en Main Engine & Electrical Equipments مقاله پژوهشي Research Paper <table class="MsoTableGrid" style="margin-left:7px; border-collapse:collapse; border:none"> <tbody> <tr> <td style="border-bottom:1px solid black; width:463px; padding:6px 0cm 6px 0cm; border-top:1px solid black; border-right:none; border-left:none" valign="top"><span style="font-size:12pt"><span new="" roman="" style="font-family:" times=""><span style="color:black">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.</span></span></span></td> </tr> </tbody> </table> Remaining Life Prediction,Failure Process Modeling,Neural Networks,Artificial Intelligence,Data-Driven Approach. 61 69 http://ijmt.ir/browse.php?a_code=A-10-7863-2&slc_lang=en&sid=1 Mohammadali Zarghami Mohamad.zarghami@gmail.com 10031947532846004245 10031947532846004245 No South Tehran Azad University Sadigh Raissi Raissi@azad.ac.ir 10031947532846004246 10031947532846004246 Yes South Tehran Azad University tohidi hamid htohidi1342@gmail.com 10031947532846004247 10031947532846004247 No South Tehran Azad University Shahrooz Bamdad sh.bamdad2000@yahoo.com 10031947532846004248 10031947532846004248 No South Tehran Azad University