Decentralized Data Analytics and Optimization Methods for Physical Asset Management

实物资产管理的去中心化数据分析和优化方法

• 批准号: RGPIN-2020-05477
• 负责人: Taghipour, Sharareh
• 金额: $ 3.79万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717155
• 关键词: Decentralized; Data; Analytics; Optimization; Methods

Nature of the Proposed Research The existing data-driven prognostics models for a network of distributed physical assets are centralized and reliant on the availability of assets' sensors, failures and anomaly data. The issue of data scarcity is usually tackled by aggregation of data from similar assets to create a larger data pool for diagnosis. However, if the similar assets belong to various enterprises, they may be reluctant to share their raw asset data with each other, or send it to a central server for processing. In addition, the current optimization models for maintenance and task allocation and scheduling are centralized. These models do not consider the objectives and preferences of various stakeholders involved in the process, and require access to stakeholders' private information, such as their production schedules and the status/availability of their assets. However, many Internet of Things (IoT) assets and cyber-physical systems (CPS) used in cloud manufacturing, smart grids, connected vehicles, and healthcare are geographically distributed and owned by different enterprises, which make the existing models no longer applicable or appropriate for these assets. Realizing the potential of recent technological advancements in providing real-time connectivity, data processing, and information sharing, the proposed research aims to develop novel decentralized solutions for prognostics, maintenance scheduling optimization, and real-time task allocation and scheduling optimization of a network of distributed IoT assets. Anticipated Outcomes The proposed research program will advance knowledge for distributed IoT asset management in Cloud-Edge infrastructures. The decentralized prognostic models will enhance failure prediction accuracy by aggregating the contributions from multiple organizations in a global model while allowing the organizations to keep their data private. This more accurate failure prediction will significantly lower maintenance costs, and decrease the negative impacts of failures on the environment, economy and society. The decentralized optimization models for maintenance, task allocation, and scheduling will result in hierarchical and collaborative knowledge creation, better utilization of resources, higher energy efficiency, more resilience, and lower computation time of the optimization process. These outcomes will be achieved while organizations still can maximize their own benefits without fully sharing their private data. The proposed models will help organizations in transition towards full realization and utilization of emerging technologies, such as cloud and edge computing. Highly qualified personnel (HQP) trained in this program will possess a unique skillset in data analytics, mathematical modeling and optimization which will create exceptional career opportunities for them in academia or industry.

拟议研究的性质 现有的分布式物理资产网络的数据驱动的动态模型是集中的，并依赖于资产的传感器，故障和异常数据的可用性。数据稀缺的问题通常通过从类似资产中汇总数据来解决，以创建更大的诊断数据池。然而，如果类似的资产属于不同的企业，它们可能不愿意彼此共享其原始资产数据，或者将其发送到中央服务器进行处理。此外，目前用于维护和任务分配和调度的优化模型是集中的。这些模型不考虑过程中涉及的各个利益相关者的目标和偏好，并且需要访问利益相关者的私人信息，例如他们的生产计划和他们的资产的状态/可用性。然而，云制造、智能电网、联网车辆和医疗保健中使用的许多物联网（IoT）资产和网络物理系统（CPS）在地理上分布并由不同的企业拥有，这使得现有模型不再适用于这些资产。实现最新技术进步在提供实时连接，数据处理和信息共享方面的潜力，拟议的研究旨在为分布式物联网资产网络的自动化，维护调度优化以及实时任务分配和调度优化开发新的分散解决方案。 预期成果 拟议的研究计划将推进云边缘基础设施中分布式物联网资产管理的知识。分散的预测模型将通过将多个组织的贡献聚合在一个全球模型中，同时允许组织保持其数据的私密性，从而提高故障预测的准确性。这种更准确的故障预测将大大降低维护成本，并减少故障对环境，经济和社会的负面影响。维护、任务分配和调度的分散优化模型将导致分层和协作的知识创建、更好的资源利用、更高的能源效率、更强的弹性和更低的优化过程的计算时间。这些结果将在组织仍然可以最大限度地提高自身利益的同时实现，而无需完全共享其私人数据。拟议的模型将帮助组织过渡到充分实现和利用新兴技术，如云计算和边缘计算。在该计划中培训的高素质人员（HQP）将拥有数据分析，数学建模和优化方面的独特技能，这将为他们在学术界或工业界创造特殊的职业机会。