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Books on the topic 'Modal parameter identification'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Longman, Richard W. Variance and bias confidence criteria for ERA modal parameter identification. American Institute of Aeronautics and Astronautics, 1988.

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2

Dilworth, Brandon J., Timothy Marinone, and Michael Mains, eds. Topics in Modal Analysis & Parameter Identification, Volume 8. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-05445-7.

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3

Dilworth, Brandon J., Timothy Marinone, and Michael Mains, eds. Topics in Modal Analysis & Parameter Identification, Volume 9. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-34942-3.

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4

Dilworth, Brandon J., Timothy Marinone, and Jon Furlich, eds. Topics in Modal Analysis & Parameter Identification, Vol. 9. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68180-6.

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5

Quach, Tai. A study of techniques for rotorcraft model identification. Aerospace Science and Engineering, 1989.

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6

Thomas, Banks H., and Langley Research Center, eds. The identification of a distributed parameter model for a flexible structure. NASA Langley Research Center, 1986.

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7

Thomas, Banks H., and Langley Research Center, eds. The identification of a distributed parameter model for a flexible structure. NASA Langley Research Center, 1986.

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8

Center, Langley Research, ed. Comparing parameter estimation techniques for an electrical power transformer oil temperature prediction model. National Aeronautics and Space Administration, Langley Research Center, 1999.

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9

Huang, Jen-Kuang. Single-mode projection filters for modal parameter identificatio n for flexible structures: Final report for the period ended December 31, 1987. Old Dominion University Research Foundation, 1988.

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10

Chung-Wen, Chen, and United States. National Aeronautics and Space Administration., eds. Single-mode projection filters for modal parameter identificatio n for flexible structures: Final report for the period ended December 31, 1987. Old Dominion University Research Foundation, 1988.

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11

Serikov, Sergey. Impact on impact strength. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2161513.

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The main purpose of the monograph is to identify the main patterns in assessing the operational reliability of metals on the basis of a mathematical model of unsteady deformation of an isotropic viscoplastic medium, with specified boundary, initial conditions and energy criterion of destruction. A physically based computational and experimental method for metal identification is formulated. Practical examples of the efficiency of the method for a wide class of materials are given: structural steels, titanium, aluminum and copper alloys. The assessment of the operational reliability of metals i

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12

A benchmark problem for development of autonomous structural modal identification. National Aeronautics and Space Administration, Langley Research Center, 1996.

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13

National Aeronautics and Space Administration (NASA) Staff. Single-Mode Projection Filters for Modal Parameter Identification for Flexible Structures. Independently Published, 2018.

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14

Topics in Modal Analysis and Parameter Identification, Volume 9: Proceedings of the 41st IMAC, a Conference and Exposition on Structural Dynamics 2023. Springer, 2023.

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15

Marinone, Timothy, Michael Mains, and Brandon J. Dilworth. Topics in Modal Analysis and Parameter Identification, Volume 8: Proceedings of the 40th IMAC, a Conference and Exposition on Structural Dynamics 2022. Springer International Publishing AG, 2022.

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16

Topics in Modal Analysis and Parameter Identification, Volume 8: Proceedings of the 40th IMAC, a Conference and Exposition on Structural Dynamics 2022. Springer International Publishing AG, 2023.

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17

Identification of rotorcraft structural dynamics from flight and wind tunnel data: Final report covering the period February 1991 - August 1992, prepared under NASA-Ames agreement no. NAG 2-694 ... National Aeronautics and Space Administration, 1997.

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18

Evaluation of a nonlinear parameter extraction mathematical model including the term Cm[delta]e₂. National Aeronautics and Space Administration, Langley Research Center, 1986.

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19

McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial a

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