A Unified Framework for Multimodal Data Fusion in Predictive Maintenance Applications Across Manufacturing Systems

Authors

Kehmar Nyan
Manufacturing Data Scientist, Myanmar.
Susie Yamin Davamoe
Predictive Analytics Engineer, Myanmar.

Keywords:

Predictive Maintenance, Multimodal Data Fusion, Manufacturing Systems, Machine Learning, Sensor Integration

Synopsis

Predictive maintenance increasingly leverages multimodal data for early fault detection and lifecycle estimation of manufacturing equipment. This paper proposes a unified data-fusion framework that integrates sensor, image, and operational log data to enhance predictive maintenance across heterogeneous manufacturing environments. We demonstrate the framework’s architecture, data preprocessing standards, fusion strategies, and performance outcomes on simulated datasets. Results show improved prediction accuracy and fault detection lead time compared to single-modal methods. The framework supports scalability and adaptability to diverse manufacturing systems.

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IJIT

Published

March 10, 2022