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.

scholarly journals Output-Only Identification of System Parameters from Noisy Measurements by Multiwavelet Denoising

2014 ◽  
Vol 6 ◽  
pp. 218328 ◽  
Author(s):  
O. Al-Gahtani ◽  
M. El-Gebeily ◽  
Y. Khulief
Keyword(s):  
Wavelet Transforms ◽  
Model Identification ◽  
Subspace Identification ◽  
Measuring Devices ◽  
Noisy Output ◽  
Output Only ◽  
Modal Values ◽  
Noisy Measurements ◽  
Stochastic Identification ◽  
Wavelet Methods

In this paper we estimate the parameters of a multidimensional system from a record of noisy output measurements by using a multiwavelet denoising technique. In this output-only identification scheme, we extend wavelet denoising methods to the multiwavelet case. After the noise has been removed from the output records by wavelet methods, either full model identification or deterministic subspace identification can be performed. In the former case, full information on the system such as modal values and shapes becomes available by postprocessing. In the latter case, the observable modal values of the system as well as modal shapes at the sensor locations can be extracted from the identified parameters. Additionally, we discuss the requirements on the measuring devices to be compatible with wavelet transforms of a particular type. The validity and merit of the developed scheme are illustrated by examples of numerically simulated and experimentally measured signals, including comparisons with stochastic identification methods.

The Role of Offshore Monitoring in an Effective Deepwater Riser Integrity Management Program

2007 ◽  
Author(s):  
Brittany Goldsmith ◽  
Elizabeth Foyt ◽  
Madhu Hariharan
Keyword(s):  
Failure Modes ◽  
Human Life ◽  
Management Program ◽  
Measured Data ◽  
Operating Conditions ◽  
Environmental Damage ◽  
Positioning Systems ◽  
Integrity Management ◽  
Deepwater Riser

As offshore field developments move into deeper water, one of the greatest challenges is in designing riser systems capable of overcoming the added risks of more severe environments, complicated well requirements and uncertainty of operating conditions. The failure of a primary riser component could lead to unacceptable consequences, including environmental damage, lost production and possible injury or loss of human life. Identification of the risks facing riser systems and management of these risks are essential to ensure that riser systems operate without failure. Operators have recognized the importance of installing instrumentation such as global positioning systems (GPS), vessel motion measurement packages, wind and wave sensors and Acoustic Doppler Current Profiler (ADCP) units to monitor vessel motions and environmental conditions. Additionally, high precision monitoring equipment has been developed for capturing riser response. Measured data from these instruments allow an operator to determine when the limits of acceptable response, predicted by analysis or determined by physical limitations of the riser components, have been exceeded. Regular processing of measured data through automated routines ensures that integrity can be quickly assessed. This is particularly important following extreme events, such as a hurricane or loop current. High and medium alert levels are set for each parameter, based on design analysis and operating data. Measured data is compared with these alert levels, and when an alert level is reached, further response evaluation or inspection of the components in question is recommended. This paper will describe the role of offshore monitoring in an integrity management program and discuss the development of alert levels based on potential failure modes of the riser systems. The paper will further demonstrate how this process is key for an effective integrity management program for deepwater riser systems.

Managing Flow Induced Vibration in Subsea Jumpers

2021 ◽  
Author(s):  
Rakshith Naik ◽  
Yetzirah Urthaler ◽  
Scot McNeill ◽  
Rafik Boubenider
Keyword(s):  
Dynamic Properties ◽  
Fatigue Analysis ◽  
Management Program ◽  
Measured Data ◽  
Operating Conditions ◽  
Valuable Insight ◽  
Fe Model ◽  
Flow Induced Vibration ◽  
Vibration Measurements

Abstract Certain subsea jumper design features coupled with operating conditions can lead to Flow Induced Vibration (FIV) of subsea jumpers. Excessive FIV can result in accumulation of allowable fatigue damage prior to the end of jumper service life. For this reason, an extensive FIV management program was instated for a large development in the Gulf of Mexico (GOM) where FIV had been observed. The program consisted of in-situ measurement, modeling and analysis. Selected well and flowline jumpers were outfitted with subsea instrumentation for dedicated vibration testing. Finite Element (FE) models were developed for each jumper and refined to match the dynamic properties extracted from the measured data. Fatigue analysis was then carried out using the refined FE model and measured response data. If warranted by the analysis results, action was taken to mitigate the deleterious effects of FIV. Details on modeling and data analysis were published in [5]. Herein, we focus on the overall findings and lessons learned over the duration of the program. The following topics from the program are discussed in detail: 1. In-situ vibration measurement 2. Overall vibration trends with flow rate and lack of correlation of FIV to flow intensity (rho-v-squared); 3. Vibration and fatigue performance of flowline jumpers vs. well jumpers 4. Fatigue analysis conservatism Reliance on screening calculations or predictive FE analysis could lead to overly conservative operational limits or a high degree of fatigue life uncertainty in conditions vulnerable to FIV. It is proposed that in-situ vibration measurements followed by analysis of the measured data in alignment with operating conditions is the best practice to obtain a realistic understanding of subsea jumper integrity to ensure safe and reliable operation of the subsea system. The findings from the FIV management program provide valuable insight for the subsea industry, particularly in the areas of integrity management of in-service subsea jumpers; in-situ instrumentation and vibration measurements and limitations associated with predictive analysis of jumper FIV. If learnings, such as those discussed here, are fed back into design, analysis and monitoring guidelines for subsea equipment, the understanding and management of FIV could be dramatically enhanced compared to the current industry practice.

STRUCTURAL IDENTIFICATION OF DYNAMIC SYSTEMS USING PARAMETER UPDATING REANALYSIS METHOD: EXPERIMENTAL INVESTIGATION USING THE MEASURED DATA

2016 ◽  
Vol 40 (5) ◽  
pp. 847-857
Author(s):  
Kyoungbong Han ◽  
Dooyong Cho ◽  
Sun Kyu Park
Keyword(s):  
Analytical Model ◽  
Dynamic Systems ◽  
Frequency Response Function ◽  
Dynamic Test ◽  
Structural Identification ◽  
Measured Data ◽  
Actual Structure ◽  
Response Data ◽  
Numerical Tests ◽  
Structural Reanalysis

In this study, the proposed parameter updating reanalysis method was directly used on the measured data of the structures. Structural reanalysis generates a correlated analytical model that defines the structure on applying the initially assumed baseline analytical model and is presented through the structure’s FRF (frequency response function). Two numerical tests were previously conducted in order to demonstrate the effectiveness of the suggested reanalysis method. The suggested method generates the correlated analytical model with higher precision, as compared to the existing method, despite the application of the noise factor to the observed data. The method initially proven by the numerical experiment of an actual structure was applied to a pseudo-dynamic test on the full-scale concrete pier. The results indicate that the proposed reanalysis is useful even for application to response data of the actual structure.

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