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Prognostics and Health Management for Smart Manufacturing Systems

Summary:

Complex system, sub-system, and component interactions within smart manufacturing systems make it challenging to determine specific influences of each on process output metrics and data integrity. There is no uniform process that guides diagnostics and prognostics at all levels (from component to system); Detailed automated diagnostics and prognostics are conducted at the lowest levels whereas higher level diagnostics and prognostics are reliant upon detailed undocumented, ad-hoc human intervention and/or broadly-defined automated means. This project will identify, characterize and enable the communication of metrics supporting diagnostics and prognostics within smart manufacturing systems to increase efficiency. Manufacturers that employ a mix of human and technology resources within their environments, their technology integrators and their supply chain partners will benefit from these enhanced diagnostic and prognostic capabilities.

Description:

Objective:
Develop a methodology, protocols, and reference datasets that enable robust real-time diagnostics and prognostics to enhance the efficiency of smart manufacturing systems by FY 2018.

What is the new technical idea?
Existing approaches to enabling diagnostics and prognostics are often developed to be applicable to a specific organization and/or manufacturing configuration. The new technical idea for this project is to develop a methodology and constituent protocols that enable manufacturers to identify, characterize and enable the communication of metrics supporting diagnostics and prognostics within smart manufacturing systems to increase efficiency. The methodology and protocols will be developed such that they will be applicable to a broad range of manufacturing domains; not just to a specific product or industry.

What is the research plan?
The project work will occur in three phases: assessment, development, and standardization. During the assessment phase, NIST will identify the requirements for diagnostics and prognostics through the analysis of existing capabilities and best practices. These requirements will enable manufacturers, solution providers, and suppliers to leverage the current state-of-the-art and identify critical solution opportunities. The assessment phase will address several research challenges. They are 1) scope identification to determine system and sub-system boundaries and 2) supply chain inclusion to determine what elements, if any, outside of the factory should be considered for this effort that impact diagnostics and prognostics. Industry engagement will be crucial part of this phase and will be highlighted by numerous factory visits and other relevant stakeholder outreach activities.

The development phase focuses on the design and delivery of a methodology and protocols to enable diagnostics and prognostics. Specifically, a hierarchical methodology will be developed to determine data sources for diagnostics and prognostics at the component, sub-system, and system levels within smart manufacturing systems. This will enable manufacturers, solution providers, and suppliers to identify fault sources in their smart manufacturing systems and take appropriate action(s) for mitigation. Once the methodology has been developed, protocols will be designed to facilitate communication of metrics across the component, sub-system, and system levels for diagnostics and prognostics.

Standardization is the third phase of this effort and begins with the development of validated reference datasets, use cases, and test scenarios for implementation of protocols needed for diagnostics and prognostics. This will allow manufacturers, solution providers, and suppliers to benchmark and assess the robustness of their diagnostic and prognostic implementations. Conformity assessment tools will then be developed for the new protocols. These tools will enable industry to assure their implementations meet industry standards supporting real-time prognostics and diagnostics to enhance the efficiency of smart manufacturing systems.

Start Date:

October 1, 2013

Lead Organizational Unit:

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Contact
Brian Weiss, Project Leader

301 975 4373 Telephone
301 990 9688 Fax

100 Bureau Drive, M/S 8230
Gaithersburg, MD 20899