A digital twin methodology for predictive maintenance of reciprocating compressor for gas transport
Résumé
Digital twin for predictive maintenance aims to develop and establish digital twins capable of monitoring how the state of health of industrial systems evolves. It focuses on the definition of digital twins capable of acquiring data in real time/delayed time, and processing it in order to assess the state of health of the industrial system. Furthermore, the digital twin must be able to predict and optimize how the lifetime of the physical system evolves, based on the different health indicators identified. The presented work targets the GRTgaz's use-case which deals with circumvention of the system failures and injection stop of the distribution network toward the transport network. To do so, the reciprocating compressors need to be efficiently maintained. The digital twin methodology is proposed to establish the maintenance policies by using the digital twin coupled between sensor data and physical simulation. The data acquisition and physical model methodology have been proposed. The primary results of physical modeling are presented.
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