Leading US airline achieves better maintenance and higher ROI with Amygda's solution
Amygda detected 92% of the failures and isolated the specific component at fault, enabling timely and more efficient maintenance across the fleet.
A leading US airline approached Amygda with recurring hydraulic system issues on its aircraft fleet. The goal was to use Amygda's advanced ML techniques to pinpoint which component was at fault, and provide advance notice to repair it.
  • Challenge
    The airline experienced long-term issues with the Hydraulic Systems on aircraft. Common discrepancies included reports of Alternating Current Motor Pumps (ACMPs) leaking, not turning on when selected, or popping the circuit breaker. Amygda's goal was to isolate which component was most likely at fault, predict failure, and provide ample advance notice to facilitate timely maintenance action.
  • Solution
    The POC (Proof of Concept) consisted of two phases of training and testing - Phase 1 proving out the development of a predictive model, and Phase 2 proving out the quick turnaround in model improvement. Applying a data-driven approach, along with collaboration with the airline maintenance team, Amygda developed a model in half the expected delivery time, at a 78% detection rate, improving to 92% with rapid iteration.

    The solution was built on the back of 3 years of Flight Operations and Quality Assurance (FOQA) data for the entire fleet at 4Hz, equating to approximately 650,000 flights, accompanied by maintenance data. Through data-driven labelling techniques, Amygda was able to re-label and date the incidents pertaining to ACMP faults. By modelling the operation of the hydraulic system, we generated effective system cycles which allowed us to engineer and trend homogenous features and detect impending failures. Amygda's approach, along with feature engineering, helped us not only predict ACMP faults, but pinpoint exactly which of the hydraulic ACMPs was at fault. Our solution differentiated between real faults and planned maintenance activities, which were previously leading to false positives.
  • Impact
    Amygda's solution allowed the airline to confidently apply a proactive approach to maintaining their hydraulic system. Previously, the system was being topped up with fluids as part of routine maintenance to keep it going, instead of addressing the underlying fault.

    Amygda's methods provided the airline with visibility on the actual behaviour of the system and ample time for taking remedial action. It also allowed the airline to go back to their pump supplier with evidence of faulty pumps which were being sent back to the airline after maintenance.
  • 92% detection rate
    Amygda's solution achieved a 92% failure detection rate, allowing for timely maintenance.
  • Higher ROI on maintenance
    The airline had full visibility of the hydraulic system's behaviour, highlighting when the fault was with the supplier.
  • Root cause analysis
    Amygda isolated the specific component responsible for the failure, enabling timely and more efficient maintenance.
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