Decentralized Data Analytics and Optimization Methods for Physical Asset Management

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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and healthcare are geographically distributed and owned by different enterprises, which make the existing models no longer applicable or appropriate for these assets.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.