Nav: Home

System automatically detects cracks in nuclear power plants

February 17, 2017

WEST LAFAYETTE, Ind. - A new automated system detects cracks in the steel components of nuclear power plants and has been shown to be more accurate than other automated systems.

"Periodic inspection of the components of nuclear power plants is important to avoid accidents and ensure safe operation," said Mohammad R. Jahanshahi, an assistant professor in Purdue University's Lyles School of Civil Engineering. "However, current inspection practices are time consuming, tedious and subjective because they involve an operator manually locating cracks in metallic surfaces."

Other automatic crack detection algorithms under development often do not detect cracks in metallic surfaces because the cracks are usually small, have low contrast and are difficult to distinguish from welds, scratches and grind marks. The new system, called CRAQ, for crack recognition and quantification, overcomes this limitation by using an advanced algorithm and a powerful "machine learning" technique to detect cracks based on the changing texture surrounding cracks on steel surfaces.

Findings are detailed in a research paper published this week in Computer-Aided Civil and Infrastructure Engineering. The paper is available online at http://onlinelibrary.wiley.com/doi/10.1111/mice.12256/full.

The automated approach could help improve the state of the nation's infrastructure, recently given an overall grade of D+ by the American Society of Civil Engineers, he said.

"One reason we have a grade of D+ for the infrastructure is insufficient inspection," said Jahanshahi, director of Purdue's Smart Informatix Laboratory. "So we want to have more frequent inspection using robotic systems to collect data."

The nation operates 99 commercial nuclear power plants, which account for about 20 percent of total U.S. electricity generation. Aging can result in cracking, fatigue, embrittlement of metal components, wear, erosion, corrosion and oxidation.

"Cracking is an important factor in aging degradation that may cause leaking and result in hazardous incidents," Jahanshahi said. "For instance, the Millstone nuclear power station in Connecticut had an accident in 1996 that was caused by a leaking valve, and the accident cost $254 million. In 2010, the Vermont Yankee Nuclear Power Plant had an accident where deteriorating underground pipes leaked radioactive tritium into groundwater supplies, resulting in $700 million in damage."

Complicating the inspection process is that nuclear reactors are submerged in water to maintain cooling.

"Consequently, direct manual inspection of reactor internals is not feasible due to high temperatures and radiation hazards," Jahanshahi said. "So remotely recorded videos at the underwater reactor surface are used for inspection. However, recent testing has identi?ed a need for increased reliability associated with identifying cracks from reviews of live and recorded data. The results indicate that this capability is degraded by human involvement in identifying cracks, even when identi?cation should be easy."

Other automated crack-detection systems under development are designed for processing single images, whereas the new method processes multiple video frames, providing more robust results. Findings show the system outperformed two others under development.

"In contrast to other methods that only focus on detecting cracks in one image, we propose a method called Bayesian data fusion that tracks detected cracks in video frames and fuses the information obtained from multiple frames," Jahanshahi said. "Moreover, we can ?lter out falsely detected cracks and increase the reliability and robustness of crack detection by using Bayesian decision theory," which determines the probability that an object is a crack or a false alarm. The system assigns "con?dence levels" automatically assessing whether the detected cracks are real, outlining the cracks with color-coded boxes that correspond to these confidence levels. For example, if the algorithm assigns a high confidence level to a crack, the box outline is red. The processing procedure takes about a minute.

"Then, a technician could do a manual inspection to confirm that there is a crack," Jahanshahi said. A YouTube video is available at: https://youtu.be/b87OgGBIR78.

The research paper was authored by doctoral student Fu-Chen Chen; Jahanshahi; doctoral student Rih-Teng Wu; and Chris Joffe, technical leader for Non-destructive Evaluation at the Electric Power Research Institute (EPRI), a nonprofit organization funded by the electric utility industry.

Researchers recorded videos using an underwater camera system scanning 304 stainless steel specimens containing cracks and also features such as welds, grinding marks and scratches.

Future research will include work to develop a more accurate and more fully automated system using advanced simulations and computational software.

"We are currently working on the second version of the software by developing deep learning algorithms to detect cracks for this application where we have significantly improved the performance of the system using Constitutional Neural Networks," Jahanshahi said.

The researchers have filed a patent application through the Office of Technology Commercialization of the Purdue Research Foundation.
-end-
The research was supported in part under a contract with EPRI.

Writer: Emil Venere, 765-494-4709, venere@purdue.edu

Source: Mohammad R. Jahanshahi, 765-494-2217, jahansha@purdue.edu

Related websites:

IMAGE CAPTION:

A new automated system developed at Purdue University detects cracks in the steel components of nuclear power plants. Here, the system accurately distinguishes between cracks and other features, outlined in red-colored boxes. (Image care of EPRI)

A publication-quality photo is available at https://news.uns.purdue.edu/images/2016/jahanshahi-cracks.jpg

IMAGE CAPTION:

Mohammad R. Jahanshahi, left, an assistant professor in Purdue's Lyles School of Civil Engineering, and doctoral student Fu-Chen Chen review results using the new system. (Purdue University image/Erin Easterling)

A publication-quality photo is available at https://news.uns.purdue.edu/images/2017/jahanshahi-craq.jpg

ABSTRACT

A Texture-based Video Processing Methodology Using Bayesian Data Fusion for Autonomous Crack Detection on Metallic Surfaces

Fu-Chen Chen1, Mohammad R. Jahanshahi2, Rih-Teng Wu2, and Chris Joffe3

1School of Electrical and Computer Engineering, Purdue University 2Lyles School of Civil Engineering, Purdue University 3Electric Power Research Institute

Regular inspection of the components of nuclear power plants is important to improve their resilience. However, current inspection practices are time consuming, tedious, and subjective: they involve an operator manually locating cracks in metallic surfaces in the plant by watching videos. At the same time, prevalent automatic crack detection algorithms may not detect cracks in metallic surfaces because these are typically very small and have low contrast. Moreover, the existence of scratches, welds, and grind marks leads to a large number of false positives when state-of-the-art vision-based crack detection algorithms are used. In this study, a novel crack detection approach is proposed based on local binary patterns (LBP) to identify crack patches in each video frame. These patches are then grouped to form a bounding box for each crack. Furthermore, the bounding boxes corresponding to each crack in different video frames are tracked and aggregated using Bayesian decision theory to enhance the robustness and reliability of detection. The proposed approach is also optimized to reduce computation time. The performance of the proposed approach was assessed by using several inspection videos, and the results showed that it is accurate and robust in cases where state-of-the-art crack detection approaches fail.Note to Journalists: A copy of the research paper is available from Emil Venere, Purdue News Service, at 765-494-4709,

venere@purdue.edu. A YouTube video is available https://youtu.be/b87OgGBIR78 and other video is accessible on Google Drive https://goo.gl/vezwH1. The video was produced by Erin Easterling, Purdue College of Engineering digital producer, 765-496-3388, Easterling@purdue.edu

System automatically detects cracks in nuclear power plants

Purdue University

Related Civil Engineering Articles:

Music was form of resistance for women during Civil Rights Movement
'Freedom songs' were key in giving motivation and comfort to those fighting for equal rights, in addition to helping empower Black women to lead others when formal leadership positions were unavailable.
1 in 5 civil monetary penalties due to EMTALA violations involved psychiatric emergencies
Nearly one in five civil monetary penalty settlements related to Emergency Medical Treatment and Labor Act (EMTALA) violations involved psychiatric emergencies.
Post-bypass survival linked to civil status and class
Civil status, education, and income are factors shown to be clearly associated with duration of survival after a bypass operation.
Facial recognition software to identify Civil War soldiers
Photo Sleuth may help uncover the mysteries of nearly 4 million photographs of Civil War-era images.
MU scientists use smartphones to improve dismal rating of nation's civil infrastructure
In the United States, aging civil infrastructure systems are deteriorating on a massive scale.
Civil engineers at Concordia University devise a cost-saving solution for cities
Why fix a road today if it's slated to be ripped up for new sewers next summer?
Accessing your own genomic data is a civil right but requires strategies to manage safety
The Genetic Information Nondiscrimination Act of 2008 expanded individuals' access to genetic information by forcing changes to the HIPAA Privacy Rule.
New research disputes claims that climate change helped spark the Syrian civil war
A new study shows that there is no sound evidence that global climate change was a factor in causing the Syrian civil war.
The crew of the Civil War submarine HL Hunley likely died from airblast injuries
The crew of the Civil War submarine HL Hunley likely died from airblast injuries, according to a study published Aug.
Civil unrest after Freddie Gray's death harms health in Baltimore mothers
The April 2015 civil unrest associated with Freddie Gray's death while in police custody caused a significant spike of stress in mothers of young children living in affected neighborhoods, according to new research from the University of Maryland School of Medicine (UM SOM).
More Civil Engineering News and Civil Engineering Current Events

Top Science Podcasts

We have hand picked the top science podcasts of 2019.
Now Playing: TED Radio Hour

Risk
Why do we revere risk-takers, even when their actions terrify us? Why are some better at taking risks than others? This hour, TED speakers explore the alluring, dangerous, and calculated sides of risk. Guests include professional rock climber Alex Honnold, economist Mariana Mazzucato, psychology researcher Kashfia Rahman, structural engineer and bridge designer Ian Firth, and risk intelligence expert Dylan Evans.
Now Playing: Science for the People

#541 Wayfinding
These days when we want to know where we are or how to get where we want to go, most of us will pull out a smart phone with a built-in GPS and map app. Some of us old timers might still use an old school paper map from time to time. But we didn't always used to lean so heavily on maps and technology, and in some remote places of the world some people still navigate and wayfind their way without the aid of these tools... and in some cases do better without them. This week, host Rachelle Saunders...
Now Playing: Radiolab

Dolly Parton's America: Neon Moss
Today on Radiolab, we're bringing you the fourth episode of Jad's special series, Dolly Parton's America. In this episode, Jad goes back up the mountain to visit Dolly's actual Tennessee mountain home, where she tells stories about her first trips out of the holler. Back on the mountaintop, standing under the rain by the Little Pigeon River, the trip triggers memories of Jad's first visit to his father's childhood home, and opens the gateway to dizzying stories of music and migration. Support Radiolab today at Radiolab.org/donate.