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2 February 2023 A fault prognosis method for pressure transmitter based on artificial neural network
Ce Han, Feng Yuan, Na Zhang, Songting Wang
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Proceedings Volume 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022); 124622Q (2023) https://doi.org/10.1117/12.2660987
Event: International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 2022, Xi'an, China
Abstract
Pressure transmitters have a large number of applications in process industry sites, and the stable operation of pressure transmitters is related to the stability and safety of the entire process industry site. Therefore, fault prognosis of the pressure transmitter can greatly reduce the unplanned shutdown of the plant due to pressure transmitter damage. This paper proposes a fault prognosis method for pressure transmitter based on artificial neural network (ANN). According to the pressure value measured by the pressure transmitter, we construct a time series sequence, and segment each group of ten measured values, and label each segment of data according to whether the pressure transmitter is damaged. Then we build a 4-layer neural network, which is trained using shuffled segmented data. The validation accuracy of the final training can reach 0.98, which can effectively distinguish fault data from normal data.
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Ce Han, Feng Yuan, Na Zhang, and Songting Wang "A fault prognosis method for pressure transmitter based on artificial neural network", Proc. SPIE 12462, Third International Symposium on Computer Engineering and Intelligent Communications (ISCEIC 2022), 124622Q (2 February 2023); https://doi.org/10.1117/12.2660987
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KEYWORDS
Transmitters
Neural networks
Data modeling
Artificial neural networks
Machine learning
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