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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Deep Space: The new frontier of radiation controls
In commercial nuclear power, there has always been a deliberate tension between the regulator and the utility owner. The regulator fundamentally exists to protect the worker, and the utility, to make a profit. It is a win-win balance.
From the U.S. nuclear industry has emerged a brilliantly successful occupational nuclear safety record—largely the result of an ALARA (as low as reasonably achievable) process that has driven exposure rates down to what only a decade ago would have been considered unthinkable. In the U.S. nuclear industry, the system has accomplished an excellent, nearly seamless process that succeeds to the benefit of both employee and utility owner.
Miltiadis Alamaniotis, Andreas Ikonomopoulos, Lefteri H. Tsoukalas
Nuclear Technology | Volume 177 | Number 1 | January 2012 | Pages 132-145
Technical Paper | Nuclear Plant Operations and Control | doi.org/10.13182/NT12-A13333
Articles are hosted by Taylor and Francis Online.
Nuclear power plants are complex engineering systems comprised of many interacting and interdependent mechanical components whose failure might lead to degraded plant performance or unplanned shutdown with loss of power generation and negative economic impact. As a result, continuous component surveillance and accurate prediction of their failing points is necessary for their on-time replacement. In this paper, a probabilistic kernel approach for intelligent online monitoring of mechanical components is presented. Specifically, the probabilistic kernel notion of Gaussian processes (GPs) is applied to the distribution prediction of a component's degradation trend. The proposed method exploits the learning ability of a GP and updates its prediction using a feedback mechanism. The methodology is tested on actual turbine blade degradation data for a variety of topologies (i.e., kernels). The GP estimations are compared to those obtained with a nonprobabilistic, kernel-based machine learning algorithm, the support vector regression (SVR). The comparison outcome clearly demonstrates that GP prediction accuracy outperforms SVR in the majority of the cases while providing a predictive distribution instead of point estimates as SVR does.