A fault prognosis method for pressure transmitter based on artificial neural network

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.