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 Cognitive Basis for HRA (NUREG-2114) 2 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)
HRA applications HRA applications SACADA and all data sources
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.
4
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) 5
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 (Wi)
Others (e.g., PIF Interaction, time distribution, dependency)
- 1. Evaluation -
Assess data source
- 2. Generalization -
Represent source data with the CFMs and PIFs in IDHEAS-DATA
- 3. Integration -
Integrate the data in IDHEAS-DATA for HEP calculation 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 10 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 10
11 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
Whats in data about PIF effects on HEPs Information Availability and Reliability can vary HEP from nearly 0 to 1; Scenario Familiarity can vary HEP from nearly 0 to 1; Task Complexity can vary HEP from nearly 0 to 1; Base PIFs 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.
% errors 3.5E-1 E-3 Base PIF - Task complexity Two type of PIFs
13 Example 1: a datapoint for base HEP PIF CFM Error rates Task (and error measure)
PIF measure Other PIFs (and Uncertainty)
REF SF3.1 U
1.2E-1 (8/69)
NPP operators diagnose in simulator training Anomaly scenario (Other PIFs may exist)
[26]
SF3.1 DM 1.1E-2 (1/92)
NPP operators decisionmaking in simulator training Anomaly scenario (Other PIFs may exist)
[26]
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:
14 Example 2: a datapoint for PIF weight PIF CFM Error rates Task (and error measure)
PIF measure Other PIFs (and Uncertainty)
REF VIS1 D
Luminance Reading error Military operators dial reading (incorrect reading)
Luminance (L/m2)
No peer-
- checking, maybe HSI VIS-9 0.15 0.16 1.5 0.1
>15 0.08 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:
15 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 Overview of IDHEAS-DATA in 2020
16 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
17 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.