Research and Application of Wear Prediction Method of NC Milling Cutter Based on Data-Driven

Liu, Zhenduo; Yang, Dongxu

doi:10.1109/ictech55460.2022.00101

Cited by 1 publication

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“…There are six datasets in PHM2010. Among them, c1, c4 and c6 are the training sets and the others are the test sets [30]. Each dataset corresponds to 315 mills, and the signals collected for each mill are 7 channels of data.…”

Section: Experimental Study B: Milling Cutters System Rul Prediction ...mentioning

confidence: 99%

A new method for remaining useful life prediction by implementing joint learning of sensor dynamic graph and spatio-temporal features

Lv¹,

Liu²

2023

Meas. Sci. Technol.

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Accurate implementation of remaining useful life (RUL) prediction of equipment is essential for health management and maintenance decisions. Advances in sensing and big data technologies have provided the possibility for research on data-driven life prediction methods. However, the current RUL prediction methods still need to improve in utilizing multi-source information. The present techniques consider more the relationship between the temporal information and RUL prediction among the multi-source information and rarely target the research and exploitation of the potential connection between sensor networks and RUL. Therefore, this paper proposes a spatio-temporal feature extraction network based on the sensor dynamic graph: DST-GT model. The method extracts the unidirectional relationship between sensors from monitoring data to construct a dynamic spatio-temporal graph reflecting the sensor relationship. The DST-GT model uses graph convolution based on message selection (MSGCN) to model the spatial dependencies of sensors, and uses multi-scale gated temporal convolution module (MGTCN) to model the temporal dependencies in sensor state monitoring data. In this paper, graph learning, graph convolution and temporal convolution modules are jointly learned in an end-to-end framework. The results on two widely used datasets and comparisons with other methods demonstrate the accuracy and advancement of DST-GT networks for RUL prediction.

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Section: Experimental Study B: Milling Cutters System Rul Prediction ...mentioning

confidence: 99%

A new method for remaining useful life prediction by implementing joint learning of sensor dynamic graph and spatio-temporal features

Lv¹,

Liu²

2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Research and Application of Wear Prediction Method of NC Milling Cutter Based on Data-Driven

Cited by 1 publication

References 0 publications

A new method for remaining useful life prediction by implementing joint learning of sensor dynamic graph and spatio-temporal features

A new method for remaining useful life prediction by implementing joint learning of sensor dynamic graph and spatio-temporal features

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