• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.837-840
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• 1997

This paper provides an experimental verification of an FRF-based structural damage identification method (SDIM) developed by the authors for beam structures. The FRF-based SDIM requires the following data : (1) natural frequencies and mode shapes measured at the intact state and (2) the FRF-data measured at the damaged state. Experiments are conducted for the cantilevered beam with one slot and three slots. It is shown that the FRF-based SDIM developed by the authors provide very successful damage identification results which agree well with true damage state.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.2
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• pp.147-154
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• 1999

A new approach to conducting the vibration parameters identification algorithm is proposed. The approach employs the concept of modal amplitude ratio implemented in a mode shape estimation. The accuracy of the improved Ibrahim Time Domain identification algorithm in extracting structural modal parameters from free response functions has been studied using computer simulated data for 9 stations on the two-span continuous beam. Simulated responses from the lumped and distributed parameter system demonstrate that this algorithm produces excellent results, even in the 300% noise response.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1754-1763
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• 2017

This paper analyses the characteristics of incomplete-journey double-circuit transmission lines on the same tower formed by single-circuit lines and double-circuit lines, and then presents a fault location algorithm based on identification of fault branch. With the relationship between the three-phase system and the double-circuit line system, a phase-mode transformation matrix for double-circuit lines can be derived. Based on the derived matrix, the double-circuit lines with faults can be decoupled, and then the fault location for an incomplete-journey double-circuit line is achieved by using modal components in the mode domain. The algorithm is divided into two steps. Firstly, the fault branch is identified by comparing the relationships of voltage amplitudes at the bonding point. Then the fault location, on the basis of the identification result, is calculated by using a two-terminal method, and only the fault distance of the actual fault branch can be obtained. There is no limit on synchronization of each terminal sampling data. The results of ATP-EMTP simulation show that the proposed algorithm can be applied within the entire line and can accurately locate faults in different fault types, fault resistances, and fault distances.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.2
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• pp.301-314
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• 2015

Aiming at accurately distinguishing modeless component and natural vibration mode terms from data series of nonlinear and non-stationary processes, such as Vortex-Induced Vibration (VIV), a new empirical mode decomposition method has been developed in this paper. The key innovation related to this technique concerns the method to decompose modeless component from non-stationary process, characterized by a predetermined 'maximum intrinsic time window' and cubic spline. The introduction of conceptual modeless component eliminates the requirement of using spurious harmonics to represent nonlinear and non-stationary signals and then makes subsequent modal identification more accurate and meaningful. It neither slacks the vibration power of natural modes nor aggrandizes spurious energy of modeless component. The scale of the maximum intrinsic time window has been well designed, avoiding energy aliasing in data processing. Finally, it has been applied to analyze data series of vortex-induced vibration processes. Taking advantage of this newly introduced empirical decomposition method and mode identification technique, the vibration analysis about vortex-induced vibration becomes more meaningful.

• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.125-147
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• 2020

Damage detection of structures is one of the most important topics in structural health monitoring. In practice, the response is not available at all structural degrees of freedom, and due to the installation of sensors at some degrees of freedom, responses exist only in limited number of degrees of freedom. This paper is investigated the damage detection of structures by applying two approaches, AllDOF and Dynamic Condensation Method (DCM), based on the Modified Modal Strain Energy Method (MMSEBI). In the AllDOF method, mode shapes in all degrees of freedom is available, but in the DCM the mode shapes only in some degrees of freedom are available. Therefore by methods like the DCM, mode shapes are obtained in slave degrees of freedom. So, in the first step, the responses at slave degrees of freedom extracted using the responses at master degrees of freedom. Then, using the reconstructed mode shape and obtaining the modified modal strain energy, the damages are detected. Two standard examples are used in different damage cases to evaluate the accuracy of the mentioned method. The results showed the capability of the DCM is acceptable for low mode shapes to detect the damage in structures. By increasing the number of modes, the AllDOF method identifies the locations of the damage more accurately.

• Structural Engineering and Mechanics
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• v.41 no.5
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• pp.663-673
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• 2012

A number of mode shape-based structure damage identification methods have been verified by numerical simulations or experiments for on-line structure health monitoring (SHM). However, many of them need a baseline mode shape generated by the healthy structure serving as a reference to identify damages. Otherwise these methods can hardly perform well when multiple cracks conditions occur. So it is important to solve the problems above. By aid of the fractal dimension method (FD), Qiao and Wang proposed a generalized fractal dimension (GFD) to detect the delamination damage. As a modification of GFD, Qiao and Cao proposed the approximate waveform capacity dimension (AWCD) technique to simplify the calculation of fractal and overcome the false peak appearing in the high mode shapes. Based on their valued work, this paper combined and applied the AWCD method and curvature mode shape data to detect multiple damages in beam. In the end, the identification properties of the AWCD for multiple damages have been verified by groups of Monte Carlo simulations and experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.449-458
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• 2000

In this paper, we propose a feed identification method using neural network to predict feed in crude distillation unit. The proposed FINN(feed identifier by neural network) is functionally composed of two modes-training mode and prediction mode. Also, we implement a neural network-based soft sensor system using Borland C++(3.0) Builder. The effectiveness of the proposed neural network-based feed identification method is shown by simulation results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.397-401
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• 1994

This paper proposes an identification method for a thin plate where multiple actuators and sensors are bonded. Since a thin plate has small damping ratios of all modes, each mode can be identified seperately with a bandpass filter for each modal signal. With the bandpass filter and the characteristics of the plate, the Multi-Input Multi-Output (MIMO) model of the plate can be converted to several Multi-Input Single-Output(MISO) models of second order linear difference equations of the modes. Parameters for each mode are obtained by using the Least Square method. Form there MISO models, the MIMO model is obtained in the form of the state space. Experiments were performed for an all-clamped plate with two pairs of piezoelectric actuators and sensors. The outputs of the identified model and the experimental data match well.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.447-452
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• 1998

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model non linear systems. In this paper, we proposes an FDI system for non linear systems using ANFIS. The proposed diagnositc system consists of two ANFISs which operate in two different modes (parallel-and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis function) network to identify the faults. The proposed FDI scheme has been tested by simultation on a two-tank system