A Kalman filter based strategy for linear structural system identification based on multiple static and dynamic test data

Tipireddy, Ramakrishna; Nasrellah, H.A.; Manohar, C.S.

doi:10.1016/j.probengmech.2008.01.001

Cited by 29 publications

(22 citation statements)

References 23 publications

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“…The traditional Kalman filter (KF) is an efficient sequential data assimilation method for linear dynamics and measurement processes with Gaussian error statistics (Kalman 1960;Drecourt 2003;Drecourt et al 2006;Tipireddy et al 2008;Yangxiao et al 1991;Zhang et al 2007). To assimilate data for nonlinear dynamics and measurement processes, the extended Kalman filter (EKF) was developed (Jazwinski 1970).…”

Section: Introductionmentioning

confidence: 99%

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Tong

Yang

2010

Stoch Environ Res Risk Assess

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A data assimilation method is developed to calibrate a heterogeneous hydraulic conductivity field conditioning on transient pumping test data. The ensemble Kalman filter (EnKF) approach is used to update model parameters such as hydraulic conductivity and model variables such as hydraulic head using available data. A synthetical two-dimensional flow case is used to assess the capability of the EnKF method to calibrate a heterogeneous conductivity field by assimilating transient flow data from observation wells under different hydraulic boundary conditions. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating continuous hydraulic head measurements and the hydraulic boundary condition will significantly affect the simulation results. For our cases, after a few data assimilation steps, the assimilated conductivity field with four Neumann boundaries matches the real field well while the assimilated conductivity field with mixed Dirichlet and Neumann boundaries does not. We found in our cases that the ensemble size should be 300 or larger for the numerical simulation. The number and the locations of the observation wells will significantly affect the hydraulic conductivity field calibration.

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Section: Introductionmentioning

confidence: 99%

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Tong

Yang

2010

Stoch Environ Res Risk Assess

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“…In condition monitoring of railway vehicles, three main categories are existed; model based dynamical techniques including various Kalman [1,2] and particle filter [3], signal based techniques including band-pass filter, spectral analysis, wavelet analysis and Fast Fourier Transform [4], and wayside sensor based approaches which are generally practical in wheel defects according to a recent review [5]; using accelerometer and piezoelectric sensors on rail and using wavelet based methods and thresholding to identify the degree of the wheel flat [6], using a high speed camera to identify the wheel profile when the railway vehicle is passing by a low speed (10 mph) and image analysis, using optical sensors, accelerometers, load cells and strain gages to measure vertical deflection of the rail which makes it able to identify wheel defects like; out-of round, flat, shelling, measuring lateral force to determine bogie performance using hunting track detectors [7], acoustic bearing defect detectors which is based on statistical processing of the data, ultrasonic cracked wheel detection and many others [8].…”

Section: Introductionmentioning

confidence: 99%

Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside

Kılınç¹,

Vagner²

2017

Vib. proced.

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Abstract. This research examines two different methods; Wavelet Packet Energy (WPE) and Time-domain Features (TDF) which are effective in faulty signal feature extraction of metro wheels in wayside level using vibration sensors. Signals of each wheelset passing of a trainset with both healthy and faulty wheels are recorded by the vibration sensors which are mounted on both left and right rails and a novel one-period sampling is performed at 51.2 kHz sample rate. Retrieved signal samples are used in the construction of a database which is consistent of healthy and faulty cases. Since the database has insufficient number of faulty samples, the database is balanced by a method so called Adaptive Synthetic Sampling (ADASYN) so that each class has the same number of observations. Two state-of art classifiers; Support Vector Machines (SVM) and Fisher Linear Discriminant Analysis (FLDA) are employed by utilizing 16-fold cross validation to solve the two-class problem. Referring to the results, SVM-I-TDF outperforms by classifying all samples with a success rate of 100 % and other methods have also promising results. Proposed methods may be used in the condition monitoring of metro wheelsets effectively by means of not only performance but also cost-efficiency.

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“…We use a particle filter, namely a densitybased Monte Carlo filter to implement the Bayesian formulation. In [27], Kalman filter is used for system identification based on multiple static and dynamic test data. Kalman filter methodology can not be used in our problem, because the state equation in the filtering problem is modeled as an implicit micrograph synthesis algorithm, where as the Kalman filter requires the state equation to be a linear equation.…”

Section: Introductionmentioning

confidence: 99%

High resolution micrograph synthesis using a parametric texture model and a particle filter

Tipireddy

Ghanem

Ghosh

et al. 2013

Integr Mater Manuf Innov

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We present a methodology for synthesizing high resolution micrographs from low resolution ones using a parametric texture model and a particle filter. Information contained in high resolution micrographs is relevant to the accurate prediction of microstructural behavior and the nucleation of instabilities. As these micrographs may be tedious and uneconomical to obtain over an extended spatial domain, we propose a statistical approach for interpolating fine details over a whole computational domain starting with a low resolution prior and high resolution micrographs available only at a few spatial locations. As a first step, a small set of high resolution micrographs are decomposed into a set of multi-scale and multi-orientation subbands using a complex wavelet transform. Parameters of a texture model are computed as the joint statistics of the decomposed subbands. The synthesis algorithm then generates random micrographs satisfying the parameters of the texture model by recursively updating the gray level values of the pixels in the input micrograph. A density-based Monte Carlo filter is used at each step of the recursion to update the generated micrograph, using a low resolution micrograph at that location as a measurement. The process is continued until the synthesized micrograph has the same statistics as those from the high resolution micrographs. The proposed method combines a texture synthesis procedure with a particle filter and produces good quality high resolution micrographs.

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A Kalman filter based strategy for linear structural system identification based on multiple static and dynamic test data

Cited by 29 publications

References 23 publications

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data

Vibration based diagnosis of wheel defects of metro train sets using one period analysis on the wayside

High resolution micrograph synthesis using a parametric texture model and a particle filter

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