Towards building digital twins for prognostics and health management (PHM) of industrial assets

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With the rapid advancement in digital and sensor technologies, real-time monitoring, diagnosis, and prognosis of machine or process health conditions are becoming increasingly common practices in an effort to improve reliability, reduce cost and waste, and maximize asset availability. For more than a decade, we have been working on diagnostic decision making for machine health condition monitoring, manufacturing quality assurance, and medical applications. We have developed techniques that use vibration, acoustic, electric current, and visual and thermal imaging signals to conduct analyses and determine the location and severity of faults. In recent years, we have started to develop a modeling framework to construct comprehensive hybrid physics-based and data-driven models. The physics-based approach is necessary to compensate for the lack of impending evidence in sensory data. The data-driven approach can address unmodeled dynamics and uncertainty. Ultimately, the goal is to build an integrated system's model that represents a digital replica of a physical system. Such a model will enable accurate localization of faults, root cause determination, impending component and system failure prediction, and optimal corrective decisions. In this research, we will develop a proof-of-concept integrated system's model that represents a digital replica of a physical system. The system model integrated component models including materials fatigue models, drivetrain dynamics models, gear transmission error models, bearing dynamics, and motor/generator electromechanical models. We will develop data-driven models to describe loose connections, poor lubrication, and fault-induced bearing resonance. Experimental studies will be carried out on a drivetrain apparatus. The end result of the research is a proof of concept that can be adopted by many application areas including advanced manufacturing, transportation, and energy production for critical asset health management.