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A novel data fusion based intelligent identification approach for working cycle stages of hydraulic excavators.

Publication ,  Journal Article
Song, H; Li, G; Xiong, X; Li, M; Qin, Q; Mitrouchev, P
Published in: ISA transactions
May 2024

Accurately identifying the stage of the excavator working cycle is the prerequisite to achieve the staged energy-saving control. However, current identification methods often overlook the influence of hydraulic system latency on identification results and depend on a single model, resulting in poor generalization performance of the identification approaches. Moreover, expert calibration system remains a necessary factor for improving identification accuracy. Aiming at these issues, a hybrid multi-scale feature extractor and a decision-level data fusion classifier approach (HMSFE-DFC) is proposed to identify the working cycle stages of excavator. The input signal employs mixed signals from the main pump pressure and the control current of the proportional solenoid valve to reduce the response delay caused by the single main pump pressure signal. A hybrid multi-scale feature extractor is constructed using a convolutional neural network temporal self-attention feature extraction mechanism and one-dimensional ResNet-50 architecture to extract multiscale features. To prevent overfitting, a decision-level data fusion classifier is used to fuse the decisions information of numerous classifiers. The accuracy of stage identification for 10 consecutive working cycles reaches 95.21%, which verifies its effectiveness.

Duke Scholars

Published In

ISA transactions

DOI

EISSN

1879-2022

ISSN

0019-0578

Publication Date

May 2024

Volume

148

Start / End Page

78 / 91

Related Subject Headings

  • Industrial Engineering & Automation
  • 4009 Electronics, sensors and digital hardware
  • 0910 Manufacturing Engineering
  • 0906 Electrical and Electronic Engineering
  • 0102 Applied Mathematics
 

Citation

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Song, H., Li, G., Xiong, X., Li, M., Qin, Q., & Mitrouchev, P. (2024). A novel data fusion based intelligent identification approach for working cycle stages of hydraulic excavators. ISA Transactions, 148, 78–91. https://doi.org/10.1016/j.isatra.2024.03.006
Song, Haoju, Guiqin Li, Xin Xiong, Ming Li, Qiang Qin, and Peter Mitrouchev. “A novel data fusion based intelligent identification approach for working cycle stages of hydraulic excavators.ISA Transactions 148 (May 2024): 78–91. https://doi.org/10.1016/j.isatra.2024.03.006.
Song H, Li G, Xiong X, Li M, Qin Q, Mitrouchev P. A novel data fusion based intelligent identification approach for working cycle stages of hydraulic excavators. ISA transactions. 2024 May;148:78–91.
Song, Haoju, et al. “A novel data fusion based intelligent identification approach for working cycle stages of hydraulic excavators.ISA Transactions, vol. 148, May 2024, pp. 78–91. Epmc, doi:10.1016/j.isatra.2024.03.006.
Song H, Li G, Xiong X, Li M, Qin Q, Mitrouchev P. A novel data fusion based intelligent identification approach for working cycle stages of hydraulic excavators. ISA transactions. 2024 May;148:78–91.
Journal cover image

Published In

ISA transactions

DOI

EISSN

1879-2022

ISSN

0019-0578

Publication Date

May 2024

Volume

148

Start / End Page

78 / 91

Related Subject Headings

  • Industrial Engineering & Automation
  • 4009 Electronics, sensors and digital hardware
  • 0910 Manufacturing Engineering
  • 0906 Electrical and Electronic Engineering
  • 0102 Applied Mathematics