mlsanityData to 14 Oct 2026

Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data

arXiv 2106.04920 ↗First posted 9 Jun 2021cs.LG cs.AIcited by 4

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Abstract (arXiv)

Deep learning promises performant anomaly detection on time-variant datasets, but greatly suffers from low availability of suitable training datasets and frequently changing tasks. Deep transfer learning offers mitigation by letting algorithms built upon previous knowledge from different tasks or locations. In this article, a modular deep learning algorithm for anomaly detection on time series datasets is presented that allows for an easy integration of such transfer learning capabilities. It is thoroughly tested on a dataset from a discrete manufacturing process in order to prove its fundamental adequacy towards deep industrial transfer learning - the transfer of knowledge in industrial applications' special environment.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 9 Jun 2021.

Builds on

10 of 33 references.

References · 10

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  10. 2021Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturingcited by 19

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.

4 papers.

  1. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  2. 2023Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontologycited by 13
  3. 2025NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelinescited by 3
  4. 2022Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selectioncited by 0