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Research Papers: Gas Turbines: Structures and Dynamics

Model Validation for Identification of Damage Dynamics

[+] Author and Article Information
Ryan J. Madden, Alexander H. Pesch

Center for Rotating Machinery
Dynamics and Control (RoMaDyC),
Cleveland State University,
Cleveland, OH 44115-2214

Jerzy T. Sawicki

Center for Rotating Machinery
Dynamics and Control (RoMaDyC),
Cleveland State University,
Cleveland, OH 44115-2214
e-mail: j.sawicki@csuohio.edu

Contributed by the Structures and Dynamics Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received September 16, 2014; final manuscript received October 1, 2014; published online December 9, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(6), 062506 (Jun 01, 2015) (7 pages) Paper No: GTP-14-1553; doi: 10.1115/1.4028845 History: Received September 16, 2014; Revised October 01, 2014; Online December 09, 2014

Robust control techniques have allowed engineers to create more descriptive models by including uncertainty in the form of additive noise and plant perturbations. As a result, the complete model set is robust to any discrepancies between the mathematical model and actual system. Experimental unfalsification of the model set leads to the guarantee that the model and uncertainties are able to recreate all experimental data points. In this work, such a robust control relevant model validation technique is applied to structural health monitoring in order to (1) detect the presence of damage and (2) identify the damage dynamics when used in conjunction with model-based identification. Additionally, the robust control relevant model validation technique allows for a novel quality measure of the identified damage dynamics. Feasibility of the method is demonstrated experimentally on a rotordynamic crack detection test rig with the detection and identification of a change in structure. Further insight is gained from application of the method to seeded damage on a rotor levitated on active magnetic bearings (AMBs) in the form of a local reduction in stiffness.

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Figures

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Fig. 1

Model validation problem

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Fig. 2

Model-based identification strategy

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Fig. 3

Experimental apparatus photo (top) and schematic (bottom)

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Fig. 4

Rotor cross section with mode shapes

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Fig. 5

Damaged data with invalidation points plotted against the healthy data and upper uncertainty bound

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Fig. 6

Damaged data with invalidation points plotted against healthy data and nominal model

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Fig. 7

Comparison of healthy data (+) and damaged data (o) to the uncertainty bound (– –) and nominal model data (×)

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Fig. 8

Identified change in dynamics due to the cut in the shaft

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Fig. 9

Simulated damage experimental apparatus

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Fig. 10

Rotor model cross section with simulated damage location

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Fig. 11

Damaged data for 5% local stiffness reduction with invalidation points plotted against the healthy data and uncertainty bounds

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Fig. 12

Damaged data for 5% local stiffness reduction with invalidation points plotted against healthy data and nominal model

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Fig. 13

Identified change in dynamics due to a 5% local reduction in stiffness

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