OmniFuser: Adaptive Multimodal Fusion for Service-Oriented Predictive Maintenance
Ziqi Wang, Hailiang Zhao, Yuhao Yang, Daojiang Hu, Cheng Bao, Mingyi Liu, Kai Di, Schahram Dustdar, Zhongjie Wang, Shuiguang Deng

TL;DR
OmniFuser is a novel multimodal learning framework that combines visual and sensor data with advanced fusion techniques to improve predictive maintenance of milling tools, enhancing reliability and service-oriented manufacturing.
Contribution
The paper introduces OmniFuser, a new adaptive multimodal fusion framework with contamination-free cross-modal interaction and recursive refinement, specifically designed for tool condition prediction in manufacturing.
Findings
Outperforms state-of-the-art baselines on real-world datasets
Effectively captures complementary features from images and signals
Supports both classification and forecasting tasks
Abstract
Accurate and timely prediction of tool conditions is critical for intelligent manufacturing systems, where unplanned tool failures can lead to quality degradation and production downtime. In modern industrial environments, predictive maintenance is increasingly implemented as an intelligent service that integrates sensing, analysis, and decision support across production processes. To meet the demand for reliable and service-oriented operation, we present OmniFuser, a multimodal learning framework for predictive maintenance of milling tools that leverages both visual and sensor data. It performs parallel feature extraction from high-resolution tool images and cutting-force signals, capturing complementary spatiotemporal patterns across modalities. To effectively integrate heterogeneous features, OmniFuser employs a contamination-free cross-modal fusion mechanism that disentangles shared…
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Taxonomy
TopicsRobot Manipulation and Learning · Digital Transformation in Industry · Advanced machining processes and optimization
