Probabilistic Assessment of Structural Condition Incorporating Uncertainty of Measured Data through Multi-model Identification

2015 ◽  
Vol 105 (19) ◽  
pp. 1-8
Author(s):  
Hyun-Joong Kim ◽  
Hyun-Moo Koh ◽  
Junho Song ◽  
Wonsuk Park
Keyword(s):  
Model Identification ◽  
Structural Condition ◽  
Measured Data ◽  
Probabilistic Assessment

Output-Only Subspace-Based Structural Identification: From Theory to Industrial Testing Practice1

2001 ◽  
Vol 123 (4) ◽  
pp. 668-676 ◽  
Author(s):  
Miche`le Basseville ◽  
Albert Benveniste ◽  
Maurice Goursat ◽  
Luc Hermans ◽  
Laurent Mevel ◽  
...  
Keyword(s):  
Structural Model ◽  
Model Identification ◽  
Structural Identification ◽  
Measured Data ◽  
Operating Conditions ◽  
Industrial Testing ◽  
Identification Methods ◽  
Multiple Sensor ◽  
Output Only ◽  
Stochastic Identification

We address the problem of structural model identification during normal operating conditions and thus with uncontrolled, unmeasured, and nonstationary excitation. We advocate the use of output-only and covariance-driven subspace-based stochastic identification methods. We explain how to handle nonsimultaneously measured data from multiple sensor setups, and how robustness with respect to nonstationary excitation can be achieved. Experimental results obtained for three real application examples are shown.

Model Identification of a Rotor With Magnetic Bearings

2001 ◽  
Author(s):  
José A. Vázquez ◽  
Eric H. Maslen ◽  
Hyeong-Joon Ahn ◽  
Dong-Chul Han
Keyword(s):  
Dynamic Stiffness ◽  
Model Identification ◽  
Magnetic Bearing ◽  
Measured Data ◽  
Response Functions ◽  
Flexible Rotor ◽  
Experimental Identification ◽  
Rotor Model ◽  
Reconciliation Process ◽  
Piano Wire

The experimental identification of a long flexible rotor with three magnetic bearing journals is presented. Frequency response functions (FRFs) are measured between the magnetic bearing journals and the sensor locations while the rotor is suspended horizontally with piano wire. These FRFs are compared with the responses of a rotor model and a reconciliation process is used to reduce the discrepancies between the model and the measured data. In this identification, the wire and the fit of the magnetic bearing journals are identified as the sources of model error. As a result of the reconciliation process, equivalent dynamic stiffness are calculated for the piano wire and the fit of the magnetic bearing journals. Several significant numeral issues that were encountered during the process are discussed and solutions to some of these problems are presented.

Rapid Probabilistic Assessment of Wave and Surge Hurricane Risk

2012 ◽  
Author(s):  
Alexandros A. Taflanidis ◽  
Andrew B. Kennedy ◽  
Joannes J. Westerink ◽  
Jane Smith
Keyword(s):  
Probabilistic Assessment ◽  
Hurricane Risk

Analysis of Measured Data in Unbalanced Distribution System and Method of making of Distribution System Model for Analyzing

2016 ◽  
Vol 136 (6) ◽  
pp. 759-766 ◽  
Author(s):  
Yu Fujita ◽  
Hiroshi Kobayashi ◽  
Takanori Kodera ◽  
Mutsumi Aoki ◽  
Hiroto Suzuki ◽  
...  
Keyword(s):  
Distribution System ◽  
Measured Data ◽  
System Model

PI(D) tuning with contextual model identification

2009 ◽  
Author(s):  
Alberto Leva ◽  
Sara Negro ◽  
Alessandro Vittorio Papadopoulos
Keyword(s):  
Model Identification ◽  
Contextual Model
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