We will discuss how AI can help structuring heterogenous legacy tests data in a consolidated data base (enabler to any AI/ML implementation).
A lot of legacy data exists but heterogenous format prevent from implementing AI analysis on this data.
• Test result analysis using ML:
o Fault localization on avionics systems
o No Go anticipation by identifying weak patterns during test program execution
o Predictive maintenance, Remaining Useful Time computation
• Open Neural Network Exchange integration in SIL/HIL systems
• How ML can accelerate test program/test hardware design and validation
Thomas Ricou, AI Technical Leader, Spherea