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Reliability Analysis for a New Semiautomated Control and Monitoring System for GHARR‐1 MNSR Using Failure Modes, Effects, and Criticality Analysis.

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Title: Reliability Analysis for a New Semiautomated Control and Monitoring System for GHARR‐1 MNSR Using Failure Modes, Effects, and Criticality Analysis.
Authors: Obeng, H. K.1,2 (AUTHOR), Edziah, R.2 (AUTHOR) redziah@ucc.edu.gh, Amponsah-Abu, E. O.1 (AUTHOR), Deepali (AUTHOR)
Source: Journal of Electrical & Computer Engineering. 10/23/2025, Vol. 2025, p1-17. 17p.
Subject Terms: *FAILURE mode & effects analysis, *CRITICALITY (Nuclear engineering), *ENGINEERING reliability theory, *FEEDBACK control systems, *SAFETY, *SUPERVISION, *RISK assessment, *SUPERVISORY control systems
Abstract: This study conducts a reliability analysis of a newly developed control and monitoring system for the Ghana Research Reactor‐1 Miniature Neutron Source Reactor (GHARR‐1 MNSR), in line with International Atomic Energy Agency (IAEA) recommendations. The current MNSRs lack 24‐h online and offline condition monitoring capabilities, which impacts reliability and safety. Using a hybrid methodology of the failure mode, effects, and criticality analysis (FMECA) and fault tree analysis (FTA), this research aims to enhance system reliability, improve safety, inform maintenance strategies, and reduce costs. Key findings identified critical failure modes such as short circuits, open circuits, and overheating, with risk priority numbers (RPNs) ranging from 20 to 80. Components with the highest RPNs, including transformers, fuses, PC motherboards, and storage devices, were deemed most critical, posing potential shutdown risks. Risk thresholds categorize components into high‐risk (70 < RPN < 90), medium‐risk (RPN ≥ 50), and low‐risk (RPN ≤ 50) levels, guiding preventive maintenance priorities. Six components, including the transformer and storage hard disk drive (HDD), were classified as high risk and require immediate action. The criticality scores further highlighted very high‐risk zones, emphasizing the need to monitor key components to ensure system reliability continuously. The analysis determined that the semiautomated control system's likelihood of failure occurrence, detection, and severity falls within a medium‐risk range. This paper also details the severity classification framework, failure causes, effects, and detection likelihood, providing actionable insights for improving system performance and safety. FTA facilitated the identification of minimal cut sets and critical paths, uncovering key vulnerabilities within the control and monitoring system and underscoring the need for targeted reliability enhancements. The probability that the control system would fail given the occurrence of the basic events and sequences analyzed in this study is 1.27E − 05 per demand. The estimated mean time to failure (MTTF) is approximately 78,740 h. The analysis emphasizes the importance of addressing power supply overvoltage and sensor calibration errors to prevent cascading failures. Implementing robust fault isolation mechanisms, redundant power supplies, and fault‐tolerant PLC modules is recommended to strengthen system resilience. The sensitivity analysis showed that load‐related and memory‐related issues dominate system sensitivity. These findings demonstrate the effectiveness of FTA in identifying high‐risk failure scenarios and informing the development of effective mitigation strategies. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
Description
Abstract:This study conducts a reliability analysis of a newly developed control and monitoring system for the Ghana Research Reactor‐1 Miniature Neutron Source Reactor (GHARR‐1 MNSR), in line with International Atomic Energy Agency (IAEA) recommendations. The current MNSRs lack 24‐h online and offline condition monitoring capabilities, which impacts reliability and safety. Using a hybrid methodology of the failure mode, effects, and criticality analysis (FMECA) and fault tree analysis (FTA), this research aims to enhance system reliability, improve safety, inform maintenance strategies, and reduce costs. Key findings identified critical failure modes such as short circuits, open circuits, and overheating, with risk priority numbers (RPNs) ranging from 20 to 80. Components with the highest RPNs, including transformers, fuses, PC motherboards, and storage devices, were deemed most critical, posing potential shutdown risks. Risk thresholds categorize components into high‐risk (70 < RPN < 90), medium‐risk (RPN ≥ 50), and low‐risk (RPN ≤ 50) levels, guiding preventive maintenance priorities. Six components, including the transformer and storage hard disk drive (HDD), were classified as high risk and require immediate action. The criticality scores further highlighted very high‐risk zones, emphasizing the need to monitor key components to ensure system reliability continuously. The analysis determined that the semiautomated control system's likelihood of failure occurrence, detection, and severity falls within a medium‐risk range. This paper also details the severity classification framework, failure causes, effects, and detection likelihood, providing actionable insights for improving system performance and safety. FTA facilitated the identification of minimal cut sets and critical paths, uncovering key vulnerabilities within the control and monitoring system and underscoring the need for targeted reliability enhancements. The probability that the control system would fail given the occurrence of the basic events and sequences analyzed in this study is 1.27E − 05 per demand. The estimated mean time to failure (MTTF) is approximately 78,740 h. The analysis emphasizes the importance of addressing power supply overvoltage and sensor calibration errors to prevent cascading failures. Implementing robust fault isolation mechanisms, redundant power supplies, and fault‐tolerant PLC modules is recommended to strengthen system resilience. The sensitivity analysis showed that load‐related and memory‐related issues dominate system sensitivity. These findings demonstrate the effectiveness of FTA in identifying high‐risk failure scenarios and informing the development of effective mitigation strategies. [ABSTRACT FROM AUTHOR]
ISSN:20900147
DOI:10.1155/jece/4692754