AI-powered condition monitoring and service life prediction

Unplanned failures and fixed maintenance intervals cost you money and valuable service life—mostly because the actual condition of components is unknown. We bring clarity to this issue: We build updatable digital twins that link physics-based service life models with operational sensor data, inspection images, and maintenance documents, and we develop data-driven methods for identifying stress fields and residual stresses. In doing so, we focus on your specific components, load scenarios, and damage mechanisms. The result: condition-based rather than worst-case-based maintenance planning, an extended safe service life for expensive components, higher material utilization, and a complete, traceable chain of evidence for certification and warranty purposes. You make decisions based on the actual condition—not on safety margins.

Digital twin for service life prediction

We integrate physics-based service life models with operational sensor data, inspection images, and maintenance documents via a component- and material-specific knowledge graph, continuously updating the component’s condition. This allows you to reduce unplanned downtime, plan maintenance based on actual condition rather than worst-case scenarios, extend the safe service life of expensive components, lower spare part and downtime costs, and maintain a seamless chain of evidence for certification and warranty purposes.

R&D services:

• Development of a component- and material-specific knowledge graph
• Integration of physics-based service life models with operational data
• Automated analysis of inspection images to update condition data
• Processing Maintenance Documents Using Text-Based Models
• Creation of a chain of evidence for certification and warranty

AI-supported stress field and stress state analysis

We determine stress fields and stress states for various operating scenarios using AI-supported evaluation methods as well as physics-based FEM simulations and experimental data. This enables you to achieve higher material utilization and increased efficiency—for example, higher allowable cyclic load cycles for turbine components.

R&D services:

• AI-supported analysis of stress fields for defined operating scenarios
• Combination of FEM simulation data with experimental stress measurements
• Identification of critical load cases and stress states
• Assessment of safety margins to increase material utilization
• Integration of results into your component design and approval processes

AI-assisted residual stress estimation for welding processes

We estimate welding residual stresses based on geometric and process parameters, thereby partially replacing time-consuming FE simulations. This enables you to achieve faster, more cost-effective component design through realistic residual stress profiles.

R&D services:

• Estimation of residual stresses based on geometric and process parameters
• Calibration of models using selected FE simulations or measurement data
• Partial replacement of complex welding FEM simulations
• Provision of realistic residual stress profiles for component design
• Application to various welding geometries and processes

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