Building a Digital Twin Powered Intelligent Predictive Maintenance System for Industrial AC Machines

Abstract

Predictive maintenance is a system’s competency in distinguishing future scenarios where the machine is likely to fail and schedule repairs just prior to this happening. A heuristic technology to enable efficient predictive maintenance is digital twin technology. The development of a twin system between real-time machinery and the virtual world is made possible by digital twin technology, which is ideal for predictive maintenance. Induction motors, which are the core of industrial machinery, are sparsely represented in the digital twin domain. Therefore, this study created a digital twin of a squirrel cage induction motor, utilizing data-driven modeling and multiple physics, and integrated it with a custom predictive maintenance system. The purpose of this study is to implement digital twin technology for induction motors for fault diagnosis and predictive maintenance. This framework can extrapolate running parameters to presciently detect motor remaining useful lifetime as well as erratic fault diagnosis. The experimental setup for the 2.2 kW squirrel cage induction motor has been integrated into the digital workspace via the dSPACE MicroLabBox controller to allow frequent calibration and reference signal setup. The resultant digital framework deployed on MATLAB Simulink provided high accuracy without placing a great computational load on the processor. The proposed model’s commercial application may open the way for computational intelligence in Industry 4.0 adoption of induction motors.

Keywords

• fault detection
• digital twin technology
• intelligent machine diagnostics
• predictive maintenance
• remaining useful life estimation