Opportunities for using big data to improve online risk monitoring for nuclear power

plants

Katrina Groth & Michelle (Shelby) BensiCenter for Risk and Reliability ▪ Center for Disaster Resilience

University of Maryland

Big Data for NPPs Workshop, December 2018

UMD Goal: Mature a NPP data fusion framework

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Multi-source, multi-modality data

Actionable Information for Decision Makers

Improvements in operator support

(monitoring, diagnosis & response planning)

Bayesian data fusion, causal & probabilistic models, ML & AI

Goals: Putting the power of big data into the hands of decision makers Data-informed, Model-informed, & Expert-Informed (NOT data-based)

Copyright, K. Groth, UMD

Established 2017, Directed by Prof. Katrina Groth. Research into risk & reliability for complex systems

Human + system + environment + complex phenomena

Supports decision making: enhanced safety, reliability, operations, and maintenance of complex systems.

Primary applications in energy & infrastructure

UMD’s Systems Risk and Reliability Analysis (SyRRA) lab

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Motivation & Purpose

Recent advances in computer science offer new opportunities for the nuclear industry Machine learning Big data Data science/analytics

We are exploring how these advances could support the safety, operational efficiency, and resilience of NPPs

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Leveraging data in NPPs requires a defined strategy

Key steps:1. Define the types of relevant data2. Process disparate plant data streams in near-real time3. Integrate data streams with existing knowledge 4. Transform complex data and information into useful

information to support decision makers

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Framework development phases Phase 1: Problem definition &

partnership development Phase 2:

Algorithms for compiling, processing, and integrating the data into a common architecture

Data analysis methods to enable identification of trends & status indicators for plant & component performance

Phase 3: Visualization & decision-support tools with actionable information for decision makers

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Sensors Arrays Industry OpE

Main Control Room

Plant Opera�onal Data

Informa�on Processing and Integra�on

Trend Analy�cs, Diagnos�cs, and Online Risk Assessment

Online Status Visualiza�on and Decision Support

Engineers have more data, in more forms, than ever (note: data > numbers)

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Written information, (e.g., operating procedures)

Control room simulation

System interaction models & simulation

System reliability data& risk assessments

System physics simulation

Release & consequence models & simulation

Copyright, K. Groth, UMD

NPPs are generating a large variety of dataOnline, real-time monitoring Plant instrumentation systems

Temperatures Vibrations Water levels Pressures Valve positions Radiation levels

Plant computer & main control room Status of major systems and critical safety

functions

Offsite data Status of offsite power

Offline data sources Plant procedures Plant operational and maintenance

information Maintenance logs Outage reports Corrective action program entries

PRA models & databases Industry operating experience

databases Event notification and licensee event

reports Maintenance, inspection, and

reliability data Training, model, & simulation results

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Methods: Bayesian Networks BNs are tool for:

Data fusion (integrating) – combining information from multiple sources into a single framework

Reasoning under uncertainty with the model About uncertain states, with limited information, under changing

conditions

Benefits: Completeness & Insight: Includes all variables, not just those with data Simplicity: Decomposes a large problem into manageable pieces Credibility: Models built with info. & data from multiple sources

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R&D: System-level prognostics for large-scale energy systems Energy production, delivery, storage & end use is a

complex system with many interacting elements. Exploring how health monitoring of individual

components can be used to enable system-level health insights for complex systems

Enabling proactive risk management, planning, sensor deployment at system level

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Data Fusion

Sub-systems

data

Simulation data

Field data

Component & System state (𝑡𝑡𝑖𝑖)

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Copyright, K. Groth, UMD

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R&D: Developed a hybrid (data + physics) corrosion degradation model for pipeline Prognostic and Health Management

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Application to maintenance policy planning

Copyright, K. Groth, UMD

“Smart Procedures”: Dynamic risk-informed diagnostic support for accidents Demonstrated possibility to support diagnosis w/ reactor

simulation, PRA Insight into instruments are most essential for diagnosis of specific

conditions; insight into e.g., which instruments to accident harden; Preliminary insights match expectations - Redundancy between

power/reactivity; high diagnostic value for T_coolant

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Equipment status

Plant Parameters

Groth, K. M.; Denman, M. R.; Jones, T. B.; Darling, M. C. & Luger, G. F. Building and using dynamic risk-informed diagnosis procedures for complex system accidents. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, Accepted 1 September 2018.

Darling, M. C.; Luger, G. F.; Jones, T. B.; Denman, M. R. & Groth, K. M. Intelligent Modeling for Nuclear Power Plant Accident Management. International Journal of Artificial Intelligence Tools, 2018

R&D: data-informed causal model of human reliability for NPP operators PIF hierarchy + SACADA + Cognitive Basis + DBNs Result: Implications for HRA, training design

Data-driven, science-based, dynamic, transparent, repeatable.

16Copyright, K. Groth, UMD

R&D: Potential for automating procedures & proc. updating; PRA updates Text recognition & modeling applied to nuclear documents. Can be connected to digital monitoring & control room

simulation architectures

17Copyright, K. Groth, UMD

Longer term vision: SyRRA lab development

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Commercially available: GSE Systems Nuclear Power Plant (Or Gas?) Control

Room Simulator

SyRRA Capability: 49” multi-touch display + lift platform + computational algorithms, tools

UMD capability for human-and-physics-in-the-loop testing of for complex systems.

(Complementary to OSU, INL capabilities)

UMD HSIS Capabilities: VR cave + data processing capabilities + experimental study protocols

Sustained investment enables projects such as: • Procedure analysis & testing• Human-automation functional allocation.

Copyright, K. Groth, UMD

Next steps

Identify partners willing to exchange data & pilot application of our tools.

Development of test-bed application to enable addressing outstanding questions relevant to broader nuclear industry.

Explore scientific questions: Model scalability (diversity of scenarios, amount of

equipment modeled) Computational scalability / efficiency Optimality of different discretization schemes System-level prognostics

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Conclusions

Big data has big potential for NPPs, but how to use it is an open area of research.

Nuclear data sources are unique – require unique hybrid methods.

Our methods + your data Seeking partners with data & real-world problems. Well-versed in nuclear industry & data techiniques We are actively researching data fusion & visualization to

enable better operational decision making.

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Katrina GrothAssistant Professor, Mechanical Engineering

kgroth@umd.edu

Michelle (Shelby) BensiAssistant Professor, Civil & Environmental Engineering

mbensi@umd.edu

SyRRA Lab http://syrra.umd.eduCenter for Risk and Reliability http://crr.umd.eduCenter for Disaster Resilience http://cdr.umd.edu

mailto:kgroth@umd.edumailto:mbensi@umd.eduhttp://syrra.umd.edu/http://crr.umd.edu/http://cdr.umd.edu/

Opportunities for using big data to improve online risk monitoring for nuclear power plantsUMD Goal: Mature a NPP data fusion frameworkUMD’s Systems Risk and Reliability Analysis (SyRRA) lab Motivation & PurposeLeveraging data in NPPs requires a defined strategyFramework development phasesEngineers have more data, in more forms, than ever (note: data > numbers)NPPs are generating a large variety of dataMethods: Bayesian NetworksR&D: System-level prognostics for large-scale energy systemsR&D: Developed a hybrid (data + physics) corrosion degradation model for pipeline Prognostic and Health Management“Smart Procedures”: Dynamic risk-informed diagnostic support for accidentsR&D: data-informed causal model of human reliability for NPP operatorsR&D: Potential for automating procedures & proc. updating; PRA updatesLonger term vision: SyRRA lab developmentNext stepsConclusionsSlide Number 21