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Synergy of Engineering and Statistics: Multimodal Data Fusion for Quality Improvement

Author

Listed:
  • Jianjun Shi

    (Georgia Institute of Technology)

  • Michael Biehler

    (Georgia Institute of Technology)

  • Shancong Mou

    (Georgia Institute of Technology)

Abstract

This chapter outlines the synergies achieved through the fusion of engineering and statistical approaches for quality improvement. It emphasizes the integration of data science and system theory, leveraging in-process sensing data for comprehensive process monitoring, diagnosis, and control. Multimodal data fusion is a key strategy for quality improvement, leading to root cause diagnosis, automatic compensation, and defect prevention. This approach goes beyond traditional aspects, such as change detection, off-line adjustment, and defect inspection. The chapter provides a concise overview of multimodal data fusion, highlights its recent developments and applications in data fusion for structured and unstructured high-dimensional data, and outlines challenges and opportunities in contemporary data-rich systems. Additionally, it explores future research directions, with a specific emphasis on harnessing emerging machine learning tools to enhance quality in systems with rich sensing data.

Suggested Citation

  • Jianjun Shi & Michael Biehler & Shancong Mou, 2024. "Synergy of Engineering and Statistics: Multimodal Data Fusion for Quality Improvement," Springer Optimization and Its Applications,, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-53092-0_12
    DOI: 10.1007/978-3-031-53092-0_12
    as

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