• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.4
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• pp.367-375
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• 2013

The mechanical vibration of a PM synchronous motor at low speeds due to the back emf harmonics may be serious problems in some application such as MDPS(Motor driven power steering), electric vehicles. In this paper, torque ripple reduction for an interior PM synchronous motor including back emf harmonics is proposed. The dq flux linkage harmonics of the permanent magnet are estimated on real time by using the dq currents of the real system and the model of the MRAS observer. Based on the estimated flux linkage harmonics, the dq harmonic currents for reducing the torque ripples are compensated on the dq reference currents. The estimation of the flux linkage harmonics by the MRAS observer and the torque ripple reduction of the proposed algorithm was verified by the simulation and experiment.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.1
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• pp.24-30
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• 2000

The aim of this paper is to describe an advanced method of the fault diagnosis using Control Theory with reference to a crack detection, a new way to localize the crack position under influence of the plant disturbance and white measurement noise on a rotating shaft. As the first step, the shaft is physically modelled with a finite element method as usual and the dynamic mathematical model is derived from it using the Hamilton-principle and in this way the system is modelled by various subsystems. The equations of motions with a crack are established by the adaption of the local stiffness change through breathing and gaping[1] from the crack to the equation of motion with an undamaged shaft. This is supposed to be regarded as a reference system for the given system. Based on the fictitious model of the time behaviour induced from vibration phenomena measured at the bearings, a nonlinear state observer is designed in order to detect the crack on the shaft. This is the elementary NL-observer(EOB). Using the elementary observer, an Estimator(Observer Bank) is established and arranged at the certain position on the shaft. In case, a crack is found and its position is known, the procedure, fro the estimation of the depth is going to begin.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.143-146
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• 1999

특정 플랜트내에 원하지 않는 음이나 진동신호를 능동적으로 제어하기 위해 LMS의 Filtered reference와 Filtered error 방법으로 피드포워드 적응제어 시스템을 사용할 때 오차신호의 수렴특성을 플랜트의 추정 임펄스응답의 정확도로써 나타낼 수 있는 식을 제안하고 수치 시뮬레이션과 실험으로 그 유효성에 대해 기술한다. 플랜트내 음향전달계의 추정 임펄스응답의 안정성 평가식은 이미 W. Ren과 P. R. Kumar에 의해 발표되었으나[1], 그 평가방법은 플랜트내의 실제(True) 임펄스응답을 필요로하고있어 수치 시뮬레이션에서는 가능한 방법이지만 임펄스응답의 측정이 불가능한 실제의 적응제어 시스템에 적용하는 것은 곤란하다. 따라서, 플랜트의 추정 임펄스응답을 크로스스펙트럼법(Cross-Spectrum)으로 추정하여 그 임펄스응답을 피드포워드 적응제어 시스템에 적용했을 때 안정성 여부를 평가하고, 또한 오차신호의 수렴특성을 Filtered reference와 Filtered error 방법에서 각각 확인하였다. 수치 시뮬레이션 결과와 실험 결과가 일치함을 확인하였다.

• Wind and Structures
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• v.10 no.4
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• pp.347-366
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• 2007

Guyed masts subjected to turbulent winds exhibit complex vibrations featuring many vibration modes, each of which contributes to various structural responses in differing degrees. This dynamic behaviour is further complicated by nonlinear guy cable properties. While previous studies have indicated that conventional frequency domain methods can reliably reproduce load effects within the mast, the system linearization required to perform such an analysis makes it difficult to relate these results directly to corresponding guy forces. As a result, the estimation of peak load effects arising jointly from the structural action of the mast and guys, such as leg loads produced as a result of guy reactions and mast bending moments, is uncertain. A numerical study was therefore undertaken to study peak load effects in a 295 m tall guyed mast acted on by simulated turbulent wind. Responses calculated explicitly from nonlinear time domain finite element analyses were compared with approximate methods in the frequency domain for estimating peak values of selected responses, including guy tension, mast axial loads and mast leg loads. It was found that these peak dynamic load effects could be accurately estimated from frequency domain analysis results by employing simple, slightly conservative assumptions regarding the correlation of related effects.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.779-788
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• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• Smart Structures and Systems
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• v.21 no.6
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• pp.741-749
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• 2018

Structural health monitoring (SHM) systems are necessary to achieve smart predictive maintenance and repair planning as well as they lead to a safe operation of mechanical structures. In the context of vibration-based SHM the measured structural responses are employed to draw conclusions about the structural integrity. This usually leads to a mathematically illposed inverse problem which needs regularization. The restriction of the solution set of this inverse problem by using prior information about the damage properties is advisable to obtain meaningful solutions. Compared to the undamaged state typically only a few local stiffness changes occur while the other areas remain unchanged. This change can be described by a sparse damage parameter vector. Such a sparse vector can be identified by employing $L_1$-regularization techniques. This paper presents a novel framework for damage parameter identification by combining sparse solution techniques with an Extended Kalman Filter. In order to ensure sparsity of the damage parameter vector the measurement equation is expanded by an additional nonlinear $L_1$-minimizing observation. This fictive measurement equation accomplishes stability of the Extended Kalman Filter and leads to a sparse estimation. For verification, a proof-of-concept example on a quadratic aluminum plate is presented.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.979-987
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• 1999

In a high precision vertical machining center, the estimation of cutting forces is important for many reasons such as prediction of chatter vibration, surface roughness and so on, and cutting forces are difficult to predict because they are very complex and time variant. In order to predict the cutting forces of end-milling process for various cutting conditions, a mathematical model is important and this model is based on chip load, cutting geometry, and the relationship between cutting forces and chip loads. Specific cutting force coefficients of the model have been obtained as interpolation function types by averaging farces of cutting tests. In this paper, the coefficients are obtained by neural network and the results of the conventional method and those of the proposed method are compared. The results show that the neural network method gives more correct values than the function type and that in teaming stage as the omitted numbers of experimental data increases the average errors increase.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.6 s.183
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• pp.119-127
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• 2006

A non-destructive time domain approach to examine structural damage using parameterized partial differential equations and Galerkin approximation techniques is presented. The time domain analysis for damage detection is independent of modal parameters and analytical models unlike frequency domain methods which generally rely on analytical models. The time history of the vibration response of the structure was used to identify the presence of damage. Damage in a structure causes changes in the physical coefficients of mass density, elastic modulus and damping coefficients. This is a part of our ongoing effort on the general problem of modeling and parameter estimation for internal damping mechanisms in a composite beam. Namely, in detecting damage through time-domain or frequency-domain data from smart sensors, the common damages are changed in modal properties such as natural frequencies, mode shapes, and mode shape curvature. This paper examines the use of beam-like structures with piezoceramic sensors and actuators to perform identification of those physical parameters, and detect the damage. Experimental results are presented from tests on cantilevered composite beams damaged at different locations and different dimensions. It is demonstrated that the method can sense the presence of damage and obtain the position of a damage.

• Journal of Ocean Engineering and Technology
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• v.8 no.1
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• pp.144-153
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• 1994

An algorithm to locate and estimate severity of damage in jacket-type offshore structures for which modal responses are availabit' for very few vibrational modes is presented. First, a theory of damage locaization and severity estimation(which yields information on the location and severity of damage directly from changes in mode shapes) is formulated. Next, the feasibility the damage detection algorithm is demonstrated by using a numerical example of an offshore jacket platform for which only three vibration modes are measured. Form the material presented here, two major results are observed. First, all damage locations in the offshore jacket platform are correctly predicted. Next, predicted damage is relatively correctly estimated.

• Smart Structures and Systems
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• v.11 no.1
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• pp.19-34
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• 2013

The objective of this paper is to develop on-line system parameter estimation and damage detection technique from the response measurements through using the Recursive Covariance-Driven Stochastic Subspace identification (RSSI-COV) approach. To reduce the effect of noise on the results of identification, discussion on the pre-processing of data using recursive singular spectrum analysis (rSSA) is presented to remove the noise contaminant measurements so as to enhance the stability of data analysis. Through the application of rSSA-SSI-COV to the vibration measurement of bridge during scouring experiment, the ability of the proposed algorithm was proved to be robust to the noise perturbations and offers a very good online tracking capability. The accuracy and robustness offered by rSSA-SSI-COV provides a key to obtain the evidence of imminent bridge settlement and a very stable modal frequency tracking which makes it possible for early warning. The peak values of the identified $1^{st}$ mode shape slope ratio has shown to be a good indicator for damage location, meanwhile, the drastic movements of the peak of $2^{nd}$ mode slope ratio could be used as another feature to indicate imminent pier settlement.