Digital twin computing for predictive maintenance of industrial systems

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Predictive maintenance (PdM) of industrial systems is a strategy that uses monitoring tools and predictive analytics to determine the condition of in-service equipment and estimate when maintenance should be performed. It can optimize resource usage and reduce unscheduled maintenance, and thus the business can achieve increased productivity, reduced breakdowns, and lower maintenance costs. However, the PdM system development faces the challenges of dealing with heterogeneous data from different sources, extrapolating solutions to complex systems with multiple components, and lacking a systematic design approach. A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process with an industrial system. The diagnostic and prognostic capabilities of the digital twin make it a perfect pair with predictive maintenance. This pairing of the virtual and physical worlds allows analysis of data and monitoring systems to head off problems before they even occur, prevent downtime, develop new opportunities and even plan for the future by using simulations. Although digital twin has recently received much research attention, a general system model has not been fully established and widely accepted yet. Moreover, most digital twin applications focus on an individual twin. The research on integrating multiple digital twins to mirror a complex industry system has not been reported in the literature yet. Understanding individual components and the interactions between them is critical to the predictive maintenance of a complex system. This research program aims to advance the digital twin technology for the predictive maintenance of industrial systems, covering the entire life cycle of an asset or process and forming the foundation for connected products and services. The long-term objective is to achieve the scalability of a digital twin ecosystem with the novel digital twin computing paradigm. In the short term, we will develop such a digital twin ecosystem for industrial predictive maintenance. Our research will first focus on the digital twin architecture based on an industrial Internet of Things (IoT) platform, and multi-source data/information fusion. Moreover, the insights of the interactions among multiple digital twins will be derived with the digital twin computing methodology through multi-agent modelling and reinforcement learning.      The outcomes of this research will make the complete control of resources and awareness of the condition of each asset and its components possible. Thus, it will help the Canadian industry, especially the SME sector, improve operational availability, productivity and machine uptime. In addition, the proposed research will offer a unique multidisciplinary training opportunity for HQP. The HQP will gain in-depth knowledge and experience of more than one discipline and address real-life challenges without regard to disciplinary boundaries.