AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines

TL;DR

AssemAI introduces an interpretable, image-based anomaly detection system tailored for manufacturing pipelines, leveraging domain knowledge, advanced models, and explainability techniques to improve reliability and efficiency in smart manufacturing.

Contribution

The paper presents a new image dataset, fine-tunes YOLO-FF, develops custom anomaly detection models, and integrates explainability methods for manufacturing pipeline anomaly detection.

Findings

Best model achieved high accuracy in detecting anomalies.

Explainability techniques provided clear insights into model decisions.

Real-time deployment demonstrated practical applicability.

Abstract

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.