
This innovative system provides a digital twin of the asset that offers real-time analytics on the structural condition through a web-based platform. By using a network of enterprise-grade structural sensors, it captures and records data on sensitive properties, which is then processed and transmitted through IoT integration. The cloud-based analytics enable immediate access to critical information, supporting decision-making and optimising maintenance schedules. This approach is particularly valuable for high-risk structures, those requiring life extension, and remote or unmanned facilities that are difficult to access, providing an efficient and comprehensive solution for asset management.
Many industrial structures, especially those in remote or high-risk environments, face challenges in maintaining safety and structural integrity due to lengthy manual data collection and delayed analysis. The traditional reliance on inspectors and consultants results in slow decision-making processes, increased labour costs, and potentially unsafe operational delays. Moreover, identifying vulnerable structural components and predicting their failure requires a targeted approach that traditional methods struggle to provide. As a result, there is a critical need for a more automated and data-driven solution that can monitor structural health in real-time, extend inspection intervals, and improve the overall management of assets.
This solution revolutionises structural health monitoring by combining real-time data capture, IoT integration, and advanced cloud-based analytics into a cohesive system. By automating data processing from enterprise-grade sensors and converting it into detailed engineering information, the technology provides accurate and timely insights into the structural condition of assets. The digital twin interface enables users to monitor local damage progression in critical members like conductors and primary joints continuously. This innovative approach not only reduces man-hours and extends inspection intervals but also enhances decision-making capabilities for continued service, particularly after incidents or for life-extension assessments.
This technology addresses the inefficiencies in traditional structural health monitoring by automating real-time data streaming and analytics. It employs a physics engine that processes data from structural sensors to simulate current conditions and predict future failures. By delivering these insights through a web-based digital twin, it facilitates immediate decision-making, particularly after incidents like impacts or fires. It enables a targeted approach by identifying structural members that require urgent inspection and optimises inspection intervals by focusing on condition-based assessments. The system also supports the extension of the structural service life, enhancing both safety and cost-effectiveness for remote and high-risk structures.
The competitive edge of this technology lies in its ability to deliver real-time structural condition analytics and predictions through an integrated, web-based platform. Unlike traditional methods that rely on periodic manual inspections and delayed analysis, this system automates data collection and processing, providing instant access to critical information. The approach reduces maintenance costs by optimising inspection schedules and extends the life of assets by allowing for targeted, condition-based inspections. Its applicability to remote, unmanned, and high-risk facilities further enhances its value proposition, making it a superior choice for industries looking to improve safety, reliability, and operational efficiency.