Stop Using Convolutional Neural Networks: Knowledge Distillation for an Interpretable and Lightweight Decision Tree in Rod Pump Working Condition Diagnosis

Abstract Recent studies on rod pump working condition diagnosis have heavily relied on convolutional neural networks (CNN) for dynamometer card classification, largely due to CNNs’ success in extensive image recognition tasks. However, these models often depend on over-parameterized architectures with millions or even billions of parameters, making them unsuitable for deployment on edge devices or embedded systems with limited computational resources and energy budgets. Their large size also results in slow prediction speed, hindering real-time fault detection in field operations. Furthermore, the black-box nature of CNNs compromises interpretability, making it difficult for field operators to understand and trust the predictions. Given these limitations, decision trees present an appealing alternative due to their lightweight structure, inherent interpretability, and ease of use. Yet traditional decision trees, such as CART, suffer from a significant accuracy gap compared to CNN, particularly in image classification tasks without effective feature extraction, and thus are rarely used. This work addresses the issue by first significantly improving the accuracy of traditional decision trees through a novel tree reformulation and gradient-based entire tree optimization, avoiding the suboptimal tree model induced by the traditional greedy optimization. Built upon our optimized decision tree, we subsequently leverage knowledge distillation to further boost its image classification accuracy, allowing the tree to inherit the knowledge or learning capability of a well-trained CNN model. Experiments on two representative dynamometer card datasets demonstrate the effectiveness of our approach. Our decision tree achieves 5.32% higher accuracy than traditional CART, and knowledge distillation brings an additional 4% improvement on average, reaching accuracy comparable to CNN-based models such as ResNet-50 and Vision Transformer (ViT). Moreover, our tree model uses only 64 IF-THEN rules to connect input images with final predictions in contrast to 4,096 rules in CART, thus ensuring interpretability and transparency. Our tree model contains only 49,457 parameters, significantly fewer than 24 million in ResNet-50 and 86 million in ViT, and achieves at least a 13,000 times speedup in prediction time over ResNet and 33,000 times over ViT. This optimal balance of competitive accuracy, lightweight structure and interpretability, combined with our open-source code, makes our method a practical and reliable alternative to heavy black-box CNN classifiers for real-time deployment in dynamometer cards as well as other image classification tasks.