Centrifugal pump fault diagnosis method based on EAS and stacked capsule autoencoder

Abstract

As one of the basic components in industrial systems, the safety and reliability of centrifugal pumps are directly related to production efficiency. This paper presents a fault diagnosis method of centrifugal pump based on EAS and stacked capsule autoencoder. First, use Electrical Signature Analysis (ESA) to select electrical signals as fault parameters for the fault data of the centrifugal pump; secondly, normalize the motor torque data of the six faults to the interval [0-255] and convert it into grayscale Images are input into the stacked capsule autoencoder network for fault diagnosis training, and selfattention-based pooling is used to reduce the number of capsules and increase the calculation speed. Train the Part Capsule Autoencoder (PCAE) to maximize the likelihood of the original image and the reconstructed image, and train the Object Capsule Autoencoder (OCAE) to maximize the likelihood of the original part and the mixed part, to obtain the optimal fault diagnosis model, and the classification accuracy of the optimized model is 96.57%. The method proposed in this paper solves the problems of complicated installation of fault signal sensors and poor generalization in fault diagnosis of centrifugal pump and improves the robustness and accuracy of fault diagnosis of centrifugal pump.