Reliability engineering for systems – quantitative, uncertainty-based, and data-driven across the entire development process.
The System Reliability Department develops methods for analyzing system reliability, functional safety, and reliability engineering in mechanical engineering systems—with a focus on sustainability and circularity, resilience, and safety. Proven methods such as probabilistic FMEA are supplemented by approaches that account for uncertainties and combine load and stress analyses with experimental system analyses under complex conditions. A key objective is the digitalization of testing and validation procedures, as well as the use of Artificial Intelligence (AI) and Machine Learning (ML) for virtualized system reliability analysis and evaluation. This allows reliability engineering to be integrated early and centrally into the development process, thereby supporting the development of complex, safety-critical systems more efficiently and quickly.
Unsere Kompetenzschwerpunkte
Experimental and virtual assessment of system reliability and safety using the following components:
- Analysis and derivation of load assumptions for reliability assessment
- Development of complex test benches for customized full-system tests under the combined influence of environmental media and mechanical loads
- Further development of methodological failure analyses, in particular the extension to include probabilistic methods of LCA and reliability methods for a more efficient and simultaneous evaluation of both attributes
- System and reliability analyses under uncertainty
- AI-supported approaches for lifetime predictions and system classification
Mehrwert für Industrie und Partner
Reliability is not something that is established only during the final validation phase. It must be quantifiable, traceable, and assessable under conditions of uncertainty as early as the development phase – serving as the foundation for sound technical decisions.
Mit uns können Sie:
- Quantify failure probabilities, service life, and safety margins and determine them under conditions of uncertainty – rather than relying on deterministic approximations.
- Targetedly reduce validation efforts and identify incorrect assumptions early on by linking load/stress analyses with experimental data and digital models.
- Identify robust decisions early in the development phases and improve the traceability and reproducibility of results through the integration of AI-supported forecasts and digitized testing procedures.
To achieve this, we integrate experimental, virtual, and failure analysis methods throughout the development cycle into a comprehensive evaluation approach. Combined with expert consulting, this results in a flexible toolkit of methods that is tailored to each customer’s specific challenges.
As a result, customers benefit from a more solid basis for decision-making during development: validation becomes more targeted, verification processes become more transparent, and product reliability is not merely demonstrated at the end but is prepared throughout the development process.
Einbindung in unsere Leistungs- und Forschungsfelder
Based on this, the System Reliability department offers the following key service areas:
Reliability Assessment & Lifetime Prediction
Probabilistic methods to quantify failure probabilities and lifetimes under uncertainty; linking load and stress analyses with experimental data to enable robust predictions and safety assessments.
Digital Twins & Simulation
Development and application of digital twins to represent system behavior under real and extrapolated operating conditions; integration of AI/ML for data-driven model calibration as well as condition and lifetime prediction.
Validation & Prototyping
Development and digitalization of testing and validation procedures; combining physical experiments with simulation-based approaches to efficiently verify complex systems under varying boundary conditions.
Sustainability and Circularity
Assessment of reliability and lifetime in the context of sustainable usage concepts; analysis of reusability, degradation, and remaining useful life as a basis for circular strategies.
Consulting and Training
Methodological consulting for integrating reliability engineering into development processes; training on probabilistic methods, uncertainty quantification, and data-driven analysis and prediction approaches.