Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 |
| Herausgeber/-innen | Michael Beer, Enrico Zio |
| Erscheinungsort | Singapur |
| Seiten | 382-386 |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 9789811127243 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Veranstaltung | 29th European Safety and Reliability Conference, ESREL 2019 - Leibniz University Hannover, Hannover, Deutschland Dauer: 22 Sept. 2019 → 26 Sept. 2019 |
Abstract
One of the reasons that Human Reliability Analysis methods had been created is because when the first method was created there was not enough data available to estimate Human Error Probabilities. Such methods provide methodologies to adjust the probabilities according to the specific industrial context being assessed (organisational, technological and individual factors). Some examples are THERP, SPAR-H, HEART, CREAM and ATHEANA. The availability and quality of human error data have improved consistently, thanks to the use of simulators and incident reports. However, the implementation of Bayesian networks models based on the available datasets faces several issues, mainly connected to the definition of adequate conditional probability tables. Indeed, the lack of data regarding the specific error events precludes the possibility of computing the frequency of their occurrence and hence to adopt suitable conditional probability distributions for the related set of event outcomes. Several strategies have been proposed to tackle this issue in the literature, although none of them has obtained unanimously agreement. This paper proposes a Credal network model for the inference of human error. The main aim of the study is to investigate the capability of the proposed approach to fully capture the uncertainty of the input and to rigorously quantify the accuracy model outputs, improving the robustness of human error inference models. Finally, a numerical model is presented to test the feasibility and efficiency of the approach.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
- Sozialwissenschaften (insg.)
- Sicherheitsforschung
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Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. Hrsg. / Michael Beer; Enrico Zio. Singapur, 2020. S. 382-386.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Tackling the lack of data for human error probability with Credal network
AU - Morais, Caroline
AU - Tolo, Silvia
AU - Moura, Raphael
AU - Beer, Michael
AU - Patelli, Edoardo
N1 - Funding information: The authors thank ANP, the Brazilian Oil & Gas Regulator, and EPSRC “Quantification and Management of Risk & Uncertainty in Complex Systems & Environments” (EP/L015927/1), for being supportive of the research of Human Reliability Analysis and Human Factors.
PY - 2020
Y1 - 2020
N2 - One of the reasons that Human Reliability Analysis methods had been created is because when the first method was created there was not enough data available to estimate Human Error Probabilities. Such methods provide methodologies to adjust the probabilities according to the specific industrial context being assessed (organisational, technological and individual factors). Some examples are THERP, SPAR-H, HEART, CREAM and ATHEANA. The availability and quality of human error data have improved consistently, thanks to the use of simulators and incident reports. However, the implementation of Bayesian networks models based on the available datasets faces several issues, mainly connected to the definition of adequate conditional probability tables. Indeed, the lack of data regarding the specific error events precludes the possibility of computing the frequency of their occurrence and hence to adopt suitable conditional probability distributions for the related set of event outcomes. Several strategies have been proposed to tackle this issue in the literature, although none of them has obtained unanimously agreement. This paper proposes a Credal network model for the inference of human error. The main aim of the study is to investigate the capability of the proposed approach to fully capture the uncertainty of the input and to rigorously quantify the accuracy model outputs, improving the robustness of human error inference models. Finally, a numerical model is presented to test the feasibility and efficiency of the approach.
AB - One of the reasons that Human Reliability Analysis methods had been created is because when the first method was created there was not enough data available to estimate Human Error Probabilities. Such methods provide methodologies to adjust the probabilities according to the specific industrial context being assessed (organisational, technological and individual factors). Some examples are THERP, SPAR-H, HEART, CREAM and ATHEANA. The availability and quality of human error data have improved consistently, thanks to the use of simulators and incident reports. However, the implementation of Bayesian networks models based on the available datasets faces several issues, mainly connected to the definition of adequate conditional probability tables. Indeed, the lack of data regarding the specific error events precludes the possibility of computing the frequency of their occurrence and hence to adopt suitable conditional probability distributions for the related set of event outcomes. Several strategies have been proposed to tackle this issue in the literature, although none of them has obtained unanimously agreement. This paper proposes a Credal network model for the inference of human error. The main aim of the study is to investigate the capability of the proposed approach to fully capture the uncertainty of the input and to rigorously quantify the accuracy model outputs, improving the robustness of human error inference models. Finally, a numerical model is presented to test the feasibility and efficiency of the approach.
KW - Credal network
KW - Human error probability
KW - Open toolbox
KW - OpenCossan
KW - Uncertainty Quantification
UR - http://www.scopus.com/inward/record.url?scp=85089190920&partnerID=8YFLogxK
U2 - 10.3850/978-981-11-2724-3_0746-cd
DO - 10.3850/978-981-11-2724-3_0746-cd
M3 - Conference contribution
AN - SCOPUS:85089190920
SP - 382
EP - 386
BT - Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
A2 - Beer, Michael
A2 - Zio, Enrico
CY - Singapur
T2 - 29th European Safety and Reliability Conference, ESREL 2019
Y2 - 22 September 2019 through 26 September 2019
ER -