Reliability and availability

Condition Diagnosis and Prognosis

Reliable monitoring and precise forecasting of component and system conditions is crucial for maximum operational reliability and cost-effectiveness. The actual condition of components depends heavily on their actual use and therefore often deviates from the original assumptions. This is why we rely on intelligent models and state-of-the-art analysis methods to detect potential damage at an early stage and enable targeted measures to be taken. This requires a continuous survey of operational stresses and the availability of suitable degradation models. We develop individual physical, data-driven and experience-based models to derive a condition diagnosis or prognosis from the available information.

Our approach:

Data acquisition: Continuous acquisition of relevant operational/environmental data and other damage parameters.
Data processing: Intelligent analysis and structuring of the recorded information.
Condition assessment: Identification of critical deviations and early detection of damage.
Damage prognosis: Prediction of possible failures for optimized planning.
Recommendations for action: Development of customized maintenance strategies for maximum availability.

From software development and implementation to the provision of hardware solutions - we accompany you on the way to an efficient, data-based maintenance strategy.

Basis for condition monitoring

Smart data collection

Successful condition monitoring requires the targeted acquisition of relevant data. The combination of simulation, testing and data-driven methods provides a robust basis for decision-making. Based on our understanding of failure mechanisms, we develop customised data acquisition concepts.

Our approach:

Targeted analysis: identification of critical failure points and relevant parameters
Intelligent sensor technology: development and integration of suitable sensor solutions
Efficient data pipeline: acquisition, processing and storage of data
Virtual sensors: derivation of damage indicators from existing measurement data

Basis for a forecast

Individual degradation models

To maximise the service life and reliability of your systems, accurate damage models are essential. They enable robust condition assessment and prediction by relating actual loads and operating conditions to system limits. This allows the current condition and remaining service life to be assessed realistically.

Our approach to degradation modelling:

Identification of key parameters: analysis of relevant influencing factors for accurate prediction of degradation mechanisms
Physics-based and data-driven models: combination of experimental data, simulation and advanced algorithms
Data-driven optimisation: continuous adaptation of models to current operating conditions
Prediction of remaining useful life and failures: reliable outputs for predictive, condition-based maintenance

Our digital twins, reduced-order models and machine learning methods provide precise insights and support the sustainable optimisation of your systems.

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