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Research Papers

A Novel Method for the Design of Proximity Sensor Configuration for Rotor Blade Tip Timing

[+] Author and Article Information
David H. Diamond

Centre for Asset Integrity Management,
Department of Mechanical and
Aeronautical Engineering,
University of Pretoria,
Pretoria 0002, South Africa
e-mail: dawie.diamond@up.ac.za

P. Stephan Heyns

Centre for Asset Integrity Management,
Department of Mechanical and
Aeronautical Engineering,
University of Pretoria,
Pretoria 0002, South Africa
e-mail: stephan.heyns@up.ac.za

Contributed by the Technical Committee on Vibration and Sound of ASME for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received November 24, 2017; final manuscript received March 29, 2018; published online May 7, 2018. Assoc. Editor: Costin Untaroiu.

J. Vib. Acoust 140(6), 061003 (May 07, 2018) (8 pages) Paper No: VIB-17-1510; doi: 10.1115/1.4039931 History: Received November 24, 2017; Revised March 29, 2018

Blade tip timing (BTT) is a noncontact method for measuring turbomachinery blade vibration. Proximity sensors are mounted circumferentially around the turbomachine casing and used to measure the tip displacements of blades during operation. Tip deflection data processing is nontrivial due to complications such as aliasing and high levels of noise. Specialized BTT algorithms have been developed to extract the utmost amount of information from the signals. The effectiveness of these algorithms is, however, influenced by the circumferential spacing between the proximity sensors. If the spacing is suboptimal, an algorithm can fail to measure dangerous blade vibration. This paper presents a novel optimization approach that determines the optimal spacing between proximity sensors.

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Figures

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Fig. 1

Principle behind BTT: (a) a compressor fan row with a broken out casing is shown with five proximity sensors (numbered S1 through S5) above the row and (b) the deflected blade tip arrives earlier than the undeflected tip due to the tip's deflection

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Fig. 2

Different measurements taken by three different sensor arrangements for an EO 6 vibration of size 200 μm

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Fig. 3

The relationship between increasing condition number and increasing BTT algorithm error is illustrated. Two EOs are used: 8 and 12. Each marker represents a single simulation with a randomly generated sensor configuration.

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Fig. 4

The error function as a heat map for a three sensor BTT optimization where θ1 is fixed to 0 rad and the EOs between 2 and 10 are taken into account. Lighter shades indicate better solutions. The sensor spacings are constrained to be monotonically increasing.

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Fig. 5

Progress of PSO algorithm for four different runs of the same problem

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Fig. 6

Condition numbers for different EOs for the run 2 optimal result

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Fig. 7

Condition numbers for different EOs when weighting the EO values lower than eight more than the remaining EO values. The difference between the weighted and unweighted EOs are also shown: (a) Weighted EO condition numbers and (b) condition number difference.

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