SLIDE 3 Micro Materials Center
Head: Prof. Dr. Sven Rzepka
Prognostics and Health Manage- ment (PHM)
- Developing the required infras-
tructure, sensors, electronic HW
- Studying and characterizing the
Failure Modes and Mechanisms by thorough Effect Analyses for PoF & data driven approaches
- Providing appropriate solutions
to the data acquisition, manage- ment, and secure data transfer
- Performing data fusion for reach-
ing at an integrated single health assessment, diagnostics, and prognosis score per application
- Establishing highly efficient yet
precise metamodeling and mod- el order reduction schemes that can be executed in each of the individual cars locally assisted by self-learning capabilities provid- ed by cloud service
Prognostic Health Management
Experimental & Design Area Infrastructure, Sensors, Hardware
Prognostic Health Management
Experimental & Design Area Infrastructure, Sensors, Hardware FMMEA / Physics of Failure, Data Driven Approach
Prognostic Health Management
Engineering Focus Experimental & Design Area Infrastructure, Sensors, Hardware FMMEA / Physics of Failure, Data Driven Approach Data Acquisition, Management & Transfer
Prognostic Health Management
Engineering Focus Experimental & Design Area Algorithm Framework Infrastructure, Sensors, Hardware FMMEA / Physics of Failure, Data Driven Approach Data Acquisition, Management & Transfer Health Assessment, Diagnostics, Prognostics
Prognostic Health Management
Engineering Focus Experimental & Design Area Algorithm Framework Infrastructure, Sensors, Hardware FMMEA / Physics of Failure, Data Driven Approach Data Acquisition, Management & Transfer Health Assessment, Diagnostics, Prognostics
Prognostic Health Management
Meta Models, Model Order Reduction Engineering Focus Experimental & Design Area Algorithm Framework Infrastructure, Sensors, Hardware FMMEA / Physics of Failure, Data Driven Approach Data Acquisition, Management & Transfer Health Assessment, Diagnostics, Prognostics
Prognostic Health Management
Meta Models, Model Order Reduction
Acceleration Models Variation- induced fai- lure risks Critical Parameters in Extreme Environments Data Readout & Processing Infrastructure Integration
Collectors (Sensors, Canaries, …) Consolidation of Health Assessment Data (Sources) Data Exploration and Hypothesis Generation PoF Model Generation and Validation Demonstration
Integration Standardized Safe and Secure Data Exchange Model Improvement for Signals Real T ime Prediction Capabilities
Dedicated stops and three methodology research phases Strategy: PHM integrated into ECS
