Artificial Neural Network-Based Vibration Data Analysis to Predict Failures of Powder Machines
Salem, K., Abdel-Gwad, E., Kouta, H. (2024). Artificial Neural Network-Based Vibration Data Analysis to Predict Failures of Powder Machines. EKB Journal Management System, 28(3), 103-120. doi: 10.21608/pserj.2024.266793.1317
khaled Waled Salem; Ebtisam Abdel-Gwad; Hanan Kamel Kouta. "Artificial Neural Network-Based Vibration Data Analysis to Predict Failures of Powder Machines". EKB Journal Management System, 28, 3, 2024, 103-120. doi: 10.21608/pserj.2024.266793.1317
Salem, K., Abdel-Gwad, E., Kouta, H. (2024). 'Artificial Neural Network-Based Vibration Data Analysis to Predict Failures of Powder Machines', EKB Journal Management System, 28(3), pp. 103-120. doi: 10.21608/pserj.2024.266793.1317
Salem, K., Abdel-Gwad, E., Kouta, H. Artificial Neural Network-Based Vibration Data Analysis to Predict Failures of Powder Machines. EKB Journal Management System, 2024; 28(3): 103-120. doi: 10.21608/pserj.2024.266793.1317

Artificial Neural Network-Based Vibration Data Analysis to Predict Failures of Powder Machines

Port-Said Engineering Research Journal
Volume 28, Issue 3, September 2024, Page 103-120  XML PDF (958.88 K)
Document Type: Original Article
DOI: 10.21608/pserj.2024.266793.1317
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Authors
khaled Waled Salem email 1; Ebtisam Abdel-Gwad2; Hanan Kamel Kouta3
1Production Engineering and Mechanical Design Department, Faculty of Engineering, Port Said University, Port Said
2Professor, Production Engineering and Mechanical Design Department, Faculty of Engineering, Port Said University, Port Said, Egypt
3Department of production engineering and mechanical design, Faculty of Engineering, Port Said University, Port Said, Egypt
Abstract
In the dynamic landscape of Industry 5.0, this research pioneers an advanced predictive maintenance methodology for a forced blower in PVC powder manufacturing at TCI Sanmar. Leveraging machine learning, this study reveals the Multilayer Perceptron (MLP) algorithm as a top performer, exhibiting exceptional efficacy in foreseeing machine failures. Following a comprehensive GridSearchCV optimization, the best-performing MLP variant demonstrated remarkable metrics, boasting an accuracy of 92.5%, Recall of 95.5%, Precision of 95%, an F-score of 92.7%, and a Matthews Correlation Coefficient (MCC) of 0.85. Furthermore, achieving consistently high Area Under the ROC Curve (AUC) values at 0.961, the selected MLP configuration outshines counterparts in the same industrial setting. This research extends beyond immediate scope, contributing crucial insights to predictive maintenance strategies. The results affirm the strategic importance of the MLP algorithm in the Industry 5.0 context, emphasizing its role in intelligent, interconnected manufacturing processes. This exploration optimizes equipment reliability, minimizes downtime, and provides valuable insights for industrial efficiency through proactive maintenance interventions.
Keywords
Maintenance Strategy Optimization; Industry 5.0 Integration; Advanced Machine Learning Applications; Equipment Health Monitoring; Intelligent Manufacturing Insights
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