Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review

Sethu, Meenu; Kotla, Bhavya; Russell, D. A.; Madadi, Mahboubeh; Titu, Nesar Ahmed; Coble, Jamie B.; Boring, Ronald L.; Blache, Klaus; Agarwal, Vivek; Yadav, Vaibhav; Khojandi, Anahita

doi:10.1080/00295450.2022.2067461

Cited by 19 publications

(7 citation statements)

References 64 publications

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“…After entering into the DSM and improving their arrangement and after the second interview with the expert team, sub-tasks 53, 48, 38,32,18,16,42,41,40,25,24,23,22,11,10, and 52 have had fewer errors, which indicates the reduced human error and increased reliability.…”

Section: Step 7 Recalculating the Error Of Terminal Conductor Tasks A...mentioning

confidence: 99%

“…Also, Shen and Zhai 23 and Sethu et al 24 investigated the effect of using intelligent technology on reducing human error. Based on studies in this field, with the rapid development of intelligent technology, human error defense has gradually developed from traditional human-defense to intelligent-defense, with the aim of further improving work efficiency and human reliability.…”

Section: Introductionmentioning

confidence: 99%

“…Also, Shen and Zhai 23 and Sethu et al 24 . investigated the effect of using intelligent technology on reducing human error.…”

Section: Introductionmentioning

confidence: 99%

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Developing a new method for improving the human resource reliability using the hybrid approach of SPAR‐H and DSM in the urban railway

Fakharzadeh,

Fakoor Saghih,

Bohlouri

et al. 2024

Quality & Reliability Eng

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As one of the most important reasons of irreparable losses at work, human error is still a significant problem in spite of the rapid technological advancement and reduced role of humans in the systems set up and control. Therefore, with the aim of reducing human error, this study presents a new method to improve human reliability using the hybrid approach of SPAR‐H and DSM. For this purpose, the number of 53 tasks was specified for the terminal conductor using Hierarchical Task Analysis (HTA). Then, the error related to each task was calculated utilizing the Standardized Plant Analysis‐Risk Human (SPAR‐H) method, and based on the expert team's opinion. Next, the terminal conductor's tasks were entered into the DSM along with the error numbers and dependencies between tasks, and the most optimal arrangement of tasks was obtained by coding in MATLAB software. Finally, by using the SPAR‐H method, the error of the terminal conductor was calculated based on the new tasks arrangement and compared with the primary state. Based on the obtained results, the 30% increase in reliability shows the significant contribution of the optimal arrangement of tasks in reducing human error. The proposed method of this study helps managers to reviewing the task structure in high‐risk processes as an effective strategy to reduce human error.

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Section: Step 7 Recalculating the Error Of Terminal Conductor Tasks A...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Developing a new method for improving the human resource reliability using the hybrid approach of SPAR‐H and DSM in the urban railway

Fakharzadeh,

Fakoor Saghih,

Bohlouri

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…The selection of ADAM was because it could reduce the time for hyper-parameter tuning because ADAM is an adaptive learning rate algorithm [34,35]. ADAM can be represented in Equation (3).…”

Section: Hyperparameter Settingmentioning

confidence: 99%

“…Additionally, inspection accuracy can also be affected by new employees lacking experience and fatigue issues. An automatic inspection system [2] can provide a solution by reducing costs and time and providing a direct correlation between operational information and product quality [3]. An automatic inspection system for identifying defects has several benefits, including operating continuously for extended periods, producing consistent results, and functioning in harsh conditions such as high temperatures and dusty environments [4].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Computational Resources for Real-Time Product Quality Assessment Using Deep Learning and Multiple High Frame Rate Camera Sensors

Wibowo

Setiawan

Afrisal

et al. 2023

ASI

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Human eyes generally perform product defect inspection in Indonesian industrial production lines; resulting in low efficiency and a high margin of error due to eye tiredness. Automated quality assessment systems for mass production can utilize deep learning connected to cameras for more efficient defect detection. However, employing deep learning on multiple high frame rate cameras (HFRC) causes the need for much computation and decreases deep learning performance, especially in the real-time inspection of moving objects. This paper proposes optimizing computational resources for real-time product quality assessment on moving cylindrical shell objects using deep learning with multiple HFRC Sensors. Two application frameworks embedded with several deep learning models were compared and tested to produce robust and powerful applications to assess the quality of production results on rotating objects. Based on the experiment results using three HFRC Sensors, a web-based application with tensorflow.js framework outperformed desktop applications in computation. Moreover, MobileNet v1 delivers the highest performance compared to other models. This result reveals an opportunity for a web-based application as a lightweight framework for quality assessment using multiple HFRC and deep learning.

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Achieving SDGs Using AI Techniques and Digital Twins for Nuclear Power Plants: A Review

Barik,

Misra,

Thunem

2024

Lecture Notes on Data Engineering and Communications Technologies

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Application of Artificial Intelligence in Detection and Mitigation of Human Factor Errors in Nuclear Power Plants: A Review

Cited by 19 publications

References 64 publications

Developing a new method for improving the human resource reliability using the hybrid approach of SPAR‐H and DSM in the urban railway

Developing a new method for improving the human resource reliability using the hybrid approach of SPAR‐H and DSM in the urban railway

Optimization of Computational Resources for Real-Time Product Quality Assessment Using Deep Learning and Multiple High Frame Rate Camera Sensors

Achieving SDGs Using AI Techniques and Digital Twins for Nuclear Power Plants: A Review

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