Physics-informed deep learning for signal compression and reconstruction of big data in industrial condition monitoring

Matthew Russell, Peng Wang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The onset of the Internet of Things enables machines to be outfitted with always-on sensors that can provide health information to cloud-based monitoring systems for prognostics and health management (PHM), which greatly improves reliability and avoids downtime of machines and processes on the shop floor. On the other hand, real-time monitoring produces large amounts of data, leading to significant challenges for efficient and effective data transmission (from the shop floor to the cloud) and analysis (in the cloud). Restricted by industrial hardware capability, especially Internet bandwidth, most solutions approach data transmission from the perspective of data compression (before transmission, at local computing devices) coupled with data reconstruction (after transmission, in the cloud). However, existing data compression techniques may not adapt to domain-specific characteristics of data, and hence have limitations in addressing high compression ratios where full restoration of signal details is important for revealing machine conditions. This study integrates Deep Convolutional Autoencoders (DCAE) with local structure and physics-informed loss terms that incorporate PHM domain knowledge such as the importance of frequency content for machine fault diagnosis. Furthermore, Fault Division Autoencoder Multiplexing (FDAM) is proposed to mitigate the negative effects of multiple disjoint operating conditions on reconstruction fidelity. The proposed methods are evaluated on two case studies, and autocorrelation-based noise analysis provides insight into the relative performance across machine health and operating conditions. Results indicate that physically-informed DCAE compression outperforms prevalent data compression approaches, such as compressed sensing, Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), and DCAE with a standard loss function. FDAM can further improve the data reconstruction quality for certain machine conditions.

Original languageEnglish
Article number108709
JournalMechanical Systems and Signal Processing
Volume168
DOIs
StatePublished - Apr 1 2022

Bibliographical note

Funding Information:
This work was supported by National Science Foundation, United States grant number 2015889 . We would thank the University of Kentucky Center for Computation Sciences and Information Technology Services Research Computing for their support and use of the Lipscomb Compute Cluster and associated research computing resources.

Funding Information:
This work was supported by National Science Foundation, United States grant number 2015889. We would thank the University of Kentucky Center for Computation Sciences and Information Technology Services Research Computing for their support and use of the Lipscomb Compute Cluster and associated research computing resources.

Publisher Copyright:
© 2021 Elsevier Ltd

Keywords

  • Big data
  • Data compression
  • Physics-informed deep learning
  • Prognostics and health management

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

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