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7 March 2022 Centrifugal pump fault diagnosis method based on EAS and stacked capsule autoencoder
Zihan Chang, Wei Yuan, Menghong Yu
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Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 1216710 (2022) https://doi.org/10.1117/12.2628796
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China
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.
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Zihan Chang, Wei Yuan, and Menghong Yu "Centrifugal pump fault diagnosis method based on EAS and stacked capsule autoencoder", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 1216710 (7 March 2022); https://doi.org/10.1117/12.2628796
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KEYWORDS
Computer programming
Data modeling
Data conversion
Principal component analysis
Diagnostics
Signal processing
Transformers
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