Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data
Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on...
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/3923 |
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| author | Sheraz Aslam Alejandro Navarro Andreas Aristotelous Eduardo Garro Crevillen Alvaro Martınez-Romero Álvaro Martínez-Ceballos Alessandro Cassera Kyriacos Orphanides Herodotos Herodotou Michalis P. Michaelides |
| author_facet | Sheraz Aslam Alejandro Navarro Andreas Aristotelous Eduardo Garro Crevillen Alvaro Martınez-Romero Álvaro Martínez-Ceballos Alessandro Cassera Kyriacos Orphanides Herodotos Herodotou Michalis P. Michaelides |
| author_sort | Sheraz Aslam |
| collection | DOAJ |
| description | Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score. |
| format | Article |
| id | doaj-art-5e64fd1be3314e97b1b2baa1d532b776 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-5e64fd1be3314e97b1b2baa1d532b7762025-08-20T03:17:08ZengMDPI AGSensors1424-82202025-06-012513392310.3390/s25133923Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor DataSheraz Aslam0Alejandro Navarro1Andreas Aristotelous2Eduardo Garro Crevillen3Alvaro Martınez-Romero4Álvaro Martínez-Ceballos5Alessandro Cassera6Kyriacos Orphanides7Herodotos Herodotou8Michalis P. Michaelides9Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, CyprusProdevelop S.L, 46001 Valencia, SpainDepartment of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, CyprusProdevelop S.L, 46001 Valencia, SpainProdevelop S.L, 46001 Valencia, SpainProdevelop S.L, 46001 Valencia, SpainEurogate Container Terminal Limassol Ltd., Limassol 3045, CyprusEurogate Container Terminal Limassol Ltd., Limassol 3045, CyprusDepartment of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, CyprusDepartment of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, CyprusMaritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score.https://www.mdpi.com/1424-8220/25/13/3923predictive maintenancesmart portsmachine learningIoT |
| spellingShingle | Sheraz Aslam Alejandro Navarro Andreas Aristotelous Eduardo Garro Crevillen Alvaro Martınez-Romero Álvaro Martínez-Ceballos Alessandro Cassera Kyriacos Orphanides Herodotos Herodotou Michalis P. Michaelides Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data Sensors predictive maintenance smart ports machine learning IoT |
| title | Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data |
| title_full | Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data |
| title_fullStr | Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data |
| title_full_unstemmed | Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data |
| title_short | Machine Learning-Based Predictive Maintenance at Smart Ports Using IoT Sensor Data |
| title_sort | machine learning based predictive maintenance at smart ports using iot sensor data |
| topic | predictive maintenance smart ports machine learning IoT |
| url | https://www.mdpi.com/1424-8220/25/13/3923 |
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