Implementation Artificial Intelligence System Using Automated Object Detection (Identification, Monitoring, and Live Reporting) for Sustainability Asset Management on 5 Different Power Plant in Indonesia
Budi Hidayat, Muchammad jati Nugroho, Egga Bahartyan, Rifky Raymond · 2022
Computer vision has been expanding at a rapid pace over the last decade to reach the equivalent of human vision level. However, even now, it is possible to emulate human vision for performing complex visual tasks faster and even more effectively than humans do. This paper discusses a novel approach to implementing computer vision in power plant asset management to detect unsafe acts and conditions. This approach involves enhancements for sense (media detection), think (algorithm), and act (notification and reporting) which can be tailored to our needs. The case study involves implementation on 5 major power plants in Indonesia and more in 2022–2023 with a multi-billion-dollar asset base and spread over various locations. With the system we developed, we can build an application that can detect various hazards and automatically report them in real-time to ensure that they are managed, controlled, or eliminated. So the risk of losing assets can be maintained appropriately. Furthermore, it is not hard to think of other domains in asset management where the ability of computer vision can be leveraged. For example, certain areas like preventive and predictive maintenance, condition base monitoring, and visual inspection can be improved to help our organization improve its asset value.