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Functional Subspace Variational Autoencoder for Domain-Adaptive Fault Diagnosis

Author

Listed:
  • Tan Li

    (Centre for Advances in Reliability and Safety (CAiRS), Hong Kong, China)

  • Che-Heng Fung

    (Centre for Advances in Reliability and Safety (CAiRS), Hong Kong, China)

  • Him-Ting Wong

    (Centre for Advances in Reliability and Safety (CAiRS), Hong Kong, China)

  • Tak-Lam Chan

    (Centre for Advances in Reliability and Safety (CAiRS), Hong Kong, China)

  • Haibo Hu

    (Centre for Advances in Reliability and Safety (CAiRS), Hong Kong, China
    Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, China)

Abstract

This paper presents the functional subspace variational autoencoder, a technique addressing challenges in sensor data analysis in transportation systems, notably the misalignment of time series data and a lack of labeled data. Our technique converts vectorial data into functional data, which captures continuous temporal dynamics instead of discrete data that consist of separate observations. This conversion reduces data dimensions for machine learning tasks in fault diagnosis and facilitates the efficient removal of misalignment. The variational autoencoder identifies trends and anomalies in the data and employs a domain adaptation method to associate learned representations between labeled and unlabeled datasets. We validate the technique’s effectiveness using synthetic and real-world transportation data, providing valuable insights for transportation infrastructure reliability monitoring.

Suggested Citation

  • Tan Li & Che-Heng Fung & Him-Ting Wong & Tak-Lam Chan & Haibo Hu, 2023. "Functional Subspace Variational Autoencoder for Domain-Adaptive Fault Diagnosis," Mathematics, MDPI, vol. 11(13), pages 1-18, June.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2910-:d:1182193
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    References listed on IDEAS

    as
    1. Xiaoyu Zheng & Dexin Yu & Chen Xie & Zhuorui Wang, 2023. "Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    2. Chin-Shiuh Shieh & Thanh-Tuan Nguyen & Mong-Fong Horng, 2023. "Detection of Unknown DDoS Attack Using Convolutional Neural Networks Featuring Geometrical Metric," Mathematics, MDPI, vol. 11(9), pages 1-24, May.
    Full references (including those not matched with items on IDEAS)

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