DYNAMIC ASSESSMENT OF UNDERWATER PIPELINE SYSTEMS USING STATISTICAL MODEL UPDATING

2008 ◽  
Vol 08 (02) ◽  
pp. 271-297 ◽  
Author(s):  
X. Q. ZHU ◽  
H. HAO ◽  
X. L. PENG
Keyword(s):  
Statistical Model ◽  
Model Updating ◽  
Dynamic Assessment ◽  
Mode Shapes ◽  
Pipeline System ◽  
Damage Scenarios ◽  
Stochastic Subspace Identification ◽  
Underwater Pipeline ◽  
Pipeline Systems ◽  
Identification Technique

This paper presents a statistical model updating technique for damage detection of underwater pipeline systems via vibration measurements. To verify the reliability of the method, laboratory tests of a scaled pipeline model were carried out in a towing tank. The model includes a plastic pipe and some removable springs which are designed and fabricated to link the pipe and the steel base to simulate the bedding conditions. Different damage scenarios, in terms of location and severity of scouring under the pipe, were simulated by removing one or several springs. The natural frequencies, damping ratios and mode shapes of the pipeline system were extracted from the measured vibrations using a stochastic subspace identification technique. Both the numerical and the experimental results show that the method is effective and reliable in identifying the underwater pipeline bedding conditions and the damage in the pipe structure.

Identification of Structural Property Degradations by Computational Model Updating

2007 ◽  
Vol 347 ◽  
pp. 19-34 ◽  
Author(s):  
Michael Link ◽  
Stefan Stöhr ◽  
Matthias Weiland
Keyword(s):  
Experimental Data ◽  
Computational Model ◽  
Structural Property ◽  
Test Data ◽  
Measurement Errors ◽  
Structural Damage ◽  
Model Updating ◽  
Moving Load ◽  
Mode Shapes ◽  
Damage Scenarios

Computational model updating techniques are used to adjust selected parameters of finite element models in order to make the models compatible with experimental data. This is done by minimizing the differences of analytical and experimental data, for example, natural frequencies and mode shapes by numerical optimization procedures. For a long time updating techniques have also been investigated with regard to their ability to localize and quantify structural damage. The success of such an approach is mainly governed by the quality of the damage model and its ability to describe the structural property changes due to damage in a physical meaningful way. Our experience has shown that due to unavoidable modelling simplifications and measurement errors the changes of the corresponding damage parameters do not always indicate structural modifications introduced by damage alone but indicate also the existence of other modelling uncertainties which may be distributed all over the structure. This means that there are two types of parameters which have to be distinguished: the damage parameters and the other parameters accounting for general modelling and test data uncertainties. Although these general parameters may be physically meaningless they are necessary to achieve a good fit of the test data and it might happen that they cannot be distinguished from the damage parameters. For complex industrial structures it is seldom possible to generate unique structural models covering all possible damage scenarios so that one has to expect, that the parameters introduced for describing the damage will not be fully consistent with the physical reality. This is the reason why in the scientific community there is still some doubt if model based techniques can be used at all for practical purposes of damage detection and quantification under in-situ environment conditions. In the present paper we summarize the methodology of computational model updating and report about our experience with damage identification exemplified by practical examples. A new technique and an application of localising and quantifying the damage from updating the parameters of the damaged and the undamaged models simultaneously using the differences of the test data from the damaged and the undamaged structure is also presented. In this application we used the deflections (influence lines) of a beam structure measured under a slowly moving load.

Wavelet-Based Modal Parameter Identification through Measurements for Damage Detection

2007 ◽  
Vol 353-358 ◽  
pp. 1195-1198 ◽  
Author(s):  
Y.B. Chen ◽  
J.G. Han ◽  
D.Q. Yang
Keyword(s):  
Modal Analysis ◽  
Frequency Domain ◽  
Operating Conditions ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Time Frequency ◽  
Stochastic Subspace Identification ◽  
Damping Coefficients ◽  
Identification Technique

Structural operating conditions may significantly differ from those applied during laboratory tests where the structure is well known, well installed and properly excited. For structures under their natural loading conditions, or excited by random forces, excitations cannot be measured and are usually non stationary. Hence, an improvement operational modal analysis is a useful complement to the traditional modal analysis approach. The aim of this paper is to present the application of a new identification procedure, named wavelet-based identification technique of structural modal parameters. Wavelet-based identification that works in time-frequency domain is used to identify the dynamic characteristics of the structural system in terms of natural frequencies, damping coefficients and mode shapes. The paper has shown how the amplitude and the phase of the wavelet transform of operational vibration measurements are related to eigenfrequencies and damping coefficients, and the wavelet-based spectrum analysis is used to identify the mode shapes of the structure. Those modal parameters can be used to detect damage of structures. A simulation example has demonstrated that current identified results are comparable with those previously obtained from the peak pick method in frequency domain and stochastic subspace identification in time domain.

scholarly journals Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Nizar Faisal Alkayem ◽  
Maosen Cao ◽  
Minvydas Ragulskis
Keyword(s):  
Structural Damage ◽  
Model Updating ◽  
Complex Problem ◽  
Dynamic Responses ◽  
Mode Shapes ◽  
Fe Model ◽  
Modal Strain Energy ◽  
3D Structures ◽  
Multiobjective Particle Swarm Optimization ◽  
Damage Tracking

Structural damage detection is a well-known engineering inverse problem in which the extracting of damage information from the dynamic responses of the structure is considered a complex problem. Within that area, the damage tracking in 3D structures is evaluated as a more complex and difficult task. Swarm intelligence and evolutionary algorithms (EAs) can be well adapted for solving the problem. For this purpose, a hybrid elitist-guided search combining a multiobjective particle swarm optimization (MOPSO), Lévy flights (LFs), and the technique for the order of preference by similarity to ideal solution (TOPSIS) is evolved in this work. Modal characteristics are employed to develop the objective function by considering two subobjectives, namely, modal strain energy (MSTE) and mode shape (MS) subobjectives. The proposed framework is tested using a well-known benchmark model. The overall strong performance of the suggested method is maintained even under noisy conditions and in the case of incomplete mode shapes.

Case Studies Highlighting Rapid Repair Methods of Pressurised Pipelines Damaged by Anchors

2018 ◽  
Author(s):  
Dale Millward
Keyword(s):  
Case Studies ◽  
Health And Safety ◽  
Cost Effective ◽  
Pipeline System ◽  
Subsea Pipeline ◽  
Damage Scenarios ◽  
Fail Safe ◽  
Subsea Pipelines ◽  
Marine Vessels ◽  
Pipeline Design

Effective pipeline design and regular maintenance can assist in prolonging the lifespan of subsea pipelines, however the presence of marine vessels can significantly increase the risk of pipeline damage from anchor hazards. As noted in the Health and Safety Executive – Guideline for Pipeline Operators on Pipeline Anchor Hazards 2009. “Anchor hazards can pose a significant threat to pipeline integrity. The consequences of damage to a pipeline could include loss of life, injury, fire, explosion, loss of buoyancy around a vessel and major pollution”. This paper will describe state of the art pipeline isolation tooling that enables safe modification of pressurised subsea pipelines. Double Block and Bleed (DBB) isolation tools have been utilised to greatly reduce downtime, increase safety and maximise unplanned maintenance, providing cost-effective solutions to the end user. High integrity isolation methods, in compliance with international subsea system intervention and isolation guidelines (IMCA D 044 / IMCA D 006), that enable piggable and unpiggable pipeline systems to be isolated before any breaking of containment, will also be explained. This paper will discuss subsea pipeline damage scenarios and repair options available to ensure a safe isolation of the pipeline and contents in the event of an incident DNV GL type approved isolation technology enables the installation of a fail-safe, DBB isolation in the event of a midline defect. The paper will conclude with case studies highlighting challenging subsea pipeline repair scenarios successfully executed, without depressurising the entire pipeline system, and in some cases without shutting down or interrupting production.

Dynamical Model Updating Based on Gradient Regularization Method

2013 ◽  
Vol 351-352 ◽  
pp. 118-121
Author(s):  
He Long Xu ◽  
Jun Xiao ◽  
Yu Xin Zhang
Keyword(s):  
Regularization Method ◽  
Solution Method ◽  
Model Updating ◽  
Input Parameter ◽  
Mode Shapes ◽  
Problem Solution ◽  
Finite Element Program ◽  
Element Program ◽  
Important Input ◽  
The Mathematical Model

Modulus of elasticity is an important input parameter in all kinds of structural analyses. The mathematical model used to identify the structural elastic modulus with measured Frequencies and mode shapes at several points is thusly built up in this paper, and then Gradient-Regularization method, an inverse problem solution method, is employed to solve the problem. General finite element program is compiled, and numerical examples have proved that the method of this thesis is efficient. The issues such as the choice of model error and the choice of measuring points are discussed as well.

L1 Regularized Model Updating for Structural Damage Detection

2018 ◽  
Vol 18 (12) ◽  
pp. 1850157 ◽  
Author(s):  
Yu-Han Wu ◽  
Xiao-Qing Zhou
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Regularization Parameter ◽  
Structural Vibration ◽  
Mode Shapes ◽  
Modal Data ◽  
Vibration Data ◽  
Stiffness Reduction ◽  
Regularized Model

Model updating methods based on structural vibration data have been developed and applied to detecting structural damages in civil engineering. Compared with the large number of elements in the entire structure of interest, the number of damaged elements which are represented by the stiffness reduction is usually small. However, the widely used [Formula: see text] regularized model updating is unable to detect the sparse feature of the damage in a structure. In this paper, the [Formula: see text] regularized model updating based on the sparse recovery theory is developed to detect structural damage. Two different criteria are considered, namely, the frequencies and the combination of frequencies and mode shapes. In addition, a one-step model updating approach is used in which the measured modal data before and after the occurrence of damage will be compared directly and an accurate analytical model is not needed. A selection method for the [Formula: see text] regularization parameter is also developed. An experimental cantilever beam is used to demonstrate the effectiveness of the proposed method. The results show that the [Formula: see text] regularization approach can be successfully used to detect the sparse damaged elements using the first six modal data, whereas the [Formula: see text] counterpart cannot. The influence of the measurement quantity on the damage detection results is also studied.

Reliable Mode Shape Expansion Method for Error Localisation and Model Updating

1997 ◽  
Author(s):  
Philippe Collignon ◽  
Jean-Claude Golinval
Keyword(s):  
Cantilever Beam ◽  
Mode Shape ◽  
Constitutive Equations ◽  
Structural Model ◽  
Model Updating ◽  
Failure Detection ◽  
Expansion Method ◽  
Relative Performance ◽  
Mode Shapes ◽  
Error Localization

Abstract Failure detection and model updating using structural model are based on the comparison of an appropriate indicator of the discrepancy between experimental and analytical results. The reliability of the expansion of measured mode shapes is very important for the process of error localization and model updating. Two mode shape expansion techniques are examined in this paper : the well known dynamic expansion (DE) method and a method based on the minimisation of errors on constitutive equations (MECE). A new expansion method based on some improvements of the previous techniques is proposed to obtain results that are more reliable for error localisation and for model updating. The relative performance of the different expansion methods is demonstrated on the example of a cantilever beam.

Updating of Non-Conservative Systems Using Inverse Methods

1997 ◽  
Author(s):  
Ladislav Starek ◽  
Milos Musil ◽  
Daniel J. Inman
Keyword(s):  
Analytical Model ◽  
Degrees Of Freedom ◽  
Ad Hoc ◽  
Natural Frequencies ◽  
Eigenvalue Problems ◽  
Model Updating ◽  
Analytical Models ◽  
Mode Shapes ◽  
Element Analysis ◽  
Modal Data

Abstract Several incompatibilities exist between analytical models and experimentally obtained data for many systems. In particular finite element analysis (FEA) modeling often produces analytical modal data that does not agree with measured modal data from experimental modal analysis (EMA). These two methods account for the majority of activity in vibration modeling used in industry. The existence of these discrepancies has spanned the discipline of model updating as summarized in the review articles by Inman (1990), Imregun (1991), and Friswell (1995). In this situation the analytical model is characterized by a large number of degrees of freedom (and hence modes), ad hoc damping mechanisms and real eigenvectors (mode shapes). The FEM model produces a mass, damping and stiffness matrix which is numerically solved for modal data consisting of natural frequencies, mode shapes and damping ratios. Common practice is to compare this analytically generated modal data with natural frequencies, mode shapes and damping ratios obtained from EMA. The EMA data is characterized by a small number of modes, incomplete and complex mode shapes and non proportional damping. It is very common in practice for this experimentally obtained modal data to be in minor disagreement with the analytically derived modal data. The point of view taken is that the analytical model is in error and must be refined or corrected based on experimented data. The approach proposed here is to use the results of inverse eigenvalue problems to develop methods for model updating for damped systems. The inverse problem has been addressed by Lancaster and Maroulas (1987), Starek and Inman (1992,1993,1994,1997) and is summarized for undamped systems in the text by Gladwell (1986). There are many sophisticated model updating methods available. The purpose of this paper is to introduce using inverse eigenvalues calculated as a possible approach to solving the model updating problem. The approach is new and as such many of the practical and important issues of noise, incomplete data, etc. are not yet resolved. Hence, the method introduced here is only useful for low order lumped parameter models of the type used for machines rather than structures. In particular, it will be assumed that the entries and geometry of the lumped components is also known.

Sign in / Sign up

Export Citation Format

Share Document