nuclear-news

A.I. system finds cracks in nuclear reactors

 Futurity, by Emil Venere-Purdue November 8th, 2017  A new system that uses artificial intelligence to find cracks captured in videos of nuclear reactors could help reduce accidents as well as maintenance costs, researchers report.

“Regular inspection of nuclear power plant components is important to guarantee safe operations,” says Mohammad R. Jahanshahi, an assistant professor in the Lyles School of Civil Engineering at Purdue University.

“However, current practice is time-consuming, tedious, and subjective and involves human technicians reviewing inspection videos to identify cracks on reactors,” Jahanshahi says.

The fact that nuclear reactors are submerged in water to maintain cooling complicates the inspection process. Consequently, direct manual inspection of a reactor’s components is not feasible due to high temperatures and radiation hazards. Technicians review remotely recorded videos of the underwater reactor surface, a procedure that is vulnerable to human error.

Checking the tape

Researchers are proposing a “deep learning” framework called a naïve Bayes-convolutional neural network to analyze individual video frames for crack detection. An innovative “data fusion scheme” aggregates the information from each video frame to enhance the overall performance and robustness of the system……..

Cracks lead to leaks

The United States is the world’s largest supplier of commercial nuclear power, which provides around 20 percent of the nation’s total electric energy. Between 1952 to 2010, there have been 99 major nuclear power incidents worldwide that cost more than $20 billion and led to 4,000 fatalities. Fifty-six incidents occurred in the United States.

“One important factor behind these incidents has been cracking that can lead to leaking,” Jahanshahi says. “Nineteen of the above incidents were related to cracking or leaking, costing $2 billion. Aging degradation is the main cause that leads to function losses and safety impairments caused by cracking, fatigue, embrittlement, wear, erosion, corrosion, and oxidation.”……..

The research team also is using deep learning to detect corrosion in photographs of metal surfaces, a technology that could potentially inspect structures such as light poles and bridges. The researchers reported the details of that work in a paper accepted for publication in the Journal of Structural Health Monitoring.

Future research will include work to further improve the technologies.

The researchers detail their work in the journal IEEE Transactions on Industrial Electronics. Computer engineering doctoral student Fu-Chen Chen was Jahanshahi’s coauthor.

Source: Purdue University

November 9, 2017 - Posted by Christina Macpherson | safety, USA