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The present research illustrates a Digital Twin Proof of Concept to support machine prognostics with Low Availability of Run-to-Failure Data. Developed in the scope of the Industry 4.0 Lab of the Manufacturing Group of the School of Management of Politecnico di Milano, the Digital Twin is capable to run in parallel to the drilling machine operations and, as such, it enables to predict the evolution of the most critical failure mode, that is the imbalance in the drilling axis. The real-time monitoring of the drilling machine is realized with a low-cost and retrofit solution, which provides the installation of a Raspberry-Pi accelerometer, able to enhance the extant automation. Relying on a joint use of real-time monitoring and simulation, the Digital Twin implements a random coefficient statistical method through the so-called Exponential Degradation Model, eventually demonstrating to increase the prediction precision as monitoring data arrives. The Digital Twin Proof of Concept is described according to the entire process from data acquisition to Remaining Useful Life prediction, following the MIMOSA OSA-CBM standards.

A Digital Twin Proof of Concept to Support Machine Prognostics with Low Availability of Run-To-Failure Data