Predictive Maintenance Based on Deep Learning: Early Identification of Failures in Heavy Machinery Components

Deep learning-based predictive maintenance is a key strategy in industry to prevent unexpected failures, reduce downtime, and improve operational safety. This study presents an advanced approach for early fault detection in heavy machinery components using image analysis, focusing on four critical defect types: hose wear, piston failure, corrosion, and moisture. To this end, three state-of-the-art object detection models were implemented and compared: YOLOv11, RT-DETR, and YOLO-World. The dataset consists of images captured in real-life industrial environments exhibiting variations in lighting, texture, and material degradation. A manual preprocessing and annotation process was applied to improve training quality. Model performance was evaluated using key metrics such as the precision-recall (PR) curve and the confusion matrix to determine the most efficient technique for real-time fault detection. Experimental results show that YOLOv11 achieves the highest overall accuracy, with an mAP@0.5 of 83.8%, followed by YOLO-World at 82.4% and RT-DETR at 80.3%. In terms of efficiency, YOLO-World offers a balance between accuracy and detection speed, while RT-DETR shows stable performance but lower accuracy for certain defect types. These findings confirm that deep learning-based detection models enable the rapid and accurate identification of industrial defects, facilitating the implementation of predictive maintenance strategies.

@article{Melgar2025,
  title     = {Predictive Maintenance Based on Deep Learning: Early Identification of Failures in Heavy Machinery Components},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Pablo Cabrera Melgar and Luis Hilasaca Chambi and Raul Sulla Torres},
  doi       = {10.14569/IJACSA.2025.0160577},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160577}
}