Session 7: Predictive Maintenance, Condition Monitoring and failure prediction
Watch our final part of a 7 part series.
During this session, we explain how prognostic health management and failure prediction methodologies are applied to quantify the remaining useful lifetime (RUL) of components, subsystem and machines under its specific real-use conditions – instead of determining the ultimate lifetime on a statistical average.
Data analytics and virtual sensing technology are used to gather deeper system insight during condition monitoring. By connecting the real data with system simulation or CAE 3D simulation in a digital twin, we can take targeted preventive actions before a predicted failure occurs.
Meet the speaker
Ralf Leis
Service Project Manager for „Data Analytics & Durability“
Graduated from Kaiserslautern University with a degree in Civil Engineering in 2001 and worked for Simcenter Engineering Services team since then as project engineer first, nowadays as service program manager and business development manager for data analytics and durability. He has a long-standing experience in the planning and execution of different kind of durability projects from load measurements, data processing and analysis up to CAE based fatigue life analysis.
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