One of the main challenges of the Blade Tip Timing (BTT) measurement method is to be able to determine the sensing position of the probe relative to the blade tip. It is highly important to identify the measurement point of BTT since each point of the blade tip may have a different vibration response. This means that a change in measurement position will affect the amplitude, phase and DC component of the results obtained from BTT data. This increases the uncertainty in the correlation between BTT measurements and Finite Element (FE) modelling. Also, the measurement point should ideally be located to measure as many modes as possible. This means that the probe’s position should not coincide with a node, or a position at which the sensor misses the blade tip. Changes in the sensing position usually arise from the steady state movements of the blades (change in mean displacement). Such movements are caused by changes to the static (thermal and pressure) loading conditions that result from changes in the rotational speed. Such movements usually have a constant direction at normal operating conditions, but the direction may fluctuate if the machine develops a fault. There are three main types of movements of the sensing position that are considered in this paper: (1) axial movement; (2) blade lean; (3) blade untwist. Ideally, the sensing position is known based on the geometries of both the blade and the probe, but due to different types of movements of the blade this position is lost. Very few works have researched the extraction of the sensing position. Such preliminary works have required a pre-knowledge of mode shapes and additional instrumentation. The aim of this paper is to present a novel method for the identification of the BTT sensing position of the probes relative to a blade tip, which can be used to quantify the above movements. The developed method works by extracting the steady state offset from measurements of blade tip displacements over a number of revolutions as the speed changes from zero to a certain value. Hence, that part of the offset that is due to the angular positioning error of the probes (outside the scope of this work) is cancelled out (since it is independent of speed). The change in steady state offset is then processed to identify the three possible movements. The new method is validated using a novel BTT simulator that is based on the modal model of the FE model of a bladed disk (“blisk”). The simulator generates BTT data for prescribed changes to the sensing position. The validation tests show that the novel algorithm can identify such movements within a 2% margin of error.

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