Intelligent Industrial Process Control Based on Fuzzy Logic and Machine Learning

Zermane, Hanane; Kasmi, Rached

doi:10.4018/ijfsa.2020010104

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…With the assistance of SVMs, one can perform both linear as well as non-linear classification (Goudjil et al, 2018;Liu et al, 2012). SVM has become very widespread in research and has been incorporated into several fields including medical (Bromová et al, 2014;Mark Chang, 2020), military (Mohril et al, 2020;Rozek et al, 2020), industry (Zermane & Kasmi, 2020), and so forth; and a variety of applications, including image classification (Elaziz et al, 2020), text mining (Chatterjee et al, 2021;Court & Cole, 2020), video recommendation (Bălan et al, 2020;Massiris Fernández et al, 2020), and multimedia concept retrieval (Aslam & Curry, 2021;Moreno-Schneider et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Intelligent Control System of a Real Industrial Process

ZERMANE¹,

Hassina²

2022

EPSTEM

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Industrial systems are difficult to control and supervise efficiently because of the complexity of the production process. The aim is to automatically control in real-time as an alternative for operators as possible and highlight the importance of machine learning in the field of industry. Integrating SVM into the industrial supervision system in the cement factory (SCIMAT) permits the classification of different measurements coming from sensors to the Programmable Logic Controller (PLC) that indicates when the process is in good functioning or bad indicating that a default has occurred. These measurements are classified after training in three classes of level (low, medium, and high) that are classified in their turn into two classes (good and bad functioning). The three classes present the inputs of the fuzzy controllers. Based on this classification, the PLC makes orders for industrial equipment. Then a regression of variation of measurements in real-time is carried out to predict the good or the bad functioning of the production line. In conclusion, the proposed approach innovates the complex supervision system to learn how to control and preserve the habitual linguistic language used by operators, react in the right way, and prevent critical situations.

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Section: Methodsmentioning

confidence: 99%

Intelligent Control System of a Real Industrial Process

ZERMANE¹,

Hassina²

2022

EPSTEM

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“…SVM employs an iterative method to minimize the error function to identify the best separable hyperplane and maximizes the margin between classes. Several studies applied SVM in different other elds (3,32,33), especially, in industry and automation (13,(34)(35)(36)(37).…”

Section: Support Vector Machinementioning

confidence: 99%

Development of an efficient cement production monitoring system based on the improved random forest algorithm

Zermane

Drardja

2022

Int J Adv Manuf Technol

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Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies and there is increasing competitiveness among them and increase companies' value. Machine Learning (ML) techniques have become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0, and the extensive integration of paradigms such as big data, cloud computing, high computational power, and enormous storage capacity. Implementing a system that can identify faults early to avoid critical situations in the line production and environment is crucial. Therefore, one of the powerful machine learning algorithms is Random Forest (RF). The ensemble learning algorithm is performed to fault diagnosis and SCADA real-time data classi cation and predicting the state of the line production. Random Forests proved to be a better classi er with a 95% accuracy. Comparing to the SVM model, the accuracy is 94.18%, however, the K-NN model accuracy is about 93.83%, an accuracy of 80.25% is achieved using the logistic regression model, nally, about 83.73% is obtained by the decision tree model. The excellent experimental results achieved on the Random Forest model showed the merits of this implementation in the production performance, ensuring predictive maintenance, and avoid wasting energy.

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Section: Support Vector Machinementioning

confidence: 99%

Development of an Efficient Cement Production Monitoring System Based on the Improved Random Forest Algorithm

Zermane

Drardja

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies and there is increasing competitiveness among them and increase companies’ value. Machine Learning (ML) techniques have become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0, and the extensive integration of paradigms such as big data, cloud computing, high computational power, and enormous storage capacity. Implementing a system that can identify faults early to avoid critical situations in the line production and environment is crucial. Therefore, one of the powerful machine learning algorithms is Random Forest (RF). The ensemble learning algorithm is performed to fault diagnosis and SCADA real-time data classification and predicting the state of the line production. Random Forests proved to be a better classifier with a 95% accuracy. Comparing to the SVM model, the accuracy is 94.18%, however, the K-NN model accuracy is about 93.83%, an accuracy of 80.25% is achieved using the logistic regression model, finally, about 83.73% is obtained by the decision tree model. The excellent experimental results achieved on the Random Forest model showed the merits of this implementation in the production performance, ensuring predictive maintenance, and avoid wasting energy.

show abstract

Intelligent Industrial Process Control Based on Fuzzy Logic and Machine Learning

Cited by 7 publications

References 42 publications

Intelligent Control System of a Real Industrial Process

Intelligent Control System of a Real Industrial Process

Development of an efficient cement production monitoring system based on the improved random forest algorithm

Development of an Efficient Cement Production Monitoring System Based on the Improved Random Forest Algorithm

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