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Research Papers: Gas Turbines: Structures and Dynamics

Noninvasive Parameter Identification in Rotordynamics Via Fluid Film Bearings—Linking Active Lubrication and Operational Modal Analysis

[+] Author and Article Information
Ilmar Ferreira Santos

Department of Mechanical Engineering,
Technical University of Denmark,
Kgs. Lyngby 2800, Denmark
e-mail: ifs@mek.dtu.dk

Peter Kjær Svendsen

Department of Mechanical Engineering,
Technical University of Denmark,
Kgs. Lyngby 2800, Denmark
e-mail: peter.kj.svendsen@gmail.com

1Corresponding author.

Contributed by the Structures and Dynamics Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received September 6, 2016; final manuscript received October 21, 2016; published online February 7, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(6), 062507 (Feb 07, 2017) (9 pages) Paper No: GTP-16-1441; doi: 10.1115/1.4035447 History: Received September 06, 2016; Revised October 21, 2016

In recent years, theoretical and experimental efforts have transformed the conventional tilting-pad journal bearing (TPJB) into a smart mechatronic machine element. The application of electromechanical elements into rotating systems makes feasible the generation of controllable forces over the rotor as a function of a suitable control signal. The servovalve input signal and the radial injection pressure are the two main parameters responsible for dynamically modifying the journal oil film pressure and generating active fluid film forces in controllable fluid film bearings. Such fluid film forces, resulting from a strong coupling between hydrodynamic, hydrostatic, and controllable lubrication regimes, can be used either to control or to excite rotor lateral vibrations. If “noninvasive” forces are generated via lubricant fluid film, “in situ” parameter identification can be carried out, enabling evaluation of the mechanical condition of the rotating machine. Using the lubricant fluid film as a “noninvasive calibrated shaker” is troublesome, once several transfer functions among mechanical, hydraulic, and electronic components become necessary. In this framework, the main original contribution of this paper is to show experimentally that the knowledge about the several transfer functions can be bypassed by using output-only identification techniques. This paper links controllable (active) lubrication techniques with operational modal analysis, allowing for in situ parameter identification in rotordynamics, i.e., estimation of damping ratio and natural frequencies. The experimental analysis is carried out on a rigid rotor-level system supported by one single pair of pads. The estimation of damping and natural frequencies is performed using classical experimental modal analysis (EMA) and operational modal analysis (OMA). Very good agreements between the two experimental approaches are found. Maximum values of the main input parameters, namely, servovalve voltage and radial injection pressure, are experimentally found with the objective of defining ranges of noninvasive perturbation forces.

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Figures

Grahic Jump Location
Fig. 1

Photo of the test apparatus—the dashed box contains the rotor-lever system, which is schematized in Fig. 2. The components of the test apparatus are as follows: (1) AC motor and frequency converter, (2) control unit for the frequency converter, (3) tachometer, (4) electromagnetic shaker, (5) force transducer, (6) amplifier for the force transducer, (7) force transducer for the static load, (8) displacement sensor, (9) and (10) accelerometers, (11) and (12) amplifiers for the accelerometers, (13) oil filter, (14) servovalve, (15) pressure sensor, (16) frame, (17) dSpace unit, and (18) amplifier for the electromagnetic shaker.

Grahic Jump Location
Fig. 2

Schematic of the test rig operational principle and mechanical model

Grahic Jump Location
Fig. 3

Experimental FRFs in (m/N) as a function of the rotor angular velocity and static load conditions: static load of (a) 1400 N (0.24 MPa) and (b) 2800 N (0.48 MPa)

Grahic Jump Location
Fig. 4

Experimental behavior of the natural frequency and damping ratio as a function of the rotor angular velocity estimated via EMA (least-squares algorithm) and OMA (ITD, SSI, and FDD algorithms). Static load condition: 1400 N (0.24 MPa). Dynamic perturbation: electromagnetic shaker.

Grahic Jump Location
Fig. 5

Experimental behavior of the natural frequency and damping ratio as a function of the rotor angular velocity estimated via EMA (least-square algorithm) and OMA (ITD, SSI, and FDD algorithms). Static load condition: 2800 N (0.48 MPa). Dynamic perturbation: electromagnetic shaker.

Grahic Jump Location
Fig. 6

Experimental FRFs in (m/bar) as a function of the rotor angular velocity and static load conditions: static load of (a) 1400 N (0.24 MPa) and (b) 2800 N (0.48 MPa)

Grahic Jump Location
Fig. 7

Experimental behavior of the natural frequency and damping ratio as a function of the rotor angular velocity estimated via EMA (least-square algorithm) and OMA (ITD, SSI, and FDD algorithms). Static load condition: 1400 N (0.24 MPa). Dynamic perturbation: ALB as an actuator.

Grahic Jump Location
Fig. 8

Experimental behavior of the natural frequency and damping ratio as a function of the rotor angular velocity estimated via EMA (least-square algorithm) and OMA (ITD, SSI, and FDD algorithms). Static load condition: 2800 N (0.48 MPa). Dynamic perturbation: ALB as an actuator.

Grahic Jump Location
Fig. 9

Curve-fitted mean values of damping ratio and natural frequency with 95% confidence interval as a function of the rotor angular velocity and static load. Dynamic perturbation: electromagnetic shaker.

Grahic Jump Location
Fig. 10

Curve-fitted mean values of damping ratio, natural frequency, and coherence with 95% confidence interval as a function of the rotor angular velocity and static load. Dynamic perturbation: ALB as an actuator.

Grahic Jump Location
Fig. 11

Curve-fitted mean values of damping ratio, natural frequency, and coherence with 95% confidence interval as a function of (a) the supply pressure, (b) DC amplitude of the servovalve input signal, and (c) AC amplitude of the servovalve input signal. Dynamic perturbation: ALB as an actuator.

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