Remaining Useful Life Estimation for Rolling Bearings Using MSGCNN-TR

Guo, Dong; Cao, Zhi; Fu, Hongyong; Li, Zhenxiang

doi:10.1109/jsen.2022.3221753

Cited by 29 publications

(10 citation statements)

References 34 publications

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“…Mo et al [28] proposed a gated convolutional unit-transformer (GCU-transformer), which embeds the CNN into the GRU for feature extraction and then sends it to the transformer. Guo et al [21] proposed multiscale gated CNN-transformer (MSGCNN-TR), which parallelizes the GRU with the CNN for feature extraction and then sends the features to the transformer for state estimation.…”

Section: Transformermentioning

confidence: 99%

“…Under the premise of the same case, we compare with some of the latest technologies on bearing RUL prediction, including Wang's [9] TCN, Ding's [27] convolutional transformer (COT), Mo's [28] GCU-transformer, Guo's [21] MSGCNN-TR, Jiang's [33] dual residual attention network (DRAN). TCN, COT, GCU-transformer and MSGCNN-TR have been introduced in the previous section.…”

Section: Case Analysismentioning

confidence: 99%

“…In the degradation process of bearings, global information refers to the information that reflects the overall behavior and regularity of the signal, while local information reflects the variation of the signal on a small scale. Both global and local modeling are necessary for accurate RUL prediction [21]. However, the transformer tends to acquire global information while ignoring local information [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Remaining useful life estimation of bearing using spatio-temporal convolutional transformer

Zhu,

Lyu,

Gao

et al. 2024

Meas. Sci. Technol.

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The maintenance costs, productivity, health, and safety of mechanical equipment all heavily rely on the remaining usable life (RUL) of the bearings. Bearing datasets from running to failure are often in the form of life cycle sequences, which contain information about potential degradation failures in both the long and short term. Recently, the Transformer has been widely used in the RUL field due to its ability to capture some of the degradation information of the bearing. However, the Transformer is weak in acquiring local information and fails to extract temporal features from the degradation process. To solve the above problems, this paper proposes a spatio-temporal convolutional Transformer (STCT) model, which mainly consists of the dual convolutional spatio-temporal network (DCSTN) and multi-scale Transformer (MST). It not only captures the degradation features of the bearings from the temporal and spatial perspectives but also enhances the ability of the Transformer to acquire local information. We propose DCSTN as a feature extraction module, and the proposed spatio-temporal attention can capture the relevant degradation state features at different moments. In addition, MST uses a new module of multi-scale dilated causal convolution combined with multi-head attention to realize the combination of global degradation information and local contextual information capturing ability. We demonstrate the effectiveness and sophistication of the STCT model by conducting comparative experiments with ablation experiments on publicly available datasets.

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Section: Transformermentioning

confidence: 99%

Section: Case Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remaining useful life estimation of bearing using spatio-temporal convolutional transformer

Zhu,

Lyu,

Gao

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…The RUL label in this study is defined as the running time of the bearing from the current time until it fails, which can be calculated from equation (28),…”

Section: Rmagrumentioning

confidence: 99%

“…As rolling bearing RUL prediction has strong temporal characteristics, the utilization of RNN in RUL prediction is widely prevalent [26,27]. Guo et al [28] proposed a predictive model based on multi-scale gated CNN and Transformer to address the rolling bearing RUL prediction problem. This method employs CNN and gated recurrent unit (GRU) for feature extraction, followed by prediction using a Transformer model.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for bearing based on automatic feature combination extraction and residual multi-Head attention GRU network

He,

Zhang,

Zhang

et al. 2023

Meas. Sci. Technol.

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Rolling bearings are indispensable parts in mechanical equipment, and predicting their remaining useful life is critical to normal operation and keep equipment in good repair. However, the complex characteristics of bearings make it difficult to describe their degradation characteristics. To address this issue, a novel method that combines an automatic feature combination extraction mechanism with a gated recurrent unit (GRU) network that has a residual multi-head attention mechanism for rolling bearing life prediction is proposed. Firstly, the automatic feature combination extraction mechanism is used to learn the degradation representation of the bearing vibration signal in the time domain, frequency domain, and time–frequency joint domain, and automatically extract the optimal bearing degradation feature combination. Then, the GRU network with residual multi-head attention mechanism is developed to weight and distinguish the learned degradation features, thereby improving the network’s attention to important bearing degradation features. In the end, the proposed method is validated on the prediction and the health management of systems dataset and compared to other advanced approaches. The experimental results show that the proposed method can effectively capture the complex and dynamic features of rolling bearings and has high accuracy and generalization ability in rolling bearing life prediction.

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A hybrid method combining Lévy process and neural network for predicting remaining useful life of rotating machinery

Lv,

Liu,

et al. 2024

Advanced Engineering Informatics

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Remaining Useful Life Estimation for Rolling Bearings Using MSGCNN-TR

Cited by 29 publications

References 34 publications

Remaining useful life estimation of bearing using spatio-temporal convolutional transformer

Remaining useful life estimation of bearing using spatio-temporal convolutional transformer

Remaining useful life prediction for bearing based on automatic feature combination extraction and residual multi-Head attention GRU network

A hybrid method combining Lévy process and neural network for predicting remaining useful life of rotating machinery

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