Parameter Estimation Techniques for Modal Analysis

Brown, David L.; Allemang, Randall J.; Zimmerman, Ray D.; Mergeay, Max

doi:10.4271/790221

Cited by 196 publications

(110 citation statements)

References 4 publications

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“…These classical system identification algorithms include the Ibrahim time domain (ITD) method (Ibrahim and Mikulcik 1977), the multiple-reference Ibrahim time domain (MITD) method (Fukuzono 1986), the least-squares complex exponential (LSCE) method (Brown et al 1979), the polyreference complex exponential (PRCE) method (Vold et al 1982), and the eigensystem realization algorithm (ERA) (Juang and Pappa 1985). In contrast to two-stage approaches, one-stage system identification methods such as the data-driven stochastic subspace identification (SSI-DATA) method (Van Overschee and De Moor 1996) can be used to identify modal parameters based on output-only measurements directly.…”

Section: Submitted To Journal Of Structural Engineering Ascementioning

confidence: 99%

System Identification of Alfred Zampa Memorial Bridge Using Dynamic Field Test Data

Moaveni

Conte

et al. 2009

J. Struct. Eng.

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The Alfred Zampa Memorial Bridge (AZMB), a newly built long-span suspension bridge, is located 32km northeast of San Francisco on interstate Highway I-80. A set of dynamic field tests were conducted on the AZMB in November 2003, just before the bridge opening to traffic. These tests provided a unique opportunity to identify the modal properties of the bridge in its as-built condition with no previous traffic loads or seismic excitation. A benchmark study on modal identification of the AZMB is performed using three different state-of-the-art system identification algorithms based on ambient as well as forced vibration measurements. These system identification methods consist of: (1) the multiple-reference natural excitation technique combined with the eigensystem realization algorithm, (2) the data-driven stochastic subspace identification method, and (3) the enhanced frequency domain decomposition method. Overall, the modal parameters identified using these system identification methods are found to be in very good agreement for each type of tests (ambient and forced vibration tests). For most vibration modes, the natural frequencies and mode shapes identified using the two different types of test data also match very well. However, the modal damping ratios identified from forced vibration test data are in general higher than those estimated from ambient vibration data. The identified natural frequencies and Submitted to Journal of Structural Engineering, ASCE-2-mode shapes are finally compared with their analytical counterparts from a three-dimensional finite element model of the AZMB. The modal properties of the AZMB presented in this paper can be used as baseline in future health monitoring studies of this bridge.

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Section: Submitted To Journal Of Structural Engineering Ascementioning

confidence: 99%

System Identification of Alfred Zampa Memorial Bridge Using Dynamic Field Test Data

Moaveni

Conte

et al. 2009

J. Struct. Eng.

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“…The auto and cross power spectra are estimated using the Correlogram approach [13,14] as it was used in the reference article (i.e., [7]) to estimate the frequency-domain data. The polyreference linear least-squares complex exponential (pLSCE) estimator [15,16] is used to estimate the modes within the selected frequency band, and a stabilization chart is built for each data record to facilitate the selection of the physical vibration modes. The stabilization chart is a very well-known mode selection tool in the modal analysis community.…”

Section: Data Validation: Low Frequency-band Analysismentioning

confidence: 99%

Automatic Tracking of the Modal Parameters of an Offshore Wind Turbine Drivetrain System

et al. 2017

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An offshore wind turbine (OWT) is a complex structure that consists of different parts (e.g., foundation, tower, drivetrain, blades, et al.). The last decade, there has been continuous trend towards larger machines with the goal of cost reduction. Modal behavior is an important design aspect. For tackling noise, vibration, and harshness (NVH) issues and validating complex simulation models, it is of high interest to continuously track the vibration levels and the evolution of the modal parameters (resonance frequencies, damping ratios, mode shapes) of the fundamental modes of the turbine. Wind turbines are multi-physical machines with significant interaction between their subcomponents. This paper will present the possibility of identifying and automatically tracking the structural vibration modes of the drivetrain system of an instrumented OWT by using signals (e.g., acceleration responses) measured on the drivetrain system. The experimental data has been obtained during a measurement campaign on an OWT in the Belgian North Sea where the OWT was in standstill condition. The drivetrain, more specifically the gearbox and generator, is instrumented with a dedicated measurement set-up consisting of 17 sensor channels with the aim to continuously track the vibration modes. The consistency of modal parameter estimates made at consequent 10-min intervals is validated, and the dominant drivetrain modal behavior is identified and automatically tracked.

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“…There are numerous methods which can be applied to discrete FRFs or their associated impulse response functions (Markov parameters) in the time domain [12][13][14][15][16]. These methods have varying performance characteristics depending on the organization and condensation of data, methods of determining model size, ability to use multiple inputs and detect repeated modal frequencies, etc.…”

Section: Identification Of Models From Response Functionsmentioning

confidence: 99%