| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 23, 2016 |
| Latest Amendment Date: | August 14, 2019 |
| Award Number: | 1544901 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Ralph Wachter
rwachter@nsf.gov (703)292-8950 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2016 |
| End Date: | August 31, 2021 (Estimated) |
| Total Intended Award Amount: | $1,125,000.00 |
| Total Awarded Amount to Date: | $1,125,000.00 |
| Funds Obligated to Date: |
FY 2017 = $217,310.00 FY 2018 = $452,860.00 FY 2019 = $233,535.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
506 S WRIGHT ST URBANA IL US 61801-3620 (217)333-2187 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
506 S Wright Urbana IL US 61801-3620 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CPS-Cyber-Physical Systems |
| Primary Program Source: |
01001718DB NSF RESEARCH & RELATED ACTIVIT 01001819DB NSF RESEARCH & RELATED ACTIVIT 01001920DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Software-Defined Control (SDC) is a revolutionary methodology for controlling manufacturing systems that uses a global view of the entire manufacturing system, including all of the physical components (machines, robots, and parts to be processed) as well as the cyber components (logic controllers, RFID readers, and networks). As manufacturing systems become more complex and more connected, they become more susceptible to small faults that could cascade into major failures or even cyber-attacks that enter the plant, such as, through the internet. In this project, models of both the cyber and physical components will be used to predict the expected behavior of the manufacturing system. Since the components of the manufacturing system are tightly coupled in both time and space, such a temporal-physical coupling, together with high-fidelity models of the system, allows any fault or attack that changes the behavior of the system to be detected and classified. Once detected and identified, the system will compute new routes for the physical parts through the plant, thus avoiding the affected locations. These new routes will be directly downloaded to the low-level controllers that communicate with the machines and robots, and will keep production operating (albeit at a reduced level), even in the face of an otherwise catastrophic fault. These algorithms will be inspired by the successful approach of Software-Defined Networking. Anomaly detection methods will be developed that can ascertain the difference between the expected (modeled) behavior of the system and the observed behavior (from sensors). Anomalies will be detected both at short time-scales, using high-fidelity models, and longer time-scales, using machine learning and statistical-based methods. The detection and classification of anomalies, whether they be random faults or cyber-attacks, will represent a significant contribution, and enable the re-programming of the control systems (through re-routing the parts) to continue production.
The manufacturing industry represents a significant fraction of the US GDP, and each manufacturing plant represents a large capital investment. The ability to keep these plants running in the face of inevitable faults and even malicious attacks can improve productivity -- keeping costs low for both manufacturers and consumers. Importantly, these same algorithms can be used to redefine the production routes (and machine programs) when a new part is introduced, or the desired production volume is changed, to maximize profitability for the manufacturing operation .
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project's aim was to reimagine the future and operations of manufacturing systems and their design, runtime monitoring and anomaly detection. As such, we envisioned the development of "software-defined control" (SDC), inspired by ideas in software-defined networking, that would provide a global view of the system to enable better monitoring and control of the manufacturing system. Coupled with ahead-of-time modeling and anomaly detection features, SDC can significantly improve the design, resiliency and security of manufacturing systems of the future.
The high-level products of this project are:
1. SDCWorks: an open-source modeling and simulation framework for SDC systems. SDCWorks provides the semantics of such a manufacturing system in terms of a discrete transition system which sets up the platform for future research in a new class of problems in formal verification, synthesis, and monitoring. It can be used to evaluate relevant metrics such as throughput, latency and load, ahead of time, for manufacturing systems. This enables engineers to explore the implications of their design choices early on in the development process.
2. VetPLC: a temporal context-aware, program analysis- based approach to produce timed event sequences that can be used for automatic safety vetting.of manufacturing systems (both current as well as future ones). VETPLC outperforms state-of-the-art techniques and can generate event sequences that can be used to automatically detect hidden safety violations, thus enabling safer and more secure manufacturing systems.
3. Digital Twin (DT) Architectures: one of the important components of the SDC architecture and its proposed monitoring capabilities is the development of ?digital twins? ? essentially DTs are real-time digital images of physical systems, processes or products that help evaluate and improve business performance. DTs are developed in order to (a) accurately model the system and (b) monitor the system at runtime to detect deviations from the expected behavior. Since digital twins can model/capture different physical and cyber aspects of the system, there is a need for a framework to manage all the DTs. We developed a unified DT framework to provide a real-time extensible global view of a manufacturing system by deploying multiple DTs at multiple levels of the automation pyramid of the International Society of Automation ISA?95. The DT framework is used within the Software-Defined Control, where it operates with a set of applications and a decision maker to monitor, control, predict, and re-configure (as necessary) complex production processes.
4. The CyPhyHouse and the Koord language were applied to the SDC framework to demonstrate that virtual commissioning can be facilitated using high-level domain specific languages (DSL) tailored for smart manufacturing.
5. Multiple PhD students were trained under this project (and completed their degrees). They also gained significant exposure to other experts in the domain of manufacturing systems, industry partners and also their peers at Michigan and Cornell.
Last Modified: 01/23/2022
Modified by: Sibin Mohan
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