Hervé Morel

Vibration-based fault detection of sharp bearing faults in helicopters

Many signal processing tools have been developed by the mechanical and signal processing community to find the characteristic symptoms of sharp bearing faults (like localized spalling) from vibratory analysis. However the context of helicopter imposes a limited sampling frequency regarding the observed phenomena, many noisy vibrations and flight regimes. The performances of the classical methods are limited in such an environment mainly in identifying fault frequencies. Local bearing faults induce temporal periodic and impulsive patterns that produce redundant harmonics in the spectral domain. In this article four methods are proposed to take advantage of that redundancy. These methods provide an estimator of the fault frequency and an indicator of the quality of that estimation. These indicators are used to assess the severity of the fault. The four methods are then tested on synthesized and flight data in order to illustrate and discuss their efficiency.

Vibration-Based Fault Detection of Accelerometers in Helicopters

Vibration-based monitoring is an approach for health analysis of helicopters. However, accelerometers and other sub-elements that convert and transmit vibrations to the recording system must not corrupt the signal. These elements are prone to defects because of external injuries during flights or maintenance. This paper will deal with a method to tackle problems of loosening and mechanical shocks. The objective is to perform a passive detection of accelerometer failures from the vibrations without knowledge of previous recordings. Experiments of mechanical failures have been carried out on a shaker to reproduce in flight vibrations, and it appears that the loosening and mechanical shocks introduce asymmetry and random peaks in the temporal vibrations. Loosening was successfully detected but mechanical shocks were much harder to detect as a result of strong dependences in the vibratory environment. Loosening data sets from flights confirm experimental observations and the proposed detection method allows for the detection of the fault with better performance than standard indicators.

Operational data Processing and Techniques for Innovative MAINTenance (OPTIMAINT)

The road to Condition-Based Maintenance (CBM) in the aerospace industry is a long and arduous one. To accelerate the journey, a collaborative research program called OPTIMAINT (Operational data Processing and Techniques for Innovative MAINTenance) was established in Singapore in 2008. Such a unique partnership involving an end user the Republic of Singapore Air Force (RSAF), the original equipment manufacturer Eurocopter (EC) with Eurocopter South East Asia (ESEA), and its corporate research entity European Aeronautic Defence and Space company (EADS) Innovation Works (IW), provides an unprecedented opportunity of sharing a common vision while acquiring invaluable insights into the many challenges of achieving CBM. The project aims to demonstrate the ability of HUMS (Health and Usage Monitoring Systems) to cover all degradation modes for selected components. Thus, fault detectability is at the heart of OPTIMAINT: a significant part of the project is dedicated to data investigation, data processing and degradation/anomaly detection. This paper covers the key steps of the ongoing project and shares some promising results obtained from historical HUMS data through the use of statistical and data mining techniques.