arXiv:2606.04582v1 Announce Type: cross Abstract: Real-time monitoring of the temperature distribution within components and sub-structures is a challenging topic in many systems due to restrictions on feasible sensor locations. While machine learning (ML) proves a versatile tool in many applications, its adoption for high-resolution thermal monitoring is hindered by the availability of high-quality datasets for training. In this work, we propose a novel approach for generating datasets for industrial applications based on randomized physics-based simulations. We demonstrate the approach in a
The increasing sophistication of AI and simulation techniques is enabling new approaches to complex scientific and engineering problems.
This work introduces a method to overcome data limitations in critical monitoring applications, potentially accelerating AI adoption in industrial sectors.
The ability to generate high-quality synthetic datasets for niche industrial applications reduces reliance on expensive or impossible real-world data collection.
- · Industrial IoT sector
- · Predictive maintenance companies
- · AI/ML consulting firms
- · Manufacturing sector
- · Companies reliant solely on traditional sensor arrays
- · Legacy thermal monitoring solutions
Improved real-time monitoring capabilities in complex industrial environments mitigating risks.
Accelerated development and deployment of AI-driven optimization and control systems in manufacturing and energy.
Enhanced efficiency and safety across critical infrastructure by leveraging AI for previously unobservable physical states.
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