A milling cutter state recognition method based on multi-source heterogeneous data fusion

Liu, Weijun; Tian, Zhiqiang; Jiang, Xingyu; Liu, Shun; Zhao, Baohai; Han, Qin; Li, Jiazhen; Deng, Jiushuai

doi:10.1007/s00170-022-10017-5

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Cited by 4 publications

(1 citation statement)

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“…Then a large feature pool is extracted from the sensing signal, and the feature subset is selected, which is then used by the ML based classification model. Use experimental data to train, verify and test various classification models [2].…”

Section: Introductionmentioning

confidence: 99%

Safety Monitoring Method of Underground Pipeline based on Machine Learning and Parameter Statistics

2021

KME

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Safety is the primary prerequisite for the development of a city. Underground pipelines (UP) carry the development of a city. The research on the SM methods of UP has become a hot topic. This paper combines machine learning (ML) and parameter statistics (PS) to study and analyze the new method of underground pipeline SM. This paper briefly analyzes the layout principle of urban UP and the SM system of the spatial layout of UP, discusses the safety evaluation indexes of UP, combines ML and PS, and studies the new method of SM with bending stress as the most critical safety evaluation index of pipelines; At last, the experimental results show that the new method of merging ML and parameter statistical security monitoring proposed in this paper is effective and feasible.

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Section: Introductionmentioning

confidence: 99%

Safety Monitoring Method of Underground Pipeline based on Machine Learning and Parameter Statistics

2021

KME

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Pattern recognition of control charts based on data feature enhancement and ensemble learning of classifiers for dimensional accuracy of products

Chu,

Dong,

Cheng

et al. 2024

International Journal of Production Research

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A Vector Splicing-Based Multi-Source Heterogeneous Data Fusion For Quality Fluctuation Characterization

Hu,

Zhang,

2023

2023 China Automation Congress (CAC)

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A milling cutter state recognition method based on multi-source heterogeneous data fusion

Cited by 4 publications

References 27 publications

Safety Monitoring Method of Underground Pipeline based on Machine Learning and Parameter Statistics

Safety Monitoring Method of Underground Pipeline based on Machine Learning and Parameter Statistics

Pattern recognition of control charts based on data feature enhancement and ensemble learning of classifiers for dimensional accuracy of products

A Vector Splicing-Based Multi-Source Heterogeneous Data Fusion For Quality Fluctuation Characterization

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