MRO Analytics and Prediction

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Credit:

Ernsting/DLR

The research area MRO Analytics and Prediction advances the science and application of Prognostics and Health Management (PHM) to enable data-driven maintenance across complex systems. We develop robust methods to transform heterogeneous, high-dimensional data into reliable, context-aware insights for diagnostics and prognostics.

By integrating physics-informed models, statistical learning, and AI techniques, we predict system health, estimate Remaining Useful Life (RUL), and optimize maintenance planning. Our work spans from developing PHM capabilities for components such as engines, batteries and sensors to integrating modules into complete system architectures for aircraft, vehicles, and systems. We focus on deploying PHM systems in operational environments, validating and scaling them for industrial use and serial production.

Through assessment and decision support, we turn data into actionable guidance for maintenance, safety, and operations—bridging the gap between raw sensor data and informed decision-making to enhance reliability, safety, and lifecycle efficiency.

Key Topics

Data Processing

  • Feature engineering: Creation and transformation of relevant features from raw data to describe key performance indicators
  • Data augmentation: Artificial expansion of the dataset by creating variations of existing data to enhance model robustness and generalization
  • Uncertainty analysis/estimation: Quantification of the uncertainty in model predictions to better assess their reliability and confidence

Diagnostics

  • Anomaly detection: Identification of unusual patterns or deviations in data that may indicate faults or unexpected events
  • Damage analysis: Examination of data to detect, characterize, and assess the extent of damage in a system or structure
  • System health assessment: Evaluation of the overall condition and performance of a system to determine its operational status

Prognostics

  • Degradation modelling: Modelling and prediction of the gradual deterioration of system components over time
  • Fault propagation modelling: Analysis of how faults spread through a system to understand their impact and progression
  • System-level prognostics: Forecast of the remaining useful life and future condition of an entire system based on current and historical data