Functional Subspace Variational Autoencoder for Domain-Adaptive Fault Diagnosis

Tan Li (), Che-Heng Fung, Him-Ting Wong, Tak-Lam Chan and Haibo Hu
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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

Mathematics, 2023, vol. 11, issue 13, 1-18

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.

Keywords: functional data analysis; variational autoencoder; domain adaptation; reliability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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