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Book ChapterDOI

Analysis of Stepped Beam Using Reduced Order Models

TL;DR: In this paper, the authors compared three reduced order models (ROMs) with specific reference to a simple problem of damage detection in complicated engineering systems from vibration measurements, including Component Mode Synthesis (CMS), Principal Component Analysis (PCA), and System Equivalent Reduction Expansion Process (SEREP).
Abstract: Damage detection in complicated engineering systems from vibration measurements typically involves the use of algorithms that are built on the principles of bayesian dynamic state estimation. These methods invariably required the solution of the forward problem a fairly large number of times. For complex engineering systems that are numerically modeled using Finite Element (FE), this can be computationally intensive especially when a single FE run for the problem takes a large time. To alleviate this problem, there is a need for the development of Reduced Order Models (ROMs) that significantly reduce the computational cost associated with solving the forward problem, for a given measure without sacrificing the accuracy. The present study discusses three ROM methods with specific reference to a simple problem. These methods include well-known Component Mode Synthesis (CMS) and System Equivalent Reduction Expansion Process (SEREP) which are applicable only for linear systems, as well as Principal Component Analysis (PCA)—the method which is more general and can be used for nonlinear systems as well. A comparison of the performance of all these methods is carried out for a stepped beam. The FE based results obtained from the full model is treated as the benchmark.
References
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Journal ArticleDOI
TL;DR: In this article, a method for treating a complex structure as an assemblage of distinct regions, or substructures, is presented using basic mass and stiffness matrices, together with conditions of geometrical compatibility along substructure boundaries.
Abstract: A method for treating a complex structure as an assemblage of distinct regions, or substructures, is presented. Using basic mass and stiffness matrices for the substructures, together with conditions of geometrical compatibility along substructure boundaries, the method employs two forms of generalized coordinates. Boundary generalized coordinates give displacements and rotations of points along substructure boundaries and are related to the displacement modes of the substructures known as "constraint modes." All constraint modes are generated by matrix operations from substructure input data. Substructure normal-mode generalized coordinates are related to free vibration modes of the substructures relative to completely restrained boundaries. The definition of substructure modes and the requirement of compatibility along substructure boundaries lead to coordinate transformation matrices that are employed in obtaining system mass and stiffness matrices from the mass and stiffness matrices of the substructures. Provision is made, through a RayleighRitz procedure, for reducing the total number of degrees of freedom of a structure while retaining accurate description of its dynamic behavior. Substructure boundaries may have any degree of redundancy. An example is presented giving a free vibration analysis of a structure having a highly indeterminate substructure boundary.

3,035 citations

Book
01 Jan 1996
TL;DR: In this article, the authors present a review of rigor properties of low-dimensional models and their applications in the field of fluid mechanics. But they do not consider the effects of random perturbation on models.
Abstract: Preface Part I. Turbulence: 1. Introduction 2. Coherent structures 3. Proper orthogonal decomposition 4. Galerkin projection Part II. Dynamical Systems: 5. Qualitative theory 6. Symmetry 7. One-dimensional 'turbulence' 8. Randomly perturbed systems Part III. 9. Low-dimensional Models: 10. Behaviour of the models Part IV. Other Applications and Related Work: 11. Some other fluid problems 12. Review: prospects for rigor Bibliography.

2,920 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a method for reducing the size of the stiffness matrix by eliminating coordinates at which no forces are applied, based on the procedure used in Ref. 1 for stiffness matrix reduction.
Abstract: Just as it is often necessary to reduce the size of the stiff­ness matrix in statical structural analysis, the simulta­neous reduction of the nondiagonal mass matrix for natural mode analysis may also be required. The basis for one such reduction technique may follow the procedure used in Ref. 1 for the stiffness matrix, namely, the elimination of coordinates at which no forces are applied.

2,418 citations

Journal ArticleDOI
TL;DR: In this article, a method is developed for analyzing complex structural systems that can be divided into interconnected components, where displacement of the separate components are expressed in generalized coordinates that are defined by displacement modes.
Abstract: A method is developed for analyzing complex structural systems that can be divided into interconnected components. Displacements of the separate components are expressed in generalized coordinates that are defined by displacement modes. These are generated in three categories: rigid-body, "constraint," and "normal" modes. Rigid-body modes are convenient where displacements are denned in inertial space for dynamic analysis. "Constraint" modes are included to treat redundancies in the interconnection system. "Normal" modes define displacements relative to the connections. Generalized mass, stiffness, and damping matrices are determined for each component, as are generalized forces. The requirement of system continuity gives rise to equations of displacement compatibility at the connections. These serve as equations of constraint among the component coordinates and are used to construct a transformation relating component coordinates to system coordinates. This transformation is used to derive system properties and forces from component properties and forces. System equations of motion are formulated and solved to determine system response. Component responses are found using the transformation. Connection forces are computed from the component equations. Each component can then be isolated and treated separately.

1,166 citations

Book
03 Sep 2004
TL;DR: This treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method.
Abstract: Independent component analysis (ICA) is becoming an increasingly important tool for analyzing large data sets. In essence, ICA separates an observed set of signal mixtures into a set of statistically independent component signals, or source signals. In so doing, this powerful method can extract the relatively small amount of useful information typically found in large data sets. The applications for ICA range from speech processing, brain imaging, and electrical brain signals to telecommunications and stock predictions. In Independent Component Analysis, Jim Stone presents the essentials of ICA and related techniques (projection pursuit and complexity pursuit) in a tutorial style, using intuitive examples described in simple geometric terms. The treatment fills the need for a basic primer on ICA that can be used by readers of varying levels of mathematical sophistication, including engineers, cognitive scientists, and neuroscientists who need to know the essentials of this evolving method. An overview establishes the strategy implicit in ICA in terms of its essentially physical underpinnings and describes how ICA is based on the key observations that different physical processes generate outputs that are statistically independent of each other. The book then describes what Stone calls "the mathematical nuts and bolts" of how ICA works. Presenting only essential mathematical proofs, Stone guides the reader through an exploration of the fundamental characteristics of ICA. Topics covered include the geometry of mixing and unmixing; methods for blind source separation; and applications of ICA, including voice mixtures, EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix tutorial, plus basic demonstration computer code that allows the reader to see how each mathematical method described in the text translates into working Matlab computer code.

671 citations