Efficient Failure Prediction: A Transfer Learning-Based Solution for Imbalanced Data Classification

Industrial predictive maintenance at the edge faces persistent challenges such as extreme class imbalance, limited labeled failure data, and the need for efficient yet scalable AI models. This paper proposes a transfer learning-based edge AI framework that addresses these challenges through a signal-to-image transformation and fine-tuning of deep residual networks (ResNet). One-dimensional sensor signals are converted into two-dimensional RGB images, enabling the use of powerful convolutional architectures originally trained on large-scale datasets. The approach emulates an edge–cloud synergy, where knowledge distilled from large pre-trained models is efficiently adapted and executed on resource-constrained edge environments. Trained on less than 5% of the original dataset, the model achieves a negative predictive value of 96.53%, significantly reducing classification cost and outperforming both conventional deep learning and traditional machine learning methods. The results demonstrate that transfer learning-driven edge intelligence offers a cost-effective, scalable, and generalizable solution for predictive maintenance and industrial automation under data scarcity.