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ADHER
Automated Diagnosis for Helicopter Engines and Rotating parts

Tags: Air

Background

Aircraft availability, in-flight reliability and low-cost maintenance are major concerns for helicopter operators. HUMS (Health Usage Monitoring System) implementing sensor-based monitoring is an enabling technology seeking to provide a condition-based maintenance (CBM) relying on automated diagnosis/prognosis of the health of aircraft components. One challenge for HUMS is 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 the use of fixed alarm thresholds. The automated analysis of fleet operating data on engine and rotating parts recorded by onboard sensors is a major scientific objective to reach adaptive, reliable, and low-cost HUMS systems. This objective will be explored in this project by addressing:

  • Performance of simultaneous oil debris monitoring (ODM) and vibration monitoring using available ODM and vibration sensors;
  • Analysis of new physical models for ODM and vibration characteristics of helicopter rotating parts (gearboxes, bearings, etc.) to calibrate ‘ageing effects’ and ‘progressive emergence of failures’;
  • Design and validation of innovative software tools dedicated to self-adaptive diagnosis/ prognosis of potential failures of helicopter rotating parts.

Objectives

The project’s main goal is to enable ‘fleet-scale’ health monitoring for helicopters with robust failure diagnosis and prognosis, relying on multi-sensor monitoring and automated analysis of sensor-recorded data. This will reduce false alarm rates and maintenance costs and increase operational aircraft availability, enabling efficient scheduling of preventive maintenance.

The main scientific and technological objectives of this project are:

  • To obtain a better understanding of the physical behaviour of ODM, vibration and acoustic sensors through new theoretical models and through a series of bench test experiments on helicopter gearboxes, especially in terms of ‘ageing effects’ and ‘progressive emergence of failures’ for rotating parts;
  • To define innovative self-adaptive algorithms enabling data-driven automatic learning to analyse time evolutions of sensor data and to generate anticipated health diagnosis, taking account of ‘vehicle usage context variables’;
  • To test these algorithms on helicopter fleet vibration data;
  • To evaluate the feasibility of automated health monitoring of helicopter fleets by self-adaptive software analysis of (ODM + vibrations) data.

Description of work

The project work breakdown structure includes three sub-projects (SP):

  • SP1 is concerned with project management, scope specification, results evaluation and dissemination towards potential end users.
  • SP2 addresses experimental data acquisition and physical modelling of three key categories of sensors known to have discriminating capabilities to monitor the health of helicopter rotating parts: oil debris monitoring, vibrations and acoustic emissions. The main goal of SP2 is to reduce the rate of undetected faults.
  • SP3 focuses on innovative multi-sensor diagnosis software tools and explores the diagnosis potential of self-learning algorithms. It includes five work packages addressing a helicopter fleet sensor database, external variable impact on vibration-based diagnosis, multi-sensor data fusion for diagnosis, automatic elimination of defective sensor data and the overall technical evaluation of the project outputs. The main goal of SP3 is to reduce the rate of false alarms.

Results

It is expected to acquire/obtain the following knowledge elements and results:

  • Oil debris fault detection rates for various damage modes and operating contexts;
  • Quantitative impact of contextual variables on defects;
  • Experimental evaluation of the potential of acoustic emission sensors for diagnosis;
  • Database of vibration recordings for a helicopter fleet;
  • Self-adaptive software tools for health diagnosis;
  • Methods and tools for automated multi-sensor data fusion for diagnosis;
  • Methods and tools for automated elimination of degraded sensor data;
  • Bench test evaluation of defect diagnosis on helicopter rotating parts;
  • Validation concepts and industrialisation feasibility;
  • Perspective assessment for fleet-scale automated multi-sensor diagnosis.

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