Empirical modelling approaches to modelling failures

Jaiwook Baik,Jinnam Jo 한국신뢰성학회 2013 International Journal of Reliability and Applicati Vol.14 No.2

Modelling of failures is an important element of reliability modelling. Empirical modelling approach suitable for complex item is explored in this paper. First step of the empirical modelling approach is to plot hazard function, density function, Weibull probability plot as well as cumulative intensity function to see which model fits best for the given data. Next step of the empirical modelling approach is select appropriate model for the data and fit the parametric model accordingly and estimate the parameters.

Modelling Approaches to Failures

백재욱 공주대학교 KNU 기업경영연구소 2017 기업경영리뷰 Vol.8 No.1

Modelling of failures is an important element of reliability modelling. The approach to modelling depends on the kind of information available and the goal of the modelling. Empirical modelling approach suitable for complex item is explored in this paper. First step of the empirical modelling approach is to plot hazard function, density function, Weibull probability plot as well as cumulative intensity function to see which model fits best for the given data. Next step of the empirical modelling approach is select appropriate model for the data and fit the parametric model accordingly and estimate the parameters.

An improved SCGM(1,m) model for multi-point deformation analysis

Qi-jie Wang,Chang-cheng Wang,Rong-an Xie,Xin-qing Zhang,Jian-jun Zhu 한국지질과학협의회 2014 Geosciences Journal Vol.18 No.4

Considering the deformation of discrete monitoringpoints within the same deformable body usually have similar physicalproperties and tend to undergoing identical dynamic process, jointmodelling of the deformation processes of these points in time domainare expected to generate better results. Yin et al. (1997) first extendedthe multi-variable grey model-system cloud grey model SCGM(1,m),with obviously superior modelling mechanism than single-variablegrey model, to multi-point deformation modelling. However, thismodel is still not widely recognized and its applications remain verylimited in the field of deformation analysis. The objective of this studyis to demonstrate the capability of the SCGM(1,m) model, to presenttwo revisions to further improve the performance of the model andto draw more attention to the community of deformation analysis. We first introduce the principles of the SCGM(1,m) model in theanalysis and prediction of deformation surveys. Two practicaltechniques, namely residuals re-modelling and linear regressionadjustment, are then presented to improve the SCGM(1,m) model. Combined with slope monitoring data, the modelling with the originaland the improved SCGM(1,m) models by residuals re-modellingand linear regression adjustment are illustrated. The mean relativeprediction errors decrease from 5.89% to 3.54% and 2.69%, whenthe two refining techniques are applied, respectively, indicating relativeimprovements of 39.9% and 54.3%.

Empirical modelling approaches to modelling failures

Baik, Jaiwook,Jo, Jinnam The Korean Reliability Society 2013 International Journal of Reliability and Applicati Vol.14 No.2

Modelling of failures is an important element of reliability modelling. Empirical modelling approach suitable for complex item is explored in this paper. First step of the empirical modelling approach is to plot hazard function, density function, Weibull probability plot as well as cumulative intensity function to see which model fits best for the given data. Next step of the empirical modelling approach is select appropriate model for the data and fit the parametric model accordingly and estimate the parameters.

Robust finite element model updating of a large-scale benchmark building structure

E. Matta,A. De Stefano 국제구조공학회 2012 Structural Engineering and Mechanics, An Int'l Jou Vol.43 No.3

Accurate finite element (FE) models are needed in many applications of Civil Engineering such as health monitoring, damage detection, structural control, structural evaluation and assessment. Model accuracy depends on both the model structure (the form of the equations) and the model parameters (the coefficients of the equations), and can be generally improved through that process of experimental reconciliation known as model updating. However, modelling errors, including (i) errors in the model structure and (ii) errors in parameters excluded from adjustment, may bias the solution, leading to an updated model which replicates measurements but lacks physical meaning. In this paper, an application of ambient-vibration-based model updating to a large-scale benchmark prototype of a building structure is reported in which both types of error are met. The error in the model structure, originating from unmodelled secondary structural elements unexpectedly working as resonant appendages, is faced through a reduction of the experimental modal model. The error in the model parameters, due to the inevitable constraints imposed on parameters to avoid ill-conditioning and under-determinacy, is faced through a multi-model parameterization approach consisting in the generation and solution of a multitude of models, each characterized by a different set of updating parameters. Results show that modelling errors may significantly impair updating even in the case of seemingly simple systems and that multimodel reasoning, supported by physical insight, may effectively improve the accuracy and robustness of calibration.

Modelling and control of a wind turbine and farm

Elsevier 2018 ENERGY Vol.156 No.-

<P><B>Abstract</B></P> <P>The Matlab/Simulink model of the Supergen (Sustainable Power Generation and Supply) Wind 5 MW exemplar wind turbine, which has been employed by a number of researchers at various institutions and Universities over the last decade, is reported. It is subsequently improved, especially in speed, to facilitate wind farm modelling, which usually involves duplicating wind turbine models. The improvement is achieved through various stages, including prewarping, discretisation using Heun's method in addition to Euler method, and conversion to C. Results are presented to demonstrate that improvement in speed is significant and that the resulting wind turbine model can be used for wind farm modelling more efficiently. It is important to highlight that improvement in speed is achieved without compromising the complexity of the turbine model; that is, each turbine included in a wind farm is neither simplified nor compromised. The use of the wind farm model for testing a wind farm controller that has recently been introduced is also demonstrated.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The Matlab/Simulink model of a 5 MW exemplar wind turbine is improved in speed. </LI> <LI> The model developed by Supergen Wind has been used for various projects in Europe. </LI> <LI> The improvement is due to discretisation and conversion to C. </LI> <LI> The discretisation employs both implicit and explicit methods with prewarping. </LI> <LI> A wind farm controller is tested by application to the model. </LI> </UL> </P>

Robust finite element model updating of a large-scale benchmark building structure

Matta, E.,De Stefano, A. Techno-Press 2012 Structural Engineering and Mechanics, An Int'l Jou Vol.43 No.3

Accurate finite element (FE) models are needed in many applications of Civil Engineering such as health monitoring, damage detection, structural control, structural evaluation and assessment. Model accuracy depends on both the model structure (the form of the equations) and the model parameters (the coefficients of the equations), and can be generally improved through that process of experimental reconciliation known as model updating. However, modelling errors, including (i) errors in the model structure and (ii) errors in parameters excluded from adjustment, may bias the solution, leading to an updated model which replicates measurements but lacks physical meaning. In this paper, an application of ambient-vibration-based model updating to a large-scale benchmark prototype of a building structure is reported in which both types of error are met. The error in the model structure, originating from unmodelled secondary structural elements unexpectedly working as resonant appendages, is faced through a reduction of the experimental modal model. The error in the model parameters, due to the inevitable constraints imposed on parameters to avoid ill-conditioning and under-determinacy, is faced through a multi-model parameterization approach consisting in the generation and solution of a multitude of models, each characterized by a different set of updating parameters. Results show that modelling errors may significantly impair updating even in the case of seemingly simple systems and that multi-model reasoning, supported by physical insight, may effectively improve the accuracy and robustness of calibration.

Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization

Ning Li,Panagiotis G. Asteris,Trung-Tin Tran,Biswajeet Pradhan,Hoang Nguyen 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.42 No.6

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner

An Approach for Physical Model Adaptation based on transient Measurements

Vasco Schirrmacher,Philipp Schmiechen,Mirko Knaak,Akira Ohata,Satoshi Kaneko 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8

In order to cope with the ever increasing complexity of the control of internal combustion engines, modelbased calibration processes have become well established in recent years. Physical engine models are often used in early development stages, when no measurement data is available. At that stage, physical models can give a good, even though rough, overview over the overall engine behavior. Later on in the development process, however, an adaptation to a particular engine is usually required in order to match that particular engine’ output as good as possible. In contrast to data-driven statistical models, physical models have the disadvantage that one has to decide how to calibrate them, since they have many parameters, which are in addition often difficult to measure. One way of adaptation to measurement data, is by using statistical correction models. These can accommodate modeling shortcomings such as unknown or non-representable physical effects. However, recording of steady state measurement data typically requires a long measurement time. A model adaptation based on transient measurements would not only provide much more data points within shorter measurements periods but also information about the dynamic engine behavior during transient operation. This paper presents an approach of how to include transient measurements in a fitting process for a physical model. In order to demonstrate the flexibility of the method even on unmeasured quantities, we created a compensation model based on this structure for an air charge model and investigated its influence on the prediction performance.

Load Modeling based on System Identification with Kalman Filtering of Electrical Energy Consumption of Residential Air-Conditioning

Nopporn Patcharaprakiti,Kasem Tripak,Jeerawan Saelao 한국인터넷방송통신학회 2015 Journal of Advanced Smart Convergence Vol.4 No.1

This paper is proposed mathematical load modelling based on system identification approach of energy consumption of residential air conditioning. Due to air conditioning is one of the significant equipment which consumes high energy and cause the peak load of power system especially in the summer time. The demand response is one of the solutions to decrease the load consumption and cutting peak load to avoid the reservation of power supply from power plant. In order to operate this solution, mathematical modelling of air conditioning which explains the behaviour is essential tool. The four type of linear model is selected for explanation the behaviour of this system. In order to obtain model, the experimental setup are performed by collecting input and output data every minute of 9,385 BTU/h air-conditioning split type with 25 C thermostat setting of one sample house. The input data are composed of solar radiation (W/m2) and ambient temperature (C). The output data are power and energy consumption of air conditioning. Both data are divided into two groups follow as training data and validation data for getting the exact model. The model is also verified with the other similar type of air condition by feed solar radiation and ambient temperature input data and compare the output energy consumption data. The best model in term of accuracy and model order is output error model with 70.78% accuracy and 17th order. The model order reduction technique is used to reduce order of model to seven order for less complexity, then Kalman filtering technique is applied for remove white Gaussian noise for improve accuracy of model to be 72.66%. The obtained model can be also used for electrical load forecasting and designs the optimal size of renewable energy such photovoltaic system for supply the air conditioning.