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

Abstract

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.

A Review of Pump Cavitation Fault Detection Methods Based on Different Signals

“…The three deep learning techniques of SAE network, LSTM network, and CNN are used in the cavitation diagnosis for the axial flow pump, and the vibration signal is used as the input dataset of the three networks. The results show that the accuracy of CNN is much higher than that of the other two networks [58].…”