Anomaly detection in manufacturing pipelines remains a critical challenge, intensified by the complexity and variability of industrial environments. This paper introduces AssemAI, an interpretable image-based anomaly detection system tailored for smart manufacturing pipelines. Utilizing a curated image dataset from an industry-focused rocket assembly pipeline, we address the challenge of imbalanced image data and demonstrate the importance of image-based methods in anomaly detection. Our primary contributions include deriving an image dataset, fine-tuning an object detection model YOLO-FF, and implementing a custom anomaly detection model for assembly pipelines. The proposed approach leverages domain knowledge in data preparation, model development and reasoning. We implement several anomaly detection models on the derived image dataset, including a Convolutional Neural Network, Vision Transformer (ViT), and pre-trained versions of these models. Additionally, we incorporate explainability techniques at both user and model levels, utilizing ontology for user-level explanations and SCORE-CAM for in-depth feature and model analysis. Finally, the best-performing anomaly detection model and YOLO-FF are deployed in a real-time setting. Our results include ablation studies on the baselines and a comprehensive evaluation of the proposed system. This work highlights the broader impact of advanced image-based anomaly detection in enhancing the reliability and efficiency of smart manufacturing processes. The image dataset, codes to reproduce the results and additional experiments are available at https://github.com/renjithk4/AssemAI.

翻译：制造流水线中的异常检测仍然是一个关键挑战，工业环境的复杂性和多变性加剧了这一难题。本文介绍了AssemAI，一种专为智能制造流水线设计的可解释图像异常检测系统。通过利用来自工业级火箭装配流水线的精选图像数据集，我们解决了图像数据不平衡的挑战，并论证了基于图像的异常检测方法的重要性。我们的主要贡献包括：构建图像数据集、微调目标检测模型YOLO-FF，以及为装配流水线实现定制化异常检测模型。所提出的方法在数据准备、模型开发和推理过程中充分融合了领域知识。我们在构建的图像数据集上实现了多种异常检测模型，包括卷积神经网络、视觉Transformer（ViT）及其预训练版本。此外，我们在用户层面和模型层面分别融入了可解释性技术：利用本体论实现用户级解释，采用SCORE-CAM进行深度特征与模型分析。最终，我们将性能最优的异常检测模型与YOLO-FF部署于实时环境中。实验结果包括基线模型的消融研究及对提出系统的全面评估。本工作凸显了先进图像异常检测技术在提升智能制造过程可靠性与效率方面的广泛影响。图像数据集、结果复现代码及补充实验材料可通过https://github.com/renjithk4/AssemAI获取。