ML22070A150
| ML22070A150 | |
| Person / Time | |
|---|---|
| Issue date: | 03/27/2022 |
| From: | Chang Y, Segarra J, Jing Xing NRC/RES/DRA/HFRB |
| To: | |
| Xing, Jing - 301 415 2410 | |
| References | |
| Download: ML22070A150 (17) | |
Text
IDHEAS-DATA - Human Error Data generalized in IDHEAS-G framework Jing Xing, Y. James Chang, Jonathan DeJesus Segarra, U.S. Nuclear Regulatory Commission Presented by Jing Xing to EHPG 2022 3-27-2022
Development of IDHEAS
- An Integrated Human Event Analysis System Scientific Cognitive Basis for HRA Literature (NUREG-2114)
SACADA and all data sources
- Research, IDHEAS General Methodology IDHEAS-operation experience (IDHEAS-G) (NUREG-2198) DATA IDHEAS Internal At- IDHEAS-ECA (RIL-2020-02) power Application (NUREG-2199)
HRA applications HRA applications 2
Outline I. Approach of using human error data for HRA II. Data source evaluation III. Data generalization (IDTABLEs) 3
I. Approach of using human error data for HRA
- Evaluation of human error data sources Human error data exist from various domains, in different formats, varying context and levels of details.
- Data generalization The General Methodology of Integrated Human Event Analysis System (IDHEAS-G) has an inherent structure for generalizing human error data:
- Five macrocognitive functions represent failure of human actions.
- 20 PIFs represent the context that affects human performance of an action.
- Data integration for human error probability (HEP) estimation Generalized human error data can be integrated to inform HEP estimation for specific HRA methods and applications.
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Generalizing human error data to inform HEPs HEP = f(states of performance influencing factors)
Data source 1 Data source 2 Tasks Context Tasks Context Failure PIFs Failure PIFs modes modes A generic, adaptable set of failure modes and PIFs 5
Use human error data to inform HEPs
- 1. Evaluation - 2. Generalization - 3. Integration -
Assess data Represent source data Integrate the data in source with the CFMs and PIFs IDHEAS-DATA for in IDHEAS-DATA HEP calculation
- Context and Human tasks -> Error rates - Base HEPs task Cognitive failure modes (CFMs) Change of error rates -
- Variables and PIF weights (Wi)
Measurements Context ->
Performance Others (e.g., PIF
- Uncertainties influencing Interaction, time factors (PIFs) distribution, dependency) 6
II. 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, manufacture)
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. Unspecific context (e.g., statistical data, ranking, frequencies of errors or causal analysis) 7
Data source evaluation
- Participants - Normal adults, trained for the tasks, good sample size
- Tasks (of which error rate was measured) - High-level, operational-surrogating tasks involving one or more macrocognitive functions
- Measurements - Human error rate preferred; task performance measures related to human error rates
- Uncertainties - Controlled, known, or traceable
- Breath of representation - Repetitive and representative 8
Outline I. Approach of using human error data for HRA II. Data source evaluation III. Human error data generalization (IDTABLEs) 9
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-21 Lowest HEPs of CFMs IDTABLE-1 Scenario Familiarity IDTABLE-22 PIF Interaction IDTABLE-2 Information IDTABLE-23 Distribution of Task Needed IDTABLE-3 Task Complexity IDTABLE-24 Modification to Time Needed IDTABLE 4--20 PIF Weights IDTABLE-25 Dependency of Human IDTABLE 4-8 Environment PIFs Actions IDTABLE 9-11 System PIFs IDTABLE-26 Recovery of Human Actions IDTABLE 11-16 Personnel PIFs IDTABLE-27 Main drivers to human events IDTABLE 17-20 Task PIFs 10 10
Data generalization process Generalizing a data source is the same as performing an HRA using IDHEAS-G
- Analyze the data source to understand the context and determine the human error data for generalization
- Analyze the tasks and identify the applicable CFMs
- Map the context to relevant PIF attributes
- Identify other PIF attributes present in the study
- Analyze uncertainties
- Document the reported human error data in IDTABLE 11
Two type of PIFs Whats in data about PIF effects on HEPs 3.5E-1 Information Availability and Reliability Base can vary HEP from nearly 0 to 1; Scenario Familiarity can vary HEP
% errors PIFs from nearly 0 to 1; Task Complexity can vary HEP from nearly 0 to 1; E-3 Base PIF - Task complexity Modification PIFs -
A single modification PIF attribute typically varies HEP in the range of 1.1 to 10 times, with a few exception high up to 30 times for feasible tasks.
Example 1: a datapoint for base HEP
- The NRCs SACADA database collects NPP operators task performance data in simulator training for requalification examination. The rates of unsatisfactory performance (UNSAT) for training objective tasks were calculated from the SACADA data available before April 2019.
- The UNSAT rates are generalized in IDTABLE-1, -2, and -3 for the three base PIFs.
- For example, SACADA characterizes Scenario Familiarity as three options:
Standard, Novel, and Anomaly. The generalized datapoints are shown in the following:
Other PIFs Error Task (and error PIF CFM PIF measure (and REF rates measure)
Uncertainty)
SF3.1 U 1.2E-1 NPP operators Anomaly (Other PIFs [26]
(8/69) diagnose in simulator scenario may exist) training SF3.1 DM 1.1E-2 NPP operators Anomaly (Other PIFs [26]
(1/92) decisionmaking in scenario may exist) simulator training 13
Example 2: a datapoint for PIF weight
- Braunstein and White measured human errors in reading dials as the luminance on the dials was varied from 0.015 to 150 L/m2.
- The error rate decreased with luminance. When the luminance was greater than 15 L/m2, the error rate was low and remained the same.
- Many other studies reported similar relation between luminance and error rates.
- The following is the datapoint generalized in IDHEAS-DATA IDTABLE-5 for Visibility:
PIF CFM Error rates Task (and error PIF measure Other PIFs REF measure) (and Uncertainty)
VIS1 D Luminance Reading error Military Luminance No peer- VIS-0.15 0.16 operators dial (L/m2) checking, 9 1.5 0.1 reading maybe HSI
>15 0.08 (incorrect reading) 14
Overview of IDHEAS-DATA in 2020
- Data sources
- Limited use of nuclear operation/simulation data (SACADA, HuREX, Halden studies)
- ~300+ literature generalized; another 200+ evaluated and selected for generalization
- 300~400 literature on task completion time to be generalized in 2021 15
Overview of IDHEAS-DATA in 2020
- IDTABLEs
- The data in IDTABLE-1 through -21 (base HEPs, PIF weights, and lowest HEPs) were integrated for IDHEAS-ECA.
- IDTABLE-23 and -24 (Task Completion Time) are on the way.
- IDTABLE-25 (dependency), -26 (recovery) and -27 (main drivers) are in piloting.
- Areas lacking human error data
- CFMs: Interteam Coordination
- PIFs: Work Process, Team and Organizational Factors 16
Summary of IDHEAS-DATA
- Human error data of various sources are generalized into IDHEAS-DATA with IDHEAS cognitive failure modes (CFMs) and PIF attributes
- Data generalization is generic with IDHEAS CFMs and PIF attributes; Data integration is specific to the HRA method or application that uses the data.
- Data generalization is an on-going, continuous effort; Data integration should be periodically updated.
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