Journal
ALEXANDRIA ENGINEERING JOURNAL
Volume 59, Issue 6, Pages 4463-4473Publisher
ELSEVIER
DOI: 10.1016/j.aej.2020.07.052
Keywords
Axial piston pump; Cavitation; Vibration; 1-D convolutional neural network; Multi-channel inputs; Anti-noise
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Funding
- National Key R&D Program of China [2017YFD0700602]
- China Postdoctoral Science Foundation [2019M660086]
- Common Technology for Equipment Pre-research Project [41402050202]
- Science and Technology Planning Project of Guangdong Province [2017B090914002]
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Raising rotational speed is an effective way to improve power density of axial piston pumps, but high rotational speed tends to cause undesirable cavitation in the pump. Although some machine learning methods have been successfully applied to detect the cavitation with high accuracy, these conventional methods suffer from the drawback of time-consuming and experience-dependent manual feature extraction. In this paper, a new model based on 1-D convolutional neural network (CNN) is proposed to recognize the cavitation intensity of axial piston pumps. To improve the recognition accuracy under noisy environment, the 1-D CNN receives multi-channel vibration data instead of single-channel data. The experimental results show that the proposed anti-noise 1-D CNN model with multi-channel inputs can achieve 15% higher recognition accuracy than its counterpart with single-channel input on a testing set with SNR = 5 dB. (C) 2020 The Authors. Published by Elsevier B.V.
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