ML20083J314
| ML20083J314 | |
| Person / Time | |
|---|---|
| Issue date: | 03/12/2020 |
| From: | Jing Xing Office of Nuclear Regulatory Research |
| To: | |
| J. Chang | |
| Shared Package | |
| ML20083J304 | List: |
| References | |
| Download: ML20083J314 (20) | |
Text
Human Error Data for the Integrated Human Event Analysis System for Event Conditions Assessment (IDHEAS-ECA)
Jing Xing NRC/RES/DRA/HFRB
Outline
- 1. Introduction to IDHEAS-ECA
- 2. Generalization and integration of human error data for IDEHAS-ECA
- 3. IDHEAS-ECA Tool 2
Intended Uses of IDHEAS-ECA
- Perform HRA for all nuclear applications
- It uses five macrocognitive functions and 20 performance influencing factors (PIFs) to model failure of human actions:
- Main control room (CR) and ex-CR actions
- At-power and Shutdown
- Level-1 to Level-2 PRA
- Human actions with reactors and non-reactors 3
Overview of IDHEAS-ECA Qualitative HEP analysis quantification
- IDHEAS-ECA - IDHEAS-ECA Worksheets Software
- Extensive qualitative analysis has to be performed using IDHEAS-ECA Worksheets
- HEP quantification can be done with IDHEAS-ECA Software.
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How IDHEAS-ECA models human failure Scenario 5 Macrocognitive functions context Critical and HFEs Tasks 20 PIFs (in 4 context categories)
& Time sufficiency
- Context are the conditions that affect human performance of an action.
- PIFs are used to model the context.
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Modeling human actions with macrocognitive functions Human action Critical Task 1 Critical Task 2 Critical Task 3 Under- Decision- Action Detection Teamwork standing making execution 6
PIF Structure Environment Context and Situation System Personnel Task
- Accessibility/habitability *Staffing *Information availability
- System and I&C and reliability of workplace including *Procedures, guidance, transparency to *Scenario familiarity travel paths and instructions personnel *Multitasking,
- Workplace visibility *Training
- HSI PIF *Noise in workplace and
- Equipment and
- Teamwork and interruptions, and communication organizational factors distractions tools *Task complexity pathways *Work processes
- Cold/heat/humidity *Mental fatigue
- Resistance to physical *Time pressure and movement stress
- Physical demands PIF *Poor lighting in *Tools are difficult to use *Procedure is inadequate workplace *Tools are unfamiliar to personnel *Procedure is difficult to *Sustained high-demand attributes *Glare or reflection on *Tools do not work use cognitive activities Note: The PIF attributes physical structure *Tools or parts are unavailable *Procedure is available, *Long working hours shown are examples *Smoke or fog- *Document nomenclature does not but does not fit the *Sleep deprivation and correspond to the induced low visibility agree with equipment labels situation PIFs highlighted in red.
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IDHEAS-ECA Process -
Step 5: HEP Quantification Time available
=
Time required CFMs PIF attributes HEP of = = (, , )
= (, , )
task 1 CFM 2 Critical = (, , )
task 2 CFM 3 Critical = (, , )
CFM 4 task 3 8
Step 5 HEP Quantification Model for Pc Base PIFs and Base HEPs PIF2 PIF1
- Information availability and reliability, E-2
- task complexity
- scenario familiarity E-3
- Modification PIFs
- Remaining 17 PIFs E-4 (orange and red curves) PIFs get worse ->
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Step 5 HEP Quantification model for Pc
- 2. Linear combination of PIF effects 1
= 1 + ( 1)
=1 Recovery factor; set Base HEP from Base PIFs PIF weights from to 1 unless data Modification PIFs suggest otherwise PIF interaction
= factor; set to 1 with linear combination error rate at a given PIF attribute error rate when the PIF attribute has no or low impact 10
IDHEAS-ECA provides the base HEP Values and PIF Weights for every CFM at a given PIF attribute (Appendix B)
Example: Base HEPs for Information availability and reliability PIF Attribute D U DM E T Inf1 Information is temporarily incomplete or not readily NA 5E-3 5E-3 NA NA available Information is moderately incomplete - a small NA 5E-2 5E-2 NA NA portion of key information is missing Information is largely incomplete NA 2E-1 2E-1 NA NA
- Key information is masked or indications are missing Inf2 Unreliable or uncertain NA E-2 E-2 NA NA
- Personnel is aware that source of information could be temporally unreliable
- pieces of Information change over time thus they become uncertain by the time personnel use them Moderately unreliable or uncertain NA 5E-2 5E-2 NA NA
- Personnel recognize information unreliable
- Conflicts in key information Key information is highly uncertain NA E-1 E-1 NA NA Extremely unreliable - Key information is NA E-3 E-3 NA NA misleading 11
IDHEAS-ECA provides the base HEP Values and PIF Weights for every CFM at a given PIF attribute (Appendix B)
Example: PIF Weights for Multitasking/Interruption/Distraction PIF Attribute D U DM E T MT0 No impact 1 1 1 1 1 MT1 Distraction by other on-going activities that 1.2 - 2.8 1.1 1.1 1.2 - 2.8 1.2 - 2.8 demand attention MT2 interruption taking away from the main task 1.1 - 4 1.1 - 1.7 1.1 - 1.7 1.1 - 4 1.1 - 4 MT3 Concurrent visual detection and other tasks 2 - 10 NA NA NA NA MT4 Concurrent auditory detection and other tasks 10 -20 NA NA NA NA MT5 Concurrent diagnosis and other tasks NA 3-30 NA NA NA MT6 Concurrent Go/No-go decision-making NA NA 2 NA NA MT7 Concurrently making Intermingled, complex NA NA 5 NA NA decisions / plans MT8 Concurrently executing action sequence and NA NA NA 2.3 NA performing another attention/working memory task MT9 Concurrently executing intermingled or inter- NA NA NA 5 NA dependent action plans MT10 Concurrently communicating or coordinating NA NA NA NA 5 multiple distributed individuals or teams 12
Outline
- 1. Introduction to IDHEAS-ECA
- 2. Generalization and integration of human error data for IDEHAS-ECA
- 3. IDHEAS-ECA Software 13
Use of Human Error Data to inform HEPs
- 1. Documentation - 2. Generalization - 3. Integration -
Evaluate data Interpret and Consolidate the data in source represent data in IDEHAS-DATA for IDEHAS-DATA IDHEAS-ECA Cognitive failure HEPs - Base HEPs Human action modes (CFMs)
/ tasks PIF Impact - Wi Performance Context influencing factors (PIFs) PIF Interaction - C
Example 1: Data documentation REF: Message Complexity on Pilot Readback Performance (Prinzo et a., 2006)
Task: Pilots listen to and read back messages from air traffic controllers CFMs: Information misperceived; Information not retained or miscommunicated PIF: Detection complexity - Message complexity indicated by the number of information items that pilots have to retain in their working memory Results: Readback errors increase with message complexity Data:
Complexity % errors 35 1 <1 5 3.6
% errors 10 6.1 11 10.8 5
12 12 13 19 Message Complexity 16 37 15
Example 1: Data generalization in IDHEAS-DATA CFMs Attributes in the original data HEP Other PIFs Ref PIF attribute Attribute states ID D Failure to Number of compelling Few 3E-3 None 02 respond to signals Several 1E-2 alarms Many 1E-1 Error in number of annunciators 1 to 10 0.0001 to 0.05 No peer-checking?
responding to compelling 11 to 40 0.10 to 0.20 signals larger than 40 0.25 Failure of Number of messages 1, 0.005 Mixed levels of 31 getting communicated 5, 0.036 stress Low information 8, 0.05 distraction 11, 0.11 No peer-checking 15, 0.23 17, 0.32
>20 >0.5 U comprehension and skill high level 0.15 Unknown 21 cognitive complexity Typical 3E-3 Unknown Moderate 0.03 Unknown High 0.05 Unknown U4 and U5 # of aviation topics in 1 0.038 No peer checking 31 one communication 2 0.060
Example 1: Data integration for IDHEAS-ECA Multiple data points in IDHEAS-DATA for CFM Failure of Detection and PIF attribute Detection overload with multiple competing signals.
Data integration:
- Anchor to operational data first
- Infer the base HEP by detaching the effects of other PIFs (e.g., the HEP is the reported error rate divided by a factor of 10 for no-peer-checking)
- Adjust the base HEP toward the median of all the data points PIF Attribute Detection C0 No impact on HEP 0 C1 Detection overload with multiple competing signals Few (<7) 3E-3
- track the states of multiple systems, Multiple (7~11)
- monitor many parameters, 1E-2
- memorize many pieces of information detected Many (11~20) 1E-
- Many types or categories of information to be detected 1 Excessive amount
(>20) 3E-1 17
Outline
- 1. Introduction to IDHEAS-ECA
- 2. Generalization and integration of human error data for IDEHAS-ECA
- 3. IDHEAS-ECA Tool 18
The NRCs IDHEAS-ECA Software If you want to acquire a free copy of IDHEAS-ECA tool, Please contact Dr. Y. James Chang (James.Chang@nrc.gov).
Summary
- Human error data of various sources are generalized into IDHEAS-DATA for IDHEAS cognitive failure modes (CFMs) and PIF attributes
- The NRC staff integrated the generalized human error data in IDHEAS-DATA to infer the numbers of base HEPs and PIF weights for IDHEAS-ECA
- Data generalization is generic for IDHEAS CFMs and PIF attributes; Data integration is specific for the HRA method.
- Data generalization is an on-going, continuous effort; Data integration should be periodically updated.
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