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
- Extensive qualitative analysis has to be performed using IDHEAS-ECA Worksheets
- HEP quantification can be done with IDHEAS-ECA Software.
4 Qualitative analysis
- IDHEAS-ECA Worksheets HEP quantification
- IDHEAS-ECA Software
How IDHEAS-ECA models human failure
- Context are the conditions that affect human performance of an action.
- PIFs are used to model the context.
5 Scenario context and HFEs 5 Macrocognitive functions 20 PIFs (in 4 context categories)
& Time sufficiency Critical Tasks
Modeling human actions with macrocognitive functions 6
Human action Critical Task 1 Critical Task 2 Critical Task 3 Detection Under-standing Decision-making Action execution Teamwork
PIF Structure 7
Environment and Situation System Personnel Task
- Accessibility/habitability of workplace including travel paths
- Workplace visibility
- Noise in workplace and communication pathways
- 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
- HSI
- Equipment and tools
- Staffing
- Procedures, guidance, and instructions
- Training
- Teamwork 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
- Document nomenclature does not agree with equipment labels
- 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.
IDHEAS-ECA Process -
Step 5: HEP Quantification 8
CFM 1 Critical task 1 CFM 2 CFM 4 PIF attributes Critical task 2 Critical task 3
= (,, )
= (,, )
= (,, )
= (,, )
Time required
=
=
=
=
=
=
Step 5 HEP Quantification Model for Pc Base PIFs and Base HEPs A Base PIFs can change HEP from a minimum value to 1 (blue curve)
Information availability and reliability, task complexity scenario familiarity Modification PIFs Remaining 17 PIFs (orange and red curves) 9 PIFs get worse ->
E-4 E-3 E-2 E-1 1
PIF1 PIF2
Step 5 HEP Quantification model for Pc
- 2. Linear combination of PIF effects
=
1 +
=1
(1) 1
10 Base HEP from Base PIFs PIF weights from Modification PIFs PIF interaction factor; set to 1 with linear combination Recovery factor; set to 1 unless data suggest otherwise
=
error rate at a given PIF attribute error rate when the PIF attribute has no or low impact
11 Example: Base HEPs for Information availability and reliability PIF Attribute D
U DM E
T Inf1 Information is temporarily incomplete or not readily available NA 5E-3 5E-3 NA NA Information is moderately incomplete - a small portion of key information is missing NA 5E-2 5E-2 NA NA Information is largely incomplete
- Key information is masked or indications are missing NA 2E-1 2E-1 NA NA Inf2 Unreliable or uncertain
- 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 NA E-2 E-2 NA NA Moderately unreliable or uncertain Personnel recognize information unreliable Conflicts in key information NA 5E-2 5E-2 NA NA Key information is highly uncertain NA E-1 E-1 NA NA Extremely unreliable - Key information is misleading NA E-3 E-3 NA NA IDHEAS-ECA provides the base HEP Values and PIF Weights for every CFM at a given PIF attribute (Appendix B)
12 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 demand attention 1.2 - 2.8 1.1 1.1 1.2 - 2.8 1.2 - 2.8 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 decisions / plans NA NA 5
NA NA MT8 Concurrently executing action sequence and performing another attention/working memory task NA NA NA 2.3 NA MT9 Concurrently executing intermingled or inter-dependent action plans NA NA NA 5
NA MT10 Concurrently communicating or coordinating multiple distributed individuals or teams NA NA NA NA 5
IDHEAS-ECA provides the base HEP Values and PIF Weights for every CFM at a given PIF attribute (Appendix B)
Outline
- 1. Introduction to IDHEAS-ECA
- 2. Generalization and integration of human error data for IDEHAS-ECA
- 3. IDHEAS-ECA Software 13
Human action
/ tasks Context Use of Human Error Data to inform HEPs Cognitive failure modes (CFMs)
Performance influencing factors (PIFs)
HEPs - Base HEPs PIF Impact - Wi PIF Interaction - C
- 1. Documentation -
Evaluate data source
- 2. Generalization -
Interpret and represent data in IDEHAS-DATA
- 3. Integration -
Consolidate the data in IDEHAS-DATA for IDHEAS-ECA
15 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:
Message Complexity
% errors 35 5
Complexity
% errors 1
< 1 5
3.6 10 6.1 11 10.8 12 12 13 19 16 37
CFMs Attributes in the original data HEP Other PIFs Ref ID PIF attribute Attribute states D
Failure to respond to alarms Number of compelling signals Few Several Many 3E-3 1E-2 1E-1 None 02 Error in responding to compelling signals number of annunciators 1 to 10 11 to 40 0.0001 to 0.05 0.10 to 0.20 No peer-checking?
larger than 40 0.25 Failure of getting information Number of messages communicated 1,
5, 8,
11, 15, 17,
>20 0.005 0.036 0.05 0.11 0.23 0.32
>0.5 Mixed levels of stress Low distraction No peer-checking 31 U
comprehension and skill high level 0.15 Unknown 21 cognitive complexity Typical Moderate High 3E-3 0.03 0.05 Unknown Unknown Unknown U4 and U5
- of aviation topics in one communication 1
2 0.038 0.060 No peer checking 31 Example 1: Data generalization in IDHEAS-DATA
17 PIF Attribute Detection C0 No impact on HEP 0
C1 Detection overload with multiple competing signals track the states of multiple systems, monitor many parameters, memorize many pieces of information detected Many types or categories of information to be detected Few (<7) 3E-3 Multiple (7~11) 1E-2 Many (11~20) 1E-1 Excessive amount
(>20) 3E-1 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
Outline
- 1. Introduction to IDHEAS-ECA
- 2. Generalization and integration of human error data for IDEHAS-ECA
- 3. IDHEAS-ECA Tool 18
If you want to acquire a free copy of IDHEAS-ECA tool, Please contact Dr. Y. James Chang (James.Chang@nrc.gov).
The NRCs IDHEAS-ECA Software
20 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.