Together with industrial partners LMS
(Belgium), Dassault and EADS-Airbus and Flemish and French
universities, a EUREKA project was started in 2001 to tackle the
problem of flutter detection in in-flight analysis and the appearance
of spurious modes in vibration data. Due to changes in speed and air
pressure, the modal characteristics of an airplane change
continuously during in-flight measurements. If the damping of a
certain mode suddenly decreases the aircraft may experience heavy
vibrations, putting a lot of stress on the body of the craftm which
may ultimately lead to the destruction of the aircraft. When these
strong vibrations occur, the aircraft is said to go into flutter. In
order to detect flutter before the aircraft is destroyed, a
continuous monitoring of the plane during in-flight measurements is
necessary. Fast, preferrably recursive techniques can then be used to
update a dynamical model describing the structural vibrations.
Vibration modes and their dampings can easily be derived from these
models. A problem however lies in the fact that together with the
identified vibration modes of the plane, all identification
procedures that are commonly used today also return some to many
modes that have no physical relevance. This results from the fact
that in modal analysis, the modelling order is usually chosen much
higher that the true system order to reduce the bias on the
estimates. It is important to separate these spurious modes from the
true ones, a cumbersome procedure which heavily relies on user
interaction.
In light of the Flite project, several methods
were investigated to automatize the detection of spurious modes.
Within SCD we studied the relation between subspace identification
and balanced model reduction. It was found that subspace
identification methods are fast, reliable, and therefore ideally
suited for use in online analysis. Detection of spurious modes proved
much harder, as a strong theoretical theory about spurious modes is
lacking. Several heuristic techniques were proposed, involving
non-balanced model reduction, modal energy analysis and pole/zero
cancellations.
Researcher(s): & Ivan Markovsky, Katrien De Cock, Jeroen Boets, Bart Vanluyten