Development of a Tele-operated Underwater Robotic System for maintaining a light-water type power reactor
The safety and reliability of nuclear power plants is very important. An inspection and maintenance of nuclear power plants should be achieved simultaneously. However, the limitation of a radiation exposure makes it difficult to work in nuclear power plants. Therefore, a tele-operated robotic system is very useful for inspecting and maintaining the components of nuclear power plants because it reduces the level of a radiation exposure to the human operators and it improves the reliability of an operation. A tele-operated underwater robotic system is needed for detecting and removing a loose part in a nuclear reactor vessel. A loose part, which can come from any failed component or an item left during a construction, refueling or maintenance like metallic parts, bolts, nuts and washers, affects the safety of a nuclear power plant. In this paper, an underwater robotic system is developed for inspecting the bottom of a nuclear reactor vessel and a reactor coolant system and also for removing some particles in them. In order to evaluate the performance of the developed underwater robotic system, an experiment was performed in a small swimming tank. In the experiments, the developed robotic system could inspect the state of the tank and remove some particles successfully
Automated Object Detection for Visual Inspection of Nuclear Reactor Cores
Abstract Remote visual inspection is a common approach to understanding the health of key components and substructures within nuclear power plants, particularly in difficult to access and high dosage areas. Interpretation of inspection footage is a manually intensive procedure and challenges arise in localizing and dimensioning defects directly from a video feed, which may be subject to uncertainty from a range of sources such as lens distortion, nonuniform lighting, and lack of depth from a monocular camera system. A common approach to addressing these issues is to develop a scaling factor based on identifying a reference object of known dimensions in the image and using this to size regions of interest. Manual, accurate identification of these reference objects is onerous, time consuming, and prone to variation across different human experts, therefore, robust identification of suitable reference objects in an automated, reliable, and repeatable manner is of significant value. In this paper we evaluate two approaches for the automated detection of reference objects in the inspection of graphite cores in the United Kingdom’s fleet of advanced gas-cooled reactors (AGRs). The first method is a multistep approach using tools from mathematical morphology. The approach uses a genetic algorithm to “grow” suitable structuring elements, refine the order of operations, and remove operations proposed by the human designer that have a negative impact on performance. The second approach uses semantic segmentation, a technique which is normally applied to scene labeling in computer vision applications, applied to produce a binary mask, separating the reference object from the background. We show that this second method performs significantly better than the mathematical morphology approach when applied to the identification of brick interface keyways in AGR inspection images. Though improved in terms of accuracy, it is recognized that a greater initial effort is required to train the approach, and as it utilizes black-box neural network approaches, the greater transparency offered by the mathematical morphology approach is lost. While explicability of techniques is often a highly desirable characteristic of automated analysis techniques applied to health assessment within nuclear power plants, the results of the reference object detection can be made explicit to the end user, ensuring that the human analyst is retained within the decision-making process thus mitigating the need for transparency.
Fault Diagnosis and CAN Bus/Ethernet Redundancy Design of a Monitoring and Control System
The ship monitoring system is used to monitor and control the working status of various equipment on the ship. The navigational safety of the ship is affected directly by the stable working of the monitoring system. In this paper, a ship monitoring system with fault-type judgment function and bus redundancy is designed to improve system reliability and reduce fault checking cost. A redundant Ethernet/CAN bus communication network is constructed in the system, which connects multiple simulation nodes. The simulation node can simulate the faults that may occur during the communication process and feeds back the fault information to the host computer. The host computer will analyze and display the corresponding fault type on the user interface with the feedback information is received. The system reconfigures the network route autonomously when the simulation node has a communication fault. Routing of fault nodes can be switched to redundant routing, and the load balancing of the communication network can be maintained. Through experimental tests, the system can accurately locate faults and achieve fast switching of redundant routes between buses. Hence, the reliability of data transmission is improved.