Using Computer Vision to Reduce Human Errors of Operating on the Wrong Control Valves in Nuclear Power Plants
Authors: Sophie Kochanek, Jinding Xing, Alper Yilmaz, George Edward Gibson, Pingbo Tang
Abstract: Nuclear power plants are complex systems that have many modules for errors to occur. Each year in the United States, an average of approximately 80 accidents happen, of which 50 (or 62%) are related to human errors (Nuclear Energy Agency, 2020). These errors reduce the efficiency of plants and have cost, safety, and environmental consequences. Nuclear operators manipulate control objects, like valves, to complete maintenance procedures involved in power generation by directing water around a plant (U.S. Bureau of Labor Statistics, 2021). However, there are many identical valves in a small area which can lead to errors handling the wrong valve. In practice, valves are identified using lockout tags that can contain as little as its normal position, or large blocks of information on its use, position, or contents. (Occupational Health and Safety Administration, 2011). This method of differentiating valves is insufficient because tags may be damaged and hard to read, or contain inadequate information to be useful in an emergency. Current efforts to reduce valve operation errors use AI tools to diagnose faults, like valve damage, and suggest best practice procedures. However, no existing solution identifies valves to prevent operational errors due to misidentification. Computer vision techniques that use machine learning, like object detection algorithms, can provide a solution to this problem. This paper explores the integration of computer vision and real-time sensors monitoring water systems to reduce errors operating on the wrong valves. This approach analyzes sensor data and uses object detection algorithms to identify control valves as workers are walking through a power plant to minimize the possibility of mixing them up. The sensor log analysis algorithm identifies critical valves that require action, then the computer vision algorithm, the YOLO version 3 object detection algorithm, highlights them in real-time. While developing the sensor data algorithm, we created a simulation that models valves controlling water flow between tanks. Within a nuclear power plant the simulation represents the long cycle cleanup operation where boiled feedwater cannot contaminate cooled feedwater. It captures random noise and is a virtual environment for testing operators on how they react to system errors like tank overflows or sudden influxes of water. For example, one valve oscillates around its input value to represent the uncertainty from imperfect or leaky valves. Other noise from large influxes of water is created by an oscillation of the inflow with a pulse function. Testing the developed algorithms on data from a mechanical room in Posner Hall at Carnegie Mellon University indicates their potential for reducing real-time operation errors. Specifically, testing results confirmed that control object operation is an issue and that computer vision provides a promising solution to this problem. Although this project is limited to merely recognizing control valves due to the method of object detection, it can have significance in reducing emission leakage, improving efficiency and reliability, and advancing technology for safety. Such limitations necessitate future work using other object detection algorithms to compare results as well as integrating spatial data to differentiate between identical valves.ReferencesNuclear Energy Agency, and International Atomic Energy Agency. “Nuclear Power Plant Operating Experience from the IAEA/NEA International Reporting System for Operating Experience 2015-2017.” Nuclear Power Plant Operating Experience, 2020, doi:10.1787/2bdd0383-en.“1910.147 - The control of hazardous energy (lockout/tagout).” (2011). Occupational Safety and Health Administration, U.S. Department of Energy, <https://www.osha.gov/laws-regs/regulations/standardnumber/1910/1910.147> (Nov. 3, 2021). “51-8011 Nuclear Power Reactor Operators.” U.S. Bureau of Labor Statistics, U.S. Bureau of Labor Statistics, 31 Mar. 2021, www.bls.gov/oes/current/oes518011.htm.
Keywords: Nuclear power plants, operational errors, object detection
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