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Volume 20 -                   ijmt 2024, 20 - : 61-69 | Back to browse issues page

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Zarghami M, Raissi S, hamid T, Bamdad S. A data-driven artificial intelligence approach to predict the remaining useful life of Neuero grain unloaders in Khuzestan ports. ijmt 2024; 20 :61-69
URL: http://ijmt.ir/article-1-845-en.html
A data-driven artificial intelligence approach to predict the remaining useful life of Neuero grain unloaders in Khuzestan ports
Mohammadali Zarghami1 , Sadigh Raissi *1 , Tohidi Hamid1 , Shahrooz Bamdad1
1- South Tehran Azad University
Abstract:   (335 Views)
This study aims to enhance equipment management in grain unloading operations at Khuzestan Ports in Iran by predicting the remaining useful life of electric motors used in grain suction systems (neuero). Utilizing LSTM models in conjunction with environmental factors, this research minimizes unexpected costs associated with equipment failures and reduces downtime in unloading and loading processes. Real-world data from Khuzestan ports demonstrates the high accuracy of the LSTM model in predicting failures. The findings support proactive maintenance strategies, thereby improving efficiency and reliability in the port and maritime industry. While challenges such as limited data, incomplete coverage of environmental factors, and reliance on deep learning models exist, this study provides a foundation for future research on optimizing maintenance and management of neuero electric motors in bulk vessels.
Keywords: Remaining Life Prediction, Failure Process Modeling, Neural Networks, Artificial Intelligence, Data-Driven Approach.
Full-Text [PDF 631 kb]   (105 Downloads)    
Type of Study: Research Paper | Subject: Main Engine & Electrical Equipments
Received: 2024/10/18 | Accepted: 2025/01/6
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Creative Commons License
International Journal of Maritime Technology is licensed under a

Creative Commons Attribution-NonCommercial 4.0 International License.