Through the combination of models and data, digital twins enable predictive maintenance and real-time diagnostics for field equipment, such as triplex pumps. Deploying these digital twins for real-time health monitoring applications reduces equipment downtime by allowing early anomaly detection and more optimal scheduled maintenance. MATLAB® and Simulink® enable development and deployment of both data-driven and model-based digital twin techniques in both field equipment and on the cloud.
In this presentation, the triplex pump, commonly used in drilling operations, is used as an example to develop both data-driven and model-based predictive maintenance applications. A model of the triplex pump is developed in Simulink using Simscape® libraries and then calibrated with field data to ensure the digital twin matches the field asset. Once calibrated, faults can be injected, such as a blocked flow inlet, into the digital twin to synthesize fault data. These fault datasets can be used to train machine learning algorithms, such as using classification techniques for diagnosing equipment faults. Last, we cover code generation for edge device deployment and compiler approaches for cloud deployment.
