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Automated Diagnosis for Helicopter Engines and Rotating parts


Helicopters availability, in-flight reliability and low cost maintenance are major concerns for helicopters operators. HUMS (sensor based health monitoring) is an enabling technology seeking to provide a Condition-Based Maintenance (CBM) relying on automated diagnosis / prognosis for aircraft components health. Challenges for HUMS are to implement automated low cost CBM systems, as an alternative to periodic physical inspections. Existing HUMS technologies tend to generate high rates of false alarms due to use of fixed alarm thresholds. Automated software analysis of fleet operating data recorded by on board sensors is a major scientific direction to reach adaptive , reliable, and low cost HUMS systems. The ADHER R and D project will improve HUMS performances in 3 directions : 1) Include simultaneous Oil Debris Monitoring (ODM) and Vibrations monitoring, using available ODM and Vibrations sensors 2) Analyze new physical models for ODM and Vibrations characteristics of helicopter rotating parts (gearboxes, bearings, etc.) to calibrate ageing effects and progressive emergence of failures 3) Conceive and validate innovative softwares dedicated to Auto-Adaptive Diagnosis / Prognosis of rotating parts potential failures for helicopters. Based on Self-Learning Algorithms, these softwares will analyze helicopters fleet data and their time evolution. New physical models and Automatically Learned functional relationships will improve links between sensor recorded features, helicopter usage,and regime characteristics. The project main goal is to enable "fleet scale" health monitoring, with robust failure diagnosis / prognosis, relying on multi-sensors monitoring , and automated software analysis of sensor recorded data. This will reduce false alarms rates and maintenance costs, and increase operational aircraft availability, enabling efficient scheduling of preventive maintenance.

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Aeroport Marseille Provence

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Participants (5)