PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Edward Chen Co-author(s): Bao Han han.bao@inl.gov
Tate Shorthill tate.shorthil@inl.gov
Nam Dinh ntdinh@ncsu.edu
| Failure Mechanism Traceability and Application in Human System Interface of Nuclear Power Plants using RESHA |
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| In recent years, there has been considerable effort to modernize existing and new nuclear power plants with digital instrumentation and control systems (DI&C). However, there has also been considerable concern both by industry and regulatory bodies on the risk and consequence analysis of these systems. Of particular concern is digital common cause failures (CCFs) as a result of failures or “misbehaviors” by the software in both the control and monitoring. While many new methods have been proposed to identify potential failure modes, such as Systems-theoretic Process Analysis (STPA), Hazard and Consequence Analysis for Digital Systems (HAZCADS), etc., these methods are focused primarily on the control action pathway of a system. Unlike the control pathway, the information feedback pathway lacks control actions, which are typically associated as software basic events, and thus assessment of software basic events in such systems is uncertain. In this work, we present the idea of intermediate processors and unsafe information flow (UIF) to help safety analysts trace failure modes in the feedback pathway and how they can be integrated into a fault tree for improved visual assessment. The concepts presented are demonstrated in a comprehensive case study on a representative advanced human system interface, specifically the Qualified Indication and Alarm System for Safety (QIAS-P). The qualitative software basic events are identified, and a fault tree analysis is conducted based on the Redundancy-guided Systems-theoretic Hazard Analysis (RESHA) methodology for tree construction. The case study demonstrates the inclusion of UIF and intermediate processors in the fault tree can significantly improve traceability of software failures that can occur in the highly complex feedback digital instrumentation and can also clarify fault tree construction when multiple sources of information are present in such systems. |
Paper EC303 Preview
Author and Presentation Info
Lead Author Name: Edward Chen (echen2@ncsu.edu)
Bio: Edward is a 4th year Ph.D. candidate researching risk and reliability in digital instrumentation and control systems at North Carolina State University under the direction of Dr. Nam Dinh. His primary areas of focus include risk quantification and model development in conventional PLC based as well as data-driven ML control and information systems. He has worked with multiple groups including Kairos power as a simulation developer for transient cases as well as a contractor for Idaho National Laboratories under the Light Water Sustainability Project. He has also worked on ARPA-e projects such as the Near Autonomous Management and Control system and has developed multiple data-driven autonomous safety systems.
Country: United States of America
Company: North Carolina State University
Job Title: Research Assistant
Bio: Edward is a 4th year Ph.D. candidate researching risk and reliability in digital instrumentation and control systems at North Carolina State University under the direction of Dr. Nam Dinh. His primary areas of focus include risk quantification and model development in conventional PLC based as well as data-driven ML control and information systems. He has worked with multiple groups including Kairos power as a simulation developer for transient cases as well as a contractor for Idaho National Laboratories under the Light Water Sustainability Project. He has also worked on ARPA-e projects such as the Near Autonomous Management and Control system and has developed multiple data-driven autonomous safety systems.
Country: United States of America
Company: North Carolina State University
Job Title: Research Assistant