Cavitation fault diagnosis method for high-speed plunger pumps based on LSTM and CNN

WEI Xiaoliang; CHAO Qun; TAO Jianfeng; LIU Chengliang; WANG Liyao

doi:10.7527/S1000-6893.2020.23876

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2021 , Vol. 42 >Issue 3: 423876 - 423876

DOI: https://doi.org/10.7527/S1000-6893.2020.23876

Material Engineering and Mechanical Manufacturing

Cavitation fault diagnosis method for high-speed plunger pumps based on LSTM and CNN

WEI Xiaoliang ,
CHAO Qun ,
TAO Jianfeng ,
LIU Chengliang ,
WANG Liyao

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State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2020-02-14

Revised date: 2020-02-29

Online published: 2020-05-21

Supported by

National Key R&D Program of China (2017YFD0700602); China Postdoctoral Science Foundation (2019M660086)

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Abstract

Aiming at the cavitation of high-speed axial piston pumps and the drawbacks of conventional cavitation diagnosis methods such as relying on manual feature extraction and lack of robustness, this paper proposes a new method based on the combination of Long Short-Term Memory (LSTM) and one-Dimensional Convolutional Neural Network (1D-CNN). A test bench for axial piston pumps is built to collect the vibration signals of the pump housing at different cavitation levels. The classification network constructed by LSTM and CNN is used to identify the cavitation levels based on the vibration signals under different inlet pressure conditions. The experimental results show that the proposed method can accurately identify four different types of cavitation levels and that the accuracy rate can reach 99.5%. Furthermore, it has good robustness without the contribution of a noise reduction method. In the case of 0 dB signal to noise rate, the recognition accuracy is up to 87.3%.

Key words： high-speed axial piston pumps; cavitation intensity identification; long short-term memory (LSTM); convolutional neural network (CNN); fault diagnosis

Cite this article

WEI Xiaoliang , CHAO Qun , TAO Jianfeng , LIU Chengliang , WANG Liyao . Cavitation fault diagnosis method for high-speed plunger pumps based on LSTM and CNN[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(3) : 423876 -423876 . DOI: 10.7527/S1000-6893.2020.23876

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