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2026 ANS Annual Conference
May 31–June 3, 2026
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.
Johannes Prock, Eggert Ohlmer, Michael Labeit
Nuclear Technology | Volume 97 | Number 1 | January 1992 | Pages 52-62
Technical Paper | Fission Reactor | doi.org/10.13182/NT92-A34625
Articles are hosted by Taylor and Francis Online.
A computer program for the detection of abrupt changes in nonhardware redundant measurement signals that uses different methods of analytical redundancy is developed by the Gesellschaft für Reaktorsicherheit, Garching, Federal Republic of Germany. This program, instrumental fault detection and identification (IFDI) module, validates in real time output signals of power plant components that are scanned at a fixed rate. The IFDI module, implemented on an IBM-compatible personal computer (PC) with an 80386 processor, is tested on-line at the light water reactor off-normal behavior investigations (LOBI)-MOD2 facility in the Joint Research Centre, Ispra, Italy, during the loss-offeedwater experiment BT-15/BT-16 on November 22, 1990. The measurement signals validated by the IFDI module originate from one of the two LOBI-MOD2 facility’s steam generators. During the experiment, sensor faults are simulated by falsifying the measurement signals through electrical resistances arranged in series. Questions about the signal validation software and the steam generator’s model are dealt with briefly, while the experimental environment and the results obtained are discussed in detail.