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A Survey on Knowledge Transfer for Manufacturing Data Analytics.

Seung Hwan Bang1,2, Ronay Ak1, Anantha Narayanan3

  • 1Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD, 20899, USA.

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|October 23, 2024
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Summary
This summary is machine-generated.

Manufacturing data analytics models degrade over time due to non-stationary environments. Knowledge transfer (KT) methods can adapt models to new conditions, maintaining accuracy and reducing retraining needs.

Keywords:
Data analyticsKnowledge transferManufacturingNon-stationary environments

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Area of Science:

  • Manufacturing Data Analytics
  • Machine Learning Operations (MLOps)

Background:

  • Data analytics models assume stationary environments, which is often false in manufacturing.
  • Model accuracy degrades over time in non-stationary manufacturing environments, necessitating periodic retraining.

Purpose of the Study:

  • To survey knowledge transfer (KT) methods applicable to manufacturing data analytics.
  • To investigate the suitability of KT for adapting models to non-stationary manufacturing environments.
  • To provide a mechanism for selecting appropriate KT methods in manufacturing.

Main Methods:

  • Surveying existing knowledge transfer methods across various applications.
  • Analyzing KT methods based on non-stationary environment types, data availability, and knowledge sources.
  • Categorizing causes of non-stationarity in manufacturing.
  • Developing a three-step mechanism (change detection, source-target definition, method selection) for practitioners.

Main Results:

  • Identified and analyzed various knowledge transfer methods.
  • Categorized manufacturing-specific non-stationary events.
  • Proposed a practical mechanism to guide the selection of KT methods for manufacturing data analytics.

Conclusions:

  • Knowledge transfer is a viable approach to mitigate accuracy degradation in manufacturing data analytics.
  • The proposed mechanism supports practitioners in choosing suitable KT methods for their specific manufacturing challenges.
  • This work facilitates the adoption of knowledge transfer in manufacturing research and practice.