A NASA turbofan simulated engine dataset with 100 engine failures was made available to Amygda. Each of the engines had 24 sensor parameters.
All 100 engine data sets had less than 1% deviation from normal operating data. To the human eye there were no signs or warnings of impending failure. Furthermore, the data was unlabelled and had no historical context.
Amygda's methods were applied to the engines and built an orchestration of algorithms, from our library of over 200 algorithms.
We found data driven insights and turned them into actionable insights to predict and prevent further engine failures.
Using Amygda's methods, we were able to clearly demonstrate damage and impending failure 50 cycles in advance.
Our platform could be implemented quickly to predict and prevent these failures in the future.
Ability to predict and prevent jet engine failures and avoid disruption
50 cycles early
Allowing the customer to plan repair or replacement in advance
Our empirical methods without physics-based modelling work
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