EKSO will be the industrial end-user of the pilot. It will contribute the training content. HOLO will provide the training infrastructure using cloud-based XR streaming, and leverage AI data analytics provided to optimize the learning experience. INNOV will implement the Chat-GPT generative AI interface for the Operator 5.0.
Water networks require regular maintenance to provide safe and reliable water to communities. Therefore, the maintenance of water network can greatly benefit from Condition-Based Maintenance (CBM) practices such as Predictive Maintenance. To benefits from such maintenance models and practices, maintenance workers (e.g., technicians) need to acquire new skills that would allow them to access and interpret analytical outcomes (e.g., ML-based Remaining Useful Life (RUL) calculations, AI-driven detection of defects). XR environments provide perfect playgrounds for training workers on such new skills in cost-effective, yet realistic and safe ways. To exploit the full potential of data-driven maintenance practices, such trainings should be tailored to the varying levels of the workers’ digital literacy and skills. The pilot utilizes XR streaming functionalities from HOLO's platform, allowing operators/ trainees to acquire hands-on experience and feedback without the physical equipment or in-person training, reducing cost and risks associated with traditional methods. The program will integrate personalized and AI- based XR content, tailoring the training process to individual knowledge and skills.
Industrial Process to be supported in terms of training>=3; XR streaming objects latency<1s; Average reduction of training time & costs>30%; 5-scale rating of site technicians’ satisfaction from the training (i.e., excellent social performance)
This Use Case aims to leverage the power of extended reality to provide immersive and interactive training experiences for smart pipes deployers and operators. Workers/technicians from EKSO and/or its customers’ network will use XR devices, such as AR headsets or smart glasses, to perform visual inspections of smart pipes using HOLO’s ISAR streaming functionality. The XR5.0 training platform will overlay real-time data, off-line data, and visual guidance on the equipment, highlighting areas that may require closer examination. Moreover, an AI-powered computer vision system will analyse live video feeds to detect anomalies, such as corrosion, leaks, or cracks, and alerts the worker to potential issues. This will enable workers to quickly identify and address quality control issues, towards reducing the risk of equipment failure and improving the overall equipment efficiency (OEE).
This Use Case will extend the use of the XR steaming technology to CBM and predictive maintenance processes. Specifically, XR streaming will be used to provide live training sessions for CBM, where trainers can remotely guide trainees based on simulated scenarios in real-time. In this case, HOLO's ISAR enabled AR 3S Pro cloud-based product will be used to enable real-time streaming of high-quality 3D content optimising computational efficiency through the deployment of cloud/edge workflows. The streamed content will be enhanced with information produced by XR5.0’s AI models and algorithms such as predictive maintenance, anomaly detection, and quality control. This will enable trainees to experience realistic/enriched scenarios and learn how to use advanced technologies and tools in a safe and controlled environment. As part of the UC a generative AI (ChatGPT-like) interface will be provided to Operator 5.0 to boost the effectiveness of their interactions with the XR representations. To this end, the OpenAI platform will be used and extended with ad-hoc data.