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

Abstract 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.