Browsing by Author "Fezai, Radhia"

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A Hybrid Approach for Process Monitoring: Improving Data-Driven Methodologies with Dataset Size Reduction and Interval-Valued Representation
(IEEE, 2020) Dhibi, Khaled; Fezai, Radhia; Mansouri, Majdi; Kouadri, Abdelmalek
Kernel principal component analysis (KPCA) is a well-established data-driven process modeling and monitoring framework that has long been praised for its performances. However, it is still not optimal for large-scale and uncertain systems. Applying KPCA usually takes a long time and a significant storage space when big data are utilized. In addition, it leads to a serious loss of information and ignores uncertainties in the processes. Consequently, in this paper, two uncertain nonlinear statistical fault detection methods using an interval reduced kernel principal component analysis (IRKPCA) are proposed. The main objective of the proposed methods is twofold. Firstly, reduce the number of observations in the data matrix through two techniques: a method, called IRKPCAED, is based on Euclidean distance between samples as dissimilarity metric such that only one observation is kept in case of redundancy to build the reduced reference KPCA model, and another method, called IRKPCAPCA, is established on the PCA algorithm to treat the hybrid correlations between process variables and extract a reduced number of observations from the training data matrix. Secondly, address the problem of uncertainties in systems using a latent-driven technique based on interval-valued data. Taking into account sensors uncertainties via IRKPCA ensures better monitoring by reducing the computational and storage costs. The study demonstrated the feasibility and effectiveness of the proposed approaches for faults detection in two real world applications: Tennessee Eastman (TE) process and real air quality monitoring network (AIRLOR) data
Reduced Kernel Random Forest Technique for Fault Detection and Classification in Grid-Tied PV Systems
(IEEE, 2020) Dhibi, Khaled; Fezai, Radhia; Mansouri, Majdi; Trabelsi, Mohamed; Abdelmalek, Kouadri; Bouzara, Kais; Hazem, Nounou; Nounou, Mohamed
The random forest (RF) classifier, which is a combination of tree predictors, is one of the most powerful classification algorithms that has been recently applied for fault detection and diagnosis (FDD) of industrial processes. However, RF is still suffering from some limitations such as the noncorrelation between variables. These limitations are due to the direct use of variables measured at nodes and therefore the only use of static information from the process data. Thus, this article proposes two enhanced RF classifiers, namely the Euclidean distance based reduced kernel RF (RK-RF ED ) and K-means clustering based reduced kernel RF (RK-RF Kmeans ), for FDD. Based on the kernel principal component analysis, the proposed classifiers consist of two main stages: feature extraction and selection, and fault classification. In the first stage, the number of observations in the training data set is reduced using two methods: the first method consists of using the Euclidean distance as dissimilarity metric so that only one measurement is kept in case of redundancy between samples. The second method aims at reducing the amount of the training data based on the K-means clustering technique. Once the characteristics of the process are extracted, the most sensitive features are selected. During the second phase, the selected features are fed to an RF classifier. An emulated grid-connected PV system is used to validate the performance of the proposed RK-RF ED and RK-RF Kmeans classifiers. The presented results confirm the high classification accuracy of the developed techniques with low computation time.