Search Results - (( variable detection method algorithm ) OR ( using simulation model algorithm ))

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  1. 1
    Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data

    Robust diagnostics and variable selection procedure based on modified reweighted fast consistent and high breakdown estimator for high dimensional data by Baba, Ishaq Abdullahi

    Published 2022
    “…Sure screening-based correlation methods are popular tools used to select the most significant variables in the true model in sparse and high dimensional analysis. …”
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  2. 2
    Fault diagnostic algorithm for precut fractionation column

    Fault diagnostic algorithm for precut fractionation column by Heng, H. Y., Ali, Mohamad Wijayanuddin, Kamsah, Mohd. Zaki

    Published 2004
    “…Hazard and Operability Study (HAZOP) is used to support the diagnosis task. The algorithm has been successful in detecting the deviations of each variable by testing the data set. …”
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    Conference or Workshop Item
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  3. 3
    Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification

    Deterministic Mutation-Based Algorithm for Model Structure Selection in Discrete-Time System Identification by Abd Samad, Md Fahmi

    Published 2011
    “…A deterministic mutation-based algorithm is introduced to overcome this problem. Identification studies using NARX (Nonlinear AutoRegressive with eXogenous input) models employing simulated systems and real plant data are used to demonstrate that the algorithm is able to detect significant variables and terms faster and to select a simpler model structure than other well-known EC methods.…”
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  4. 4
    Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC)

    Development of fault detection, diagnosis and control system identification using multivariate statistical process control (MSPC) by Ibrahim, Kamarul 'Asri, Ahmad, Arshad, Ali, Mohamad Wijayanuddin, Mak, Weng Yee

    Published 2006
    “…In this research work, an FDD algorithm is developed using MSPC and correlation coefficients between process variables. …”
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    Monograph
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  5. 5
    Development of a rule-based fault diagnostic advisory system for precut fractionation column

    Development of a rule-based fault diagnostic advisory system for precut fractionation column by Heng, Han Yann

    Published 2005
    “…The advisory system algorithm used process history based method and presented by rule-based approach. …”
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  6. 6
    Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari

    Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari by Satari, Siti Zanariah

    Published 2015
    “…We classify the cluster group that exceeds the stopping rule as potential outliers. Model verification of all method and model proposed in this study are examined using the simulation study. …”
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  7. 7
    Robust Estimation Methods And Outlier Detection In Mediation Models

    Robust Estimation Methods And Outlier Detection In Mediation Models by Fitrianto, Anwar

    Published 2010
    “…When the relationship between the dependent variable (DV) and an independent variables (IV) can be accounted for by an intermediate variable M, mediation is said to occur. Simple mediation model consists of three regression equations. The Ordinary Least Squares (OLS) method is often use to estimate the parameters of the mediation model. …”
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  8. 8
    DEVELOPMENT OF OE-BASED BROWN-FORSYTHE TEST ALGORITHM FOR CONTROL VALVE STICTION DETECTION

    DEVELOPMENT OF OE-BASED BROWN-FORSYTHE TEST ALGORITHM FOR CONTROL VALVE STICTION DETECTION by JOLENE DIANDRA, JAMES

    Published 2018
    “…Therefore, the main objective of the project is to develop an OE-based Brown-Forsythe test algorithm to effectively detect the presence of control valve stiction.In this study, the proposed OE model is developed using System Identification in MATLAB, where it is used to simulate the process output (PV). …”
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    Final Year Project Report / IMRAD
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  9. 9
    An Automated Method For Model-Plant Mismatch Detection And Correction In Process Plants Employing Model Predictive Control (MPC)

    An Automated Method For Model-Plant Mismatch Detection And Correction In Process Plants Employing Model Predictive Control (MPC) by Ahmed Bahakim, Sami Saeed

    Published 2012
    “…A model-predictive controller (MPC) uses the process model to predict future outputs of the system. …”
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    Final Year Project
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  10. 10
    A simple model-free butterfly shape-based detection (BSD) method integrated with deep learning CNN for valve stiction detection and quantification

    A simple model-free butterfly shape-based detection (BSD) method integrated with deep learning CNN for valve stiction detection and quantification by Kamaruddin, B., Zabiri, H., Mohd Amiruddin, A.A.A., Teh, W.K., Ramasamy, M., Jeremiah, S.S.

    Published 2020
    “…In this paper, a â��butterflyâ�� shape derived from the manipulation of the standard PV and OP data, which is more robust towards different loop dynamics, is developed for stiction detection. This simple model-free butterfly shape-based detection (BSD) method uses Stenman's one parameter stiction model, which results in a distinctive â��butterflyâ�� pattern in the presence of stiction. …”
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  11. 11
    Detection of leak size and its location in a water distribution system by using K-NN / Nasereddin Ibrahim Sherksi

    Detection of leak size and its location in a water distribution system by using K-NN / Nasereddin Ibrahim Sherksi by Sherksi, Nasereddin Ibrahim

    Published 2020
    “…This thesis proposes a classification model to detect water leakage, focusing on finding water leakage’s location and size, using K-Nearest Neighbour (K-NN) classification method. …”
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  12. 12
    Fault detection and diagnosis using rule-based support system on fatty acid fractionation column

    Fault detection and diagnosis using rule-based support system on fatty acid fractionation column by Yann, H. H., Ali, Mohamad Wijayanuddin, Kamsah, Mohd Zaki

    Published 2003
    “…Plant model was simulated by using an existing commercial process simulator-HYSYS. …”
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  13. 13
    Fault detection and diagnosis using correlation coefficients

    Fault detection and diagnosis using correlation coefficients by Mak, Weng Yee

    Published 2005
    “…Shewhart Control Chart and Range Control Chart together with the developed correlation coefficients are used for fault detection and diagnosis. Results show that the method based on PCorrA (overall FDD efficiency = 100%) is more superior than the method based on NC (overall FDD efficiency = 67.82%) and the two analysis methods based on PCA (overall FDD efficiency = 67.82%).…”
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  14. 14
    Preliminary study on fault detection using artificial neural network for water-cooled reactors

    Preliminary study on fault detection using artificial neural network for water-cooled reactors by Abdul Karim, Julia, Lanyau, Tony, Maskin, Masleha, Anuar, M. A. S., Che Soh, Azura, Abdul Rahman, Ribhan Zafira

    Published 2020
    “…Using actual data from the reactor to train the multilayer network model with backpropagation algorithm. …”
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  15. 15
    Numerical Analysis of structural batteries response with the presence of uncertainty

    Numerical Analysis of structural batteries response with the presence of uncertainty by Syahiir Kamil, Ahmad Kamal Ariffin, Abdul Hadi Azman, Mohamad Syazwan Zafwan Mohamad Suffian

    Published 2023
    “…Uncertainty of the material properties and loading are modeled as Fuzzy Random Variables. In evaluating the influence of the uncertainty parameters, Interval Monte Carlo Simulation and the interval finite element method are used to compute the bounds of the structure behavior. …”
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  16. 16
    Numerical Analysis of Structural Batteries Response with the Presence of Uncertainty

    Numerical Analysis of Structural Batteries Response with the Presence of Uncertainty by Syahiir, Kamil, Mohamad Syazwan Zafwan, Mohamad Suffian, Ahmad Kamal Ariffin, Mohd Ihsan, Abdul Hadi, Azman

    Published 2023
    “…Uncertainty of the material properties and loading are modeled as Fuzzy Random Variables. In evaluating the influence of the uncertainty parameters, Interval Monte Carlo Simulation and the interval finite element method are used to compute the bounds of the structure behavior. …”
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  17. 17
    On line fault detection for transmission line using power system stabilizer signals

    On line fault detection for transmission line using power system stabilizer signals by Ali Falifla, Hamza AbuBeker

    Published 2007
    “…Then by using PST(Power System Toolbox) to build state variable models in small signal analysis, and for modeling of machines and control system for performing transient stability simulation of a power system, These dynamic models are coded as MATLAB functions. …”
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  18. 18
    Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis

    Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis by Mohamed Elfaki, Faiz Ahmed

    Published 2004
    “…A simulation data using subdistribution function for the two types of failure are carried out to compare the performance of the modified model. …”
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  19. 19
    Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid

    Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid by Gaeid, Khalaf Salloum

    Published 2012
    “…The fault detection algorithm identifies the time and location of each fault. …”
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  20. 20
    Model predictive control based on Lyapunov function and near state vector selection of four-leg inverter / Abdul Mannan Dadu

    Model predictive control based on Lyapunov function and near state vector selection of four-leg inverter / Abdul Mannan Dadu by Abdul Mannan, Dadu

    Published 2018
    “…The proposed control algorithm takes advantage of a predefined Lyapunov control law which minimizes the required calculation time by the Lyapunov model equations just once in each control loop to predict future variables. …”
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