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Denver, CO|Sheraton Denver
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AI at work: Southern Nuclear’s adoption of Copilot agents drives fleet forward
Southern Nuclear is leading the charge in artificial intelligence integration, with employee-developed applications driving efficiencies in maintenance, operations, safety, and performance.
The tools span all roles within the company, with thousands of documented uses throughout the fleet, including improved maintenance efficiency, risk awareness in maintenance activities, and better-informed decision-making. The data-intensive process of preparing for and executing maintenance operations is streamlined by leveraging AI to put the right information at the fingertips for maintenance leaders, planners, schedulers, engineers, and technicians.
T. M. Tsai , H. P. Chou
Nuclear Science and Engineering | Volume 114 | Number 2 | June 1993 | Pages 141-148
Technical Paper | doi.org/10.13182/NSE93-A24026
Articles are hosted by Taylor and Francis Online.
A sensor fault detection method combining the single sensor parity relation (SSPR) with the likelihood ratio test (LRT) is described. The SSPR is in an algebraic form that correlates system dynamics with multistep readings of a sensor and is therefore fast running. The scheme can easily be duplicated for each sensor of interest and thus has advantages for modular design and parallel processing. In the fault detection architecture, residuals generated from the SSPR module are examined by an LRT module for failure signatures. The likelihood ratios are maximized according to the fault occurrence time to improve detection sensitivity and are then calculated using a recursive form to match the speed of the SSPR module. The proposed concept is demonstrated with hypothetical sensor failures for pressurizer instruments. Comparisons with the Kalman filtering technique and the sequential probability ratio test are discussed.