Application of deep learning model based on transfer learning in activated sludge process for wastewater purification

Li, Xiaoyong; Dong, Jilan; Niu, Guoqiang; Chen, Zhenguo; Huang, Mingzhi

doi:10.1016/j.jwpe.2024.104902

Cited by 1 publication

(1 citation statement)

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“…It can capture the dependency relationships between different positions when processing sequence data, allowing the model to dynamically allocate attention weights to different positions [22]. This enables a better understanding of the important information within the sequence [23]. The calculation process structure is shown in Figure 2.…”

Section: Attention Mechanismmentioning

confidence: 99%

Application of Residual Structure Time Convolutional Network Based on Attention Mechanism in Remaining Useful Life Interval Prediction of Bearings

Zhang,

Zeng,

Fan

et al. 2024

Sensors

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In the context of Industry 4.0, bearings, as critical components of machinery, play a vital role in ensuring operational reliability. The detection of their health status is thus of paramount importance. Existing predictive models often focus on point predictions of bearing lifespan, lacking the ability to quantify uncertainty and having room for improvement in accuracy. To accurately predict the long-term remaining useful life (RUL) of bearings, a novel time convolutional network model with an attention mechanism-based soft thresholding decision residual structure for quantifying the lifespan interval of bearings, namely TCN-AM-GPR, is proposed. Firstly, a spatio-temporal graph is constructed from the bearing sensor signals as the input to the prediction model. Secondly, a residual structure based on a soft threshold decision with a self-attention mechanism is established to further suppress noise in the collected bearing lifespan signals. Thirdly, the extracted features pass through an interval quantization layer to obtain the RUL and its confidence interval of the bearings. The proposed methodology has been verified using the PHM2012 bearing dataset, and the comparison of simulation experiment results shows that TCN-AM-GPR achieved the best point prediction evaluation index, with a 2.17% improvement in R2 compared to the second-best performance from TCN-GPR. At the same time, it also has the best interval prediction comprehensive evaluation index, with a relative decrease of 16.73% in MWP compared to the second-best performance from TCN-GPR. The research results indicate that TCN-AM-GPR can ensure the accuracy of point estimates, while having superior advantages and practical significance in describing prediction uncertainty.

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Section: Attention Mechanismmentioning

confidence: 99%