Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques

被引:2
|
作者
Tan, Yangyang [1 ]
Wu, Guoying [2 ]
Qiu, Yanlin [3 ]
Fan, Honggang [4 ]
Wan, Jun [5 ]
机构
[1] Jiangxi Port & Waterway Construct Investment Grp C, Jian, Peoples R China
[2] China Water Resources Pearl River Planning Surveyi, Guangzhou, Peoples R China
[3] Shanghai Chongming Dist Miao Town Water Affairs Ma, Shanghai, Peoples R China
[4] Tsinghua Univ, Dept Energy & Power Engn, State Key Lab Hydrosci & Engn, Beijing, Peoples R China
[5] Jiangxi Jiepai Nav & Elect Hub Management Off, Yingtan, Peoples R China
基金
中国国家自然科学基金;
关键词
fault diagnosis; mixed-flow pump; deep learning; convolution neural network; cavitation condition;
D O I
10.3389/fenrg.2022.1109214
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Deep learning technique is an effective mean of processing complex data that has emerged in recent years, which has been applied to fault diagnosis of a wide range of equipment. In the present study, three types of deep learning techniques, namely, stacked autoencoder (SAE) network, long short term memory (LSTM) network, and convolutional neural network (CNN) are applied to fault diagnosis of a mixed-flow pump under cavitation conditions. Vibration signals of the mixed-flowed pump are collected from experiment measurements, and then employed as input datasets for deep learning networks. The operation status is clarified into normal, minor cavitation, and severe cavitation conditions according to visualized bubble density. The techniques of FFT and dropout algorithms are also applied to improve diagnosis accuracy. The results show that the diagnosis accuracy based on SAE and LSTM networks is lower than 50%, while is higher than 68% when using CNN. The maximum accuracy can reach 87.2% by mean of a combination of CNN, BN, MLP, and using frequency domain data by FFT as inputs, which validates the feasibility of applying CNN in mixed-flow pumps.
引用
收藏
页数:10
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