Piston pump fault diagnosis based on Siamese neural network with small samples

Aiming at the problems of low accuracy and under-fitting in current fault diagnosis methods for piston pumps based on deep neural networks with small samples, a new fault diagnosis method for piston pumps based on Siamese neural networks was proposed. A test bench for piston pumps was built to collect the vibration signals of the pump housing under different health states. The convolution layers and pooling layers were used to construct the Siamese sub network and adaptively extract low-dimensional features from the raw vibration signals. The similarity of the input sample pairs was determined by Euclidean distance to expand training samples, train the Siamese neural network model. And finally identify the health states on the testing dataset. Experimental results demonstrate that compared with traditional deep neural networks, the proposed method has higher diagnosis accuracy with small samples. In addition, data fusion experiments show that the proposed method can learn relevant fault information from signals in different channels, which can improve the accuracy of the fault diagnosis. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.