Residual-Hypergraph Convolution Network: A Model-Based and Data-Driven Integrated Approach for Fault Diagnosis in Complex Equipment

Liqiao Xia, Yongshi Liang, Pai Zheng (Corresponding Author), Xiao Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

Timely and accurate fault diagnosis plays a critical role in today’s smart manufacturing practices, saving invaluable time and expenditure on maintenance process. To date, numerous data-driven approaches have been introduced for equipment fault diagnosis, and part of them attempt to involve equipment knowledge in their data-driven models. However, those combinations mainly concentrate on feature engineering and superposition of their separate results without considering or leveraging the relationship between equipment knowledge and collecting sensor data. To fill this gap, this research proposes a Residual-hypergraph convolution network (Res-HGCN) approach that holistically embeds equipment’s structure and operational mechanisms as a hypergraph form into data-driven model, considering the reaction among equipment’s components. The generic model-based hypergraph construction framework is first introduced, which represents a synergetic mechanism of complex equipment. Then, a multi-sensory data-driven Res-HGCN approach, combining residual block and HGCN, is presented for fault diagnosis based on pre-defined hypergraph. Lastly, a case study of turbofan engine is conducted and compared with other typical methods to reveal the superiority of the proposed approach. This work establishes the association of different sensing variables through equipment’s structure and operational mechanisms, thus integrating the advantages of model-based and data-driven-based approaches holistically. It is envisioned that this research can provide insightful knowledge for many other model-based and data-driven integrated manufacturing scenarios.

Original languageEnglish
Article number3501811
Number of pages11
JournalIEEE Transactions on Instrumentation and Measurement
Volume72
DOIs
Publication statusPublished - 8 Dec 2022

Keywords

  • Complex equipment
  • Computational modeling
  • Data models
  • Fault diagnosis
  • Graph convolution network
  • Hypergraph
  • Mathematical models
  • Matrix decomposition
  • Modeling
  • Predictive models
  • Smart manufacturing

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Residual-Hypergraph Convolution Network: A Model-Based and Data-Driven Integrated Approach for Fault Diagnosis in Complex Equipment'. Together they form a unique fingerprint.

Cite this