Simultaneous fault diagnosis based on multiple kernel support vector machine in nonlinear dynamic distillation column

Taqvi, Syed Ali Ammar; Zabiri, Haslinda; Uddin, Fahim; Naqvi, Muhammad; Tufa, Lemma Dendena; Kazmi, Majida; Rubab, Saddaf; Naqvi, Salman Raza; Maulud, Abdulhalim Shah

doi:10.1002/ese3.1058

Cited by 10 publications

(3 citation statements)

References 48 publications

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“…Recently, machine learning algorithms have gained great attention and are used in mechanical fault diagnosing operations. The use of a backpropagation neural network based on the Levenberg-Marquardt training algorithm to develop an effective fault detection strategy for proton exchange membrane fuel cells (PEMFCs) systems is presented in [39]. Another fault diagnosis based on a multiple kernel support vector machine for the distillation column is developed in [40].…”

Section: Previous Workmentioning

confidence: 99%

Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

Alshathri¹,

Hemdan²,

El‐Shafai³

et al. 2023

Computers, Materials &Amp; Continua

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In recent years, Digital Twin (DT) has gained significant interest from academia and industry due to the advanced in information technology, communication systems, Artificial Intelligence (AI), Cloud Computing (CC), and Industrial Internet of Things (IIoT). The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complexity during the life cycle that produces a massive amount of engendered data and information. Likewise, with the development of AI, digital twins can be redefined and could be a crucial approach to aid the Internet of Things (IoT)-based DT applications for transferring the data and value onto the Internet with better decision-making. Therefore, this paper introduces an efficient DT-based fault diagnosis model based on machine learning (ML) tools. In this framework, the DT model of the machine is constructed by creating the simulation model. In the proposed framework, the Genetic algorithm (GA) is used for the optimization task to improve the classification accuracy. Furthermore, we evaluate the proposed fault diagnosis framework using performance metrics such as precision, accuracy, F-measure, and recall. The proposed framework is comprehensively examined using the triplex pump fault diagnosis. The experimental results demonstrated that the hybrid GA-ML method gives outstanding results compared to ML methods like Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machine (SVM). The suggested framework achieves the highest accuracy of 95% for the employed hybrid GA-SVM. The proposed framework will effectively help industrial operators make an appropriate decision concerning the fault analysis for IIoT applications in the context of Industry 4.0.

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Section: Previous Workmentioning

confidence: 99%

Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

Alshathri¹,

Hemdan²,

El‐Shafai³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Taqvi et al [74] diagnosed multiple faults in distillation column by using multi kernel SVM in which multi label approach using various kernel function was utilized to identify eight simultaneous as valve sticking at reflux and reboiler, tray upsets, loss of feed flow, feed composition, and feed temperature changes. Among the developed models quadratic kernel-based model gave 99.7 % accuracy which is higher than others, hence selected for fault classification.…”

Section: Application Of Artificial Intelligence In Oil and Gas Indust...mentioning

confidence: 99%

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Khan

Taqvi

2022

ChemBioEng Reviews

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The field of machine learning has proven to be a powerful approach in smart manufacturing and processing in the chemical and process industries. This review provides a systematic overview of current state of artificial intelligence and machine learning and their applications in textile, nuclear power plant, fertilizer, water treatment, and oil and gas industries. Moreover, this study reveals the current dominant machine learning methods, pre and post processing of models, increased utilization of machine learning in terms of fault detection, prediction, optimization, quality control, and maintenance in these sectors. In addition, this review gives the insight into the actual benefits and impact of each method, and complications in their extensive deployment. Finally in the current impressive state, challenges, future development in terms of algorithm and infrastructure aspects are highlighted.

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“…Various control strategies based on artificial intelligence techniques have been reported in the literature, such as generic model control (GMC) [7], artificial neural networks (ANN) [8][9][10][11][12], fuzzy logic control (FLC) [13,14], and hybrid controllers [15]. The most commonly applied intelligent techniques are ANN and fuzzy logic (FL).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of an Industrial Debutanizer Column

et al. 2022

Self Cite

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A debutanizer column located at a refinery in Malaysia produces LPG as its top product and light naphtha as its bottom product. Control of the product compositions of the column is very important to maintain the desired purities. The current control design of the debutanizer column is based on the classical proportional integral derivative approach, which has been found to be less effective in controlling the variations in the column as the process is characterized by strong nonlinear and uncertain dynamics. Here, intelligent control based on an adaptive neurofuzzy inference system (ANFIS) is investigated and compared against an artificial neural network, for the special case of limited training data of the industrial debutanizer column. The ANFIS-based controller outperforms the other control configurations for both set point tracking and disturbance rejection cases and has better generalization performance even when trained with limited data samples.

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Simultaneous fault diagnosis based on multiple kernel support vector machine in nonlinear dynamic distillation column

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 10 publications

References 48 publications

Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Intelligent Control of an Industrial Debutanizer Column

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