Key highlights
90 percent
improved data quality with AI
50 percent
faster fault detection with AI
Challenges
1.
Faced with missing or corrupted data due to sensor failures, network disruptions, and maintenance, reducing reliability.
2.
Incurred high costs and delays from physical tests and simulations for predictive maintenance and optimization.
3
Failed to capture complex sensor data relationships, leading to inaccurate predictions and control actions.
Solution
1.
Used Gen AI to mirror sensor data and generate synthetic data for simulating various conditions.
2.
Applied conditional generative adversarial networks (cGANs), diffusion models, and transformer-based models to create realistic sensor data for specific inputs, such as temperature, pressure, and load.
3
Enabled scenario analysis by simulating sensor outputs for different conditions, such as increased machine load or sensor drift.
4
Developed an interactive interface or API for users to input parameters and generate synthetic sensor data for forecasting and testing.
5.
Continuously updated and refined the digital twin using real-time sensor data and feedback loops from simulations.
6.
Used reinforcement learning to optimize parameter selection for data generation.
7.
Generated synthetic operational data to test failure scenarios, optimize maintenance schedules, and stress-test equipment.
8.
Integrated with optimization models to provide prescriptive recommendations for industrial and IoT processes.
Impact
Improved data quality Reduced missing and corrupted data impact by up to 90 percent with AI-driven reconstruction.
Faster fault detection Identified anomalies 30–50 percent faster than traditional rule-based systems.
Optimized maintenance Minimized unplanned downtime by up to 25 percent using predictive analytics.
Enhanced decision-making Delivered real-time insights and simulations, improving process efficiency by 10–20 percent.
Scalable and cost-effective Lowered reliance on physical tests, cutting costs in industrial and IoT deployments.