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A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends.

Athina Tsanousa1, Evangelos Bektsis1, Constantine Kyriakopoulos1

  • 1Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece.

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Summary

Industry 4.0 smart manufacturing relies on multisensor data fusion for improved monitoring. This review guides data integration for better manufacturing prognosis, identifying research gaps.

Keywords:
data fusionfeature extractionindustrial prognosissmart manufacturing

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Area of Science:

  • Industrial Engineering
  • Data Science
  • Manufacturing Technology

Background:

  • Industry 4.0 revolution drives manufacturing companies towards 'smarter' operations.
  • Multisensor systems are crucial for industrial monitoring, enhancing processes, reducing costs, and increasing safety.
  • Heterogeneous data from various sensors pose challenges in data management and analysis.

Purpose of the Study:

  • To provide a comprehensive review of state-of-the-art data fusion solutions for manufacturing prognosis.
  • To guide early stages of analytic pipelines by covering data storage, feature engineering, and multimodal integration.
  • To identify existing weaknesses and research gaps in current data fusion methodologies for smart manufacturing.

Main Methods:

  • Detailed literature review of data fusion techniques in manufacturing.
  • Analysis of solutions for data storage and indexing from diverse sensors.
  • Examination of feature engineering and multimodal data integration strategies.

Main Results:

  • The reviewed literature indicates advanced, state-of-the-art methods are being applied in data fusion and preprocessing within the manufacturing sector.
  • Identified specific techniques for handling heterogeneous multisensor data.
  • Highlighted areas where current approaches fall short, indicating potential for innovation.

Conclusions:

  • Current data fusion and preprocessing methods in manufacturing are sophisticated.
  • Gaps in the literature suggest avenues for future research to further optimize manufacturing prognosis.
  • The review serves as a foundational guide for developing advanced analytic pipelines.