Digital twin computing for predictive maintenance of industrial systems

工业系统的预测性维护（PdM）是一种使用监控工具和预测分析来确定在役设备的状况并估计何时应执行维护的策略。它可以优化资源使用并减少计划外维护，从而提高企业的生产力，减少故障，降低维护成本。然而，PdM系统开发面临的挑战是处理来自不同来源的异构数据，将解决方案外推到具有多个组件的复杂系统，以及缺乏系统的设计方法。数字孪生是一种虚拟表示，它作为工业系统中物理对象或过程的实时数字对应物。数字双胞胎的诊断和预后能力使其成为预测性维护的完美配对。虚拟世界和物理世界的这种配对允许分析数据和监控系统，以便在问题发生之前阻止它们，防止停机，开发新的机会，甚至通过使用模拟来规划未来。虽然数字孪生近年来受到了广泛的研究关注，但一个通用的系统模型还没有完全建立并被广泛接受。此外，大多数数字双胞胎应用程序都专注于单个双胞胎。关于整合多个数字双胞胎以反映复杂行业系统的研究尚未在文献中报道。了解各个组件及其之间的相互作用对于复杂系统的预测性维护至关重要。该研究计划旨在推进工业系统预测性维护的数字孪生技术，涵盖资产或流程的整个生命周期，并为互联产品和服务奠定基础。长期目标是实现数字孪生生态系统的可扩展性与新的数字孪生计算范式。在短期内，我们将为工业预测性维护开发这样一个数字孪生生态系统。我们的研究将首先关注基于工业物联网（IoT）平台的数字孪生架构，以及多源数据/信息融合。此外，多个数字双胞胎之间的交互的见解将通过多智能体建模和强化学习的数字双胞胎计算方法来获得。 这项研究的成果将使资源的完全控制和了解每项资产及其组成部分的状况成为可能。因此，它将帮助加拿大工业，特别是中小企业部门，提高业务可用性，生产力和机器寿命。此外，拟议中的研究将提供一个独特的多学科培训的机会HQP。HQP将获得多个学科的深入知识和经验，并在不考虑学科界限的情况下解决现实生活中的挑战。

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.