ML21096A181

Item 2 & 3 IDHEAS-G and IDHEAS-DATA by J Xing - 2021-04-08 Public Meeting

ML21096A181
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Issue date:	04/08/2021
From:	Chang Y, Dejesus-Segarra J, Jing Xing NRC/RES/DRA/HFRB
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Xing, Jang - 301 415 2410
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IDHEAS - An Integrated Human Event Analysis System Jing Xing, Y. James Chang, Jonathan DeJesus Segarra, U.S. Nuclear Regulatory Commission Presented by Jing Xing to the public meeting on IDHEAS April-08-2021

Outline I.

Overview of IDHEAS II.

IDHEAS General Methodology (IDHEAS-G)

III.

Generalization of Human Error Data - IDHEAS-DATA 2

Scope Ex-CR actions Shutdown Severe accidents HRA Variability Use of Human Performance Data Uncertainties in the scenario Analysts practices HRA method Qualitative analysis guidance PIFs - explicit description Cognitive and data basis Qualitative to quantification Where we started 3

What we have achieved Expanded scope - IDHEAS is an HRA method suite for all nuclear HRA applications Use of human performance data - Human error data were explicitly used in IDHEAS

- The method and data structure are based on the same cognitive basis model such that data can be generalized and used by the method.

HRA variability - IDHEAS improves HRA method variability by enhancing the four areas (identified in HRA benchmarking studies)

- Systematic qualitative analysis guidance

- Links between qualitative analysis outcomes and quantification of human error probabilities (HEPs)

Explicit attributes for every performance influencing factor (PIF)

- Cognitive and data basis that links PIF attributes to cognitive failure modes (CFMs) 4

Development of IDHEAS

- An Integrated Human Event Analysis System Cognitive Basis for HRA (NUREG-2114)

IDHEAS General Methodology (IDHEAS-G) (NUREG-2198)

IDHEAS Internal At-power Application (NUREG-2199)

Scientific Literature

Research, operation experience IDHEAS-DATA IDHEAS-ECA (RIL-2020-02)

Testing the method SACADA and other data sources Evaluation of FLEX actions Event analysis FLEX HRA Expert Elicitation SDP/ASP analysis (RIL-2020-13) 5

Development of IDHEAS

- An Integrated Human Event Analysis System Cognitive Basis for HRA (NUREG-2114)

IDHEAS General Methodology (IDHEAS-G) (NUREG-2198)

IDHEAS Internal At-power Application (NUREG-2199)

Scientific Literature

Research, operation experience IDHEAS-DATA IDHEAS-ECA (RIL-2020-02)

Testing the method SACADA and all data sources Evaluation of FLEX actions Event analysis FLEX HRA Expert Elicitation SDP/ASP analysis (RIL-2020-13) 6

Outline I.

Overview of IDHEAS II.

IDHEAS General Methodology (IDHEAS-G)

III.

Generalization of Human Error Data - IDHEAS-DATA 7

What is IDHEAS-G

• A methodology for developing application-specific HRA methods

• A platform for generalizing and integrating human error data to support HEP estimation

• A general HRA method for human event analysis and human error root causal analysis 8

Overview of IDHEAS-G Stage 2 Modeling of important human actions Stage 3 HEP quantification Stage 1 Scenario analysis Stage 4 Integrative analysis Cognition Model Cognition Model Cognitive Basis Structure PIF Structure Cognitive Basis Structure PIF Structure IDHEAS-G consists of a cognition model as the framework for HRA, its implementation in an HRA process, and detailed guidance for HRA applications.

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Whats in IDHEAS-G report (NUREG-2198) 10 Model Guidance Cognitive model Cognitive basis structure PIF structure Time uncertainty model HEP quantification model Dependency model Structure for generalizing human error data Collecting data and information for HRA Analyzing scenario and searching for context Identifying and defining human failure events Analyzing and characterizing tasks Understanding and selecting applicable cognitive failure modes (CFMs)

Representing (mapping) context with performance influencing factors (PIFs)

Analyzing and documenting uncertainties Developing application-specific IDHEAS methods from IDHEAS-G A step-by-step HRA process integrating all the models and guidance Three full examples demonstrating IDHEAS process

Cognitive Basis Structure Human task Cognitive mechanism Cognitive mechanism Cognitive mechanism Cognitive mechanism Cognitive mechanism Cognitive mechanism Processor - D1

Processor - D5 PIF 1 Processor - U1

Processor - U5 Processor - DM1

Processor - DM6 Processor - E1

Processor - E5 Processor - T1

Processor - T7 PIF 2 PIF 3 PIF 17 PIF 18 PIF 19 Macrocognitive functions Processors Cognitive mechanisms PIFs Detection Understanding Decision-making Action execution Interteam coordination 11

PIF Structure Environment and Situation System Personnel and organization Task

• Accessibility/habitabi lity of workplace and travel paths

• Workplace visibility

• Workplace Noise Cold/heat/humidity

• Resistance to physical movement

• Poor lighting in workplace

• Glare or reflection on physical structure

• Smoke or fog-induced low visibility

• System and I&C transparency to personnel

• Human system interface

• Equipment and tools

• Staffing

• Procedures, guidance, and instructions

• Training

• Team and organizational factors

• Work processes

• Information availability and reliability

• Scenario familiarity

• Multitasking, interruptions, and distractions

• Task complexity

• Mental fatigue

• Time pressure and stress

• Physical demands PIF PIF attributes Context

• Tools are difficult to use

• Tools are unfamiliar to personnel

• Tools do not work

• Tools or parts are unavailable

• Procedure is inadequate

• Procedure is difficult to use

• Procedure is available, but does not fit the situation

• Sustained high-demand cognitive activities

• Long working hours

• Sleep deprivation Note: The PIF attributes shown are examples and correspond to the PIFs highlighted in red.

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IDHEAS-G HRA process 13 Human Failure Event (HFE)

Critical tasks Macrocognitive functions and cognitive failure modes (CFMs)

Critical task 2 Critical task 3 HFE2 Critical task 1 Understanding Detection Decisionmaking Action execution Interteam coordination PIFs PIFs PIFs PIFs PIFs Performance influencing factors (PIFs)

Pc - HEP of a CFM = f(PIFs); Pt - HEP of time uncertainty Human error probability (HEP)

Integration to PRA Dependency analysis and Uncertainty analysis PRA model PRA scenario HFE3 HFE1

IDHEAS-G HRA Process Develop scenario narrative Develop scenario timeline (3.1.1)

Analyze scenario context (3.1.2)

Identify and define HFE (3.1.3)

PRA model Break down HFE into CT(s)

(3.2.1)

Characterize the CT(s) and select applicable CFMs (3.2.1 and 3.2.2)

Calculate (3.3.2)

Analyze HFE timeline (subset of scenario timeline, if there are multiple HFEs in the scenario)

Assess attributes of every applicable PIF (3.2.3)

Estimate parameters of distribution (3.3.1)

Estimate parameters of distribution (3.3.1)

Calculate (3.3.1)

Scenario context and list of applicable PIFs PIF attributes of every CFM for every CT List of CT(s)

HFE and its definition List of applicable CFM(s) for the CT(s) and and Calculate overall HEP (3.3)

HFE and its definition HFE and its definition CFM = cognitive failure mode CT = critical task HEP = human error probability HFE = human failure event PIF = performance-influencing factor PRA = probabilistic risk assessment

= error probability due to CFMs

= error probability due variability in and

= time required

= time available and = mean and standard deviation of and = mean and standard deviation of Uncertainty and dependency analysis and documentation 14

HEP QuantificationPc

• Probability of CFM,, can be estimated in one or a combination of the following three ways:

- Calculation from the number of errors divided by number of occurrences

- Expert judgment

- HEP quantification model

• IDHEAS-G provides a data structure of generalizing human error data to support the three ways.

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Outline I.

Overview of IDHEAS II.

IDHEAS General Methodology (IDHEAS-G)

III.

Generalization of Human Error Data - IDHEAS-DATA 16

Generalizing human error data to inform HEPs Data source 1 Tasks A generic, adaptable set of failure modes and PIFs Context Failure modes PIFs Data source 2 Tasks Context Failure modes PIFs HEP = f(states of performance influencing factors) 17 Human error data exist from various domains, in different formats, varying context and levels of detail.

Context and task Variables and Measurements Uncertainties Use human error data to inform HEPs Human tasks ->

Cognitive failure modes (CFMs)

Context ->

Performance influencing factors (PIFs)

Error rates - Base HEPs Change of error rates -

PIF weights ()

Others (e.g., PIF interaction, time distribution, dependency)

1. Evaluation -

Assess data source

2. Generalization -

Represent source data with the CFMs and PIFs

3. Integration -

Integrate the generalized data for HEP calculation 18

Data sources A. Nuclear simulator data and operational data (e.g., SACADA, HuREX, German NPP maintenance database analysis)

B.Operation performance data from other domains (e.g., transportation, off-shore oil, military operations, manufacturing)

C.Experimental studies in the literature (e.g., cognitive and behavior science, human factors, neuroscience)

D.Expert judgment of human reliability in the nuclear domain E. Unspecified context (e.g., statistical data, ranking, frequencies of errors or causal analysis) 19

IDHEAS-DATA Structure IDHEAS-DATA has 27 tables (IDTABLEs) documenting generalized human error data and empirical evidence Human error data are generalized to IDHEAS-G CFMs and PIF attributes IDHEAS-DATA IDTABLE IDTABLE 1-3 Base HEPs IDTABLE-1 Scenario Familiarity IDTABLE-2 Information IDTABLE-3 Task Complexity IDTABLE 4--20 PIF Weights IDTABLE 4-8 Environment PIFs IDTABLE 9-11 System PIFs IDTABLE 11-16 Personnel PIFs IDTABLE 17-20 Task PIFs IDTABLE-21 Lowest HEPs of CFMs IDTABLE-22 PIF Interaction IDTABLE-23 Distribution of Task Needed IDTABLE-24 Modification to Time Needed IDTABLE-25 Dependency of Human Actions IDTABLE-26 Recovery of Human Actions IDTABLE-27 Main drivers to human events 20 20

21 Example datapoints from SACADA database SACADA collects operators task performance data in simulator training.

The unsatisfactory performance rates (UNSAT) for training objective tasks were calculated from the SACADA data before April 2019 and used for IDHEAS-ECA.

For example, SACADA characterizes operators scenario familiarity as three options: Standard, Novel, and Anomaly. The datapoints are used for the base HEPs of PIF Scenario Familiarity (SF3.1) for CFMs Failure of Understanding (U) and Failure of Decisionmaking (DM), as shown in the table below SACADA data IDHEAS-DATA Task (Training Objectives)

Situation factors Error rates CFM PIF Uncertainties Operators diagnose in simulator training Anomaly scenario 1.2E-1 (8/69)

U SF3.1 Other PIFs may exist Operators make decisions in simulator training Anomaly scenario 1.1E-2 (1/92)

DM SF3.1 Other PIFs may exist

By 2020:

Use of nuclear operation/simulation data (SACADA, HuREX, Halden studies)

~300+ literature generalized; another 200+ evaluated and selected for generalization The generalized data were independently verified and reviewed The generalized data were integrated for HEP calculation in IDHEAS-ECA Summary of IDHEAS-DATA In the future:

Human error data needed in teamwork and organizational factors Data generalization is an on-going, continuous effort; Data integration should be periodically updated.

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