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Author: search.filters.author.Kouadri, Abdelmalek (supervisor)Subject: search.filters.subject.Principal Component Analysis (PCA)

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    Reduced kernel PCA Technique for fault
    (2021) Boulmerdj, Anes; Bounaas, Alaeddine; Kouadri, Abdelmalek (supervisor)
    Fault detection and diagnosis is an important problem in process engineering. It is the central component of abnormal event management (AEM) which has attracted a lot of attention recently. This thesis discuses different classes of FDD approaches for process monitoring. In addition, it presents main results of fault detection and diagnosis in a cement manufacturing plant using three monitoring techniques. The techniques are based on multivariate statistical analysis and a threshold strategy. The process is statistically modeled using Principle Component Analysis (PCA), kernel PCA and new proposed reduced KPCA to cope with the computational problem introduced by KPCA. The proposed RKPCA method consists on reducing the number of observations in a data matrix using a proposed algorithm based on fractal dimension. The Hotelling’s T², Q in addition to the new proposed index called the combined statistic f are used as fault indicators for testing PCA, KPCA and the suggested approach RKPCA carried out using the cement rotary kiln system. The three methods are compared to in terms of False Alarms Rate (FAR), Missed Alarms Rate (MDR), Detection Time Delay (DTD) and the cost function (J). The obtained results demonstrate the effectiveness of the proposed technique in reducing the number of observations from 768 to 11, leading to an 11x11 kernel matrix instead of 768x768, hence, diminishing computational time and storage requirement. Moreover, it has effectively detected the different types of faults when using statistical indices.
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    Intelligent fault diagnosis scheme for Plant-wide processes
    (2021) Goutas, Firas; Aouaneche, Farouk; Kouadri, Abdelmalek (supervisor)
    For fault detection and diagnosis in complex systems, model based methods are not very practical. Instead, data-driven techniques are widely used especially machine learning methods such as classification and k-mean clustering. However to our knowledge, hierarchical clustering technique based on KLD is not explored in this field. This study suggests a new approach to build an unsupervised model for fault diagnosis using hierarchical clustering with the statistical multivariate technique Kullback-Leibler divergence as an index to compute the dissimilarity degree between the different data distributions. These datasets are preprocessed through the principal component analysis (PCA) which allows to reduce the dimensionality and generates a principal and a residual subspace that can both be used to train our model which can be visualized in a dendrogram. In order to produce the optimal model, we set various CPVs to obtain different numbers of retained components, hence different models. The proposed method was applied to plant-wide Tennessee Eastman process to test the accuracy and the elapsed time of our algorithm. The results show the effectiveness of our optimal model with an accuracy of 86.36% of correct predictions. Keywords: Fault Diagnosis (FD), Machine Learning (ML), Hierarchical Clustering, Kullback- Leibler Divergence (KLD), Principal Component Analysis (PCA), Dissimilarity Index, Tennessee Eastman Process.