transformer (ProgSViT), which combines the separable convolution and MobileViT. In ProgSViT network, the separable convolutions are first constructed for extracting local feature from the input vibration signal, and the new vision transformer architecture is proposed to learn the global feature representations. In improved MobileViT model, the loss function is optimized, and a new training strategy is provided. Finally, the obtained features are input to the global average pool layers and the full connection layers to perform RUL estimation. Experiment results present the proposed ProgSViT network surpasses the other models in RUL predicting, which possesses higher precision and computational efficiency.

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Section: Introductionmentioning

confidence: 99%

A novel vision transformer network for rolling bearing remaining useful life prediction

Hu,

Zhu,

Liu

et al. 2023

Meas. Sci. Technol.

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“…Wang et al 20 proposed an end-to-end RUL prediction approach for the rapid ultracapacitor based on the convolutional neural network (CNN). Cheng et al 21 developed a two-stage RUL prediction strategy by utilizing deep transfer learning with insufficient performance degradation information. Although these artificial intelligence-based approaches have caused extensive concern and achieved certain effects, it is difficult to obtain convincing prediction results for performance degradation patterns outside the scope of the training model database.…”

Section: Introductionmentioning

confidence: 99%

Performance degradation assessment for mechanical system based on semi-analytical solution of self-similar stable distribution process

et al. 2023

Structural Health Monitoring

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To more accurately predict remaining useful life (RUL) and quantitatively evaluate the uncertainty of the predicted results, a performance degradation assessment framework based on semi-analytical solution of self-similar stable distribution process is proposed. The established performance degradation model based on adaptive fractional Lévy stable motion (AFLSM) is more flexible in revealing the long-range dependence, non-Gaussian, and heavy-tailed distribution properties of the incremental behavior. The corresponding stable distribution parameters are estimated through characteristic function method, and Hurst exponent is calculated based on the generalized Hurst exponent approach with narrower confidence interval. Aiming at the difficulties in solving the exact analytical solution and the excessive computation of the numerical solution in the whole process, based on Mellin-Stieltjes transform and direct integration, a semi-analytical solution of RUL distribution function is proposed, which can be readily implemented in practical equipment operations. The proposed performance degradation assessment framework is validated by the novel truck transmission dataset and the benchmark rolling bearing dataset. Experimental results indicate that the developed framework is more effective and superior than other state-of-the-art approaches in terms of RUL prediction.

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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%

“…Because of the remarkable ability of extracting degradation features from monitoring data, CNN-based RUL prediction methods become a research hotspot, especially the multiscale CNN (MSCNN) [31][32][33][34][35][36][37][38][39]. Te architecture of traditional MSCNN with self-attention is shown in Figure 1.…”

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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The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data

Cited by 41 publications

References 28 publications

A novel vision transformer network for rolling bearing remaining useful life prediction

A novel vision transformer network for rolling bearing remaining useful life prediction

Performance degradation assessment for mechanical system based on semi-analytical solution of self-similar stable distribution process

Remaining Useful Life Prediction of Milling Tool Based on Pyramid CNN

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