A review on predictive maintenance technique for nuclear reactor cooling system using machine learning and augmented reality

Abstract

<span lang="EN-US">Reactor TRIGA PUSPATI (RTP) is the only research nuclear reactor in Malaysia. Maintenance of RTP is crucial which affects its safety and reliability. Currently, RTP maintenance strategies used corrective and preventative which involved many sensors and equipment conditions. The existing preventive maintenance method takes a longer time to complete the entire system’s maintenance inspection. This study has investigated new predictive maintenance techniques for developing RTP predictive maintenance for primary cooling systems using machine learning (ML) and augmented reality (AR). Fifty papers from recent referred publications in the nuclear areas were reviewed and compared. Detailed comparison of ML techniques, parameters involved in the coolant system and AR design techniques were done. Multiclass support vector machines (SVMs), artificial neural network (ANN), long short-term memory (LSTM), feed forward back propagation (</span><span lang="EN-US">FFBP), graph neural networks-feed forward back propagation (GNN-FFBP) and ANN were used for the machine learning techniques for the nuclear reactor. Temperature, water flow, and water pressure were crucial parameters used in monitoring a nuclear reactor. Image marker-based techniques were mainly used by smart glass view and handheld devices. A switch knob with handle switch, pipe valve and machine feature were used for object detection in AR markerless technique. This study is significant and found seven recent papers closely related to the development of predictive maintenance for a research nuclear reactor in Malaysia.</span>

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“…Multicolor images are made up of the three monochrome-based images, which are R (red), G (green), and B (blue), also known as primary or component images. The importance of RGB colors related to the camera on a mobile phone that captures certain images for processing, such as the recent mobile app augmented reality research development [21]. This is why the typical techniques that were used for monochrome images are further applied to process multicolor images by working on the three basic-colored images separately.…”

An overview of emerging trends in robotics and automation

“…Within the realm of robotics, AR has the potential to offer operators real-time information regarding the surroundings of the robot, hence simplifying the process of controlling and monitoring the robot. In a variety of contexts, including gaming and entertainment, as well as teaching and training, this technology is applicable [13], [18].…”

“…As AR technology, such as smart glasses, become more widely available and advanced, the number of people using them continues to increase. The possibility for more immersive and intuitive human-machine interactions is the driving force behind this trend, which is generating a dynamic and bright future for augmented reality within the context of the robotics and automation technology [13], [18], [44], [45].…”

AI-Driven approach for enhancing nuclear reactor safety predictive anomaly detection and risk assessment

“…This research explores the integration of machine learning algorithms with IoT technologies for predictive maintenance in nuclear reactors. By continuously monitoring equipment health and performance, the AI-driven system can predict component failures in advance, allowing for proactive maintenance and reducing downtime (Gohel et al, 2020) (Farley et al, 2018) (Nor et al, 2022).…”