A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing DOI Creative Commons
Tangxiao Yuan, Kondo H. Adjallah, Alexandre Sava

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(20), P. 6586 - 6586

Published: Oct. 12, 2024

The ability and rapid access to execution data information in manufacturing workshops have been greatly improved with the wide spread of Internet Things artificial intelligence technologies, enabling real-time unmanned integrated control facilities production. However, widespread issue quality field raises concerns among users about robustness automatic decision-making models before their application. This paper addresses three main challenges relative issues during automated decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, fault-tolerant control. To address these problems, this proposes a risk assessment framework case continuous production workshops. aims determine method for systematically assessing specific scenarios. It specifies preparation requirements, as well assumptions such datasets on typical working conditions, model. Within framework, are transformed into deviation problems. By employing Monte Carlo simulation measure impact decision risk, direct link between risks is established. defines steps challenges. A study steel industry confirms effectiveness framework. proposed offers new approach safety reducing industrial settings.

Language: Английский

Optimizing non-linear autoregressive networks with Bird Sea Lion algorithms for effective rainfall forecasting DOI

C. Vijayalakshmi,

M. Pushpa

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 22, 2025

Language: Английский

Citations

0
A Decision Risk Assessment and Alleviation Framework under Data Quality Challenges in Manufacturing DOI Creative Commons
Tangxiao Yuan, Kondo H. Adjallah, Alexandre Sava

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(20), P. 6586 - 6586

Published: Oct. 12, 2024

The ability and rapid access to execution data information in manufacturing workshops have been greatly improved with the wide spread of Internet Things artificial intelligence technologies, enabling real-time unmanned integrated control facilities production. However, widespread issue quality field raises concerns among users about robustness automatic decision-making models before their application. This paper addresses three main challenges relative issues during automated decision-making: parameter identification under measurement uncertainty, sensor accuracy selection, fault-tolerant control. To address these problems, this proposes a risk assessment framework case continuous production workshops. aims determine method for systematically assessing specific scenarios. It specifies preparation requirements, as well assumptions such datasets on typical working conditions, model. Within framework, are transformed into deviation problems. By employing Monte Carlo simulation measure impact decision risk, direct link between risks is established. defines steps challenges. A study steel industry confirms effectiveness framework. proposed offers new approach safety reducing industrial settings.

Language: Английский

Citations

1