Abstract: A system and method for predicting maintenance and providing optimized operational performance in industrial operations on a Metaverse platform, is described. In one aspect, the system implements AI/ML engines for anomaly detection and predictive analytics to control future failures, facilitate planned maintenance, and provide actionable recommendations to control future failures. The system combines data with AR/VR-based digital twin solutions for real-time troubleshooting and maintenance training. The system detects anomalies in industrial assets using sensor and IIoT data and provides predictive analytics for capturing failures and actionable recommendations, provides improved overall equipment effectiveness, enhanced device and system utilization, simulation of processes using data, and prescription uptime plans, achieving superior productivity gains and predictable uptime.

Abstract: Embodiments of system and method for a sustainable digital twin system, are described. In one aspect, the sustainable digital twin system includes a cooperative working of multiple engines to enable managing virtual assets corresponding to each physical asset in a facility. The sustainable digital twin system may implement a manage of change (MoC) mechanism to track and manage modifications of the virtual assets in the facility. The modifications of the virtual assets may be simulated in a work-in-progress environment. The impact of the modifications of the modified virtual asset and the other virtual assets in the facility may be analyzed. Upon analysis, an end user may review suggestions or recommendations provided by the sustainable digital twin system, update and implement the modifications in the work-in-progress environment, before propagating the modifications to a production environment.