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

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
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:   (1321 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]   (431 Downloads)    
Type of Study: Research Paper | Subject: Main Engine & Electrical Equipments
Received: 2024/10/18 | Accepted: 2025/01/6
References
1. Seif, M. S., Sadeghi, A. A., & Mohammadi, M. (2023). Application of artificial intelligence in predicting the failure of marine equipment. International Journal of Maritime Technology, 18(2), 125-134.
2. Ahmadi, A. R., Alizad, H., & Rezaei, M. B. (2022). Reliability assessment of marine propulsion systems using simulation methods. International Journal of Maritime Technology, 17(4), 281-290.
3. Mohammadi, S., Hosseini, A. H., & Jafari, A. A. (2021). A novel model for preventive maintenance of marine equipment. International Journal of Maritime Technology, 16(3), 197-206.
4. Rezaei, M., Sadeghi, M. H., & Ahmadi, S. (2020). Application of artificial intelligence in ship maintenance. International Journal of Maritime Technology, 15(2), 113-122.
5. Mohammadi, A., Ahmadi, A., & Hosseini, H. (2019). The impact of artificial intelligence on the maritime industry. International Journal of Maritime Technology, 14(4), 271-280.
6. Rezaei, M., Sadeghi, M. H., & Ahmadi, S. (2022). An AI-based system for condition monitoring of marine equipment. International Journal of Maritime Technology, 17(3), 207-216.
7. Jin, X., Wang, P., & Tsui, K. L. (2016). An adaptive prognostic model for rolling element bearings based on particle filter and extended Kalman filter. IEEE Transactions on Industrial Electronics, 63(10), 6148-6157.
8. Huang, H. Z., Xi, L., Li, C., & Liu, C. (2017). Remaining useful life prediction for machinery based on adaptive skew-Wiener process and health state similarity. Mechanical Systems and Signal Processing, 85, 770-787.
9. Yang, J., Zhang, Y., & Wang, P. (2019). Deep convolutional neural network-based remaining useful life prediction of bearings under different working conditions. IEEE Transactions on Industrial Electronics, 67(2), 1262-1272.
10. Deutsch, J., & He, D. (2018). A deep learning approach for remaining useful life prediction based on machine condition data. Journal of Manufacturing Science and Engineering, 140
11. Mao, W., He, J., & Zuo, M. J. (2020). A novel deep learning approach for remaining useful life prediction of bearings based on health state similarity. IEEE Transactions on Industrial Electronics, 67(11), 9714-9724.
12. Wang, Y., Ma, X., & Li, Y. F. (2018). A quantum-weighted gated recurrent unit network for remaining useful life prediction of bearings. IEEE Transactions on Industrial Informatics, 15(4), 2410-242
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Creative Commons License
International Journal of Maritime Technology is licensed under a

Creative Commons Attribution-NonCommercial 4.0 International License.