OmniFuser: Adaptive Multimodal Fusion for Service-Oriented Predictive Maintenance

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 and modality-specific components, allowing for efficient cross-modal interaction. Furthermore, a recursive refinement pathway functions as an anchor mechanism, consistently retaining residual information to stabilize fusion dynamics. The learned representations can be encapsulated as reusable maintenance service modules, supporting both tool-state classification (e.g., Sharp, Used, Dulled) and multi-step force signal forecasting. Experiments on real-world milling datasets demonstrate that OmniFuser consistently outperforms state-of-the-art baselines, providing a dependable foundation for building intelligent industrial maintenance services.