TY - JOUR
T1 - Residual-Hypergraph Convolution Network: A Model-Based and Data-Driven Integrated Approach for Fault Diagnosis in Complex Equipment
AU - Xia, Liqiao
AU - Liang, Yongshi
AU - Zheng, Pai
AU - Huang, Xiao
N1 - This work was supported in part by the Mainland-Hong Kong Joint Funding Scheme under Grant MHX/001/20;
in part by the Innovation and Technology Commission (ITC); in part by the Hong Kong Special Administration Region; in part by the National Key Research and Development Programs of Cooperation on Science and
Technology Innovation with Hong Kong, Macao and Taiwan, Ministry of Science and Technology (MOST), China, under Grant SQ2020YFE020182; and in part by the State Key Laboratory of Ultra-Precision Machining
Technology, The Hong Kong Polytechnic University, HKSAR, China, under Project 1-BBR2.
Publisher Copyright:
IEEE
PY - 2022/12/8
Y1 - 2022/12/8
N2 - 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.
AB - 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.
KW - Complex equipment
KW - Computational modeling
KW - Data models
KW - Fault diagnosis
KW - Graph convolution network
KW - Hypergraph
KW - Mathematical models
KW - Matrix decomposition
KW - Modeling
KW - Predictive models
KW - Smart manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85144782683&partnerID=8YFLogxK
U2 - 10.1109/TIM.2022.3227609
DO - 10.1109/TIM.2022.3227609
M3 - Journal article
AN - SCOPUS:85144782683
SN - 0018-9456
VL - 72
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 3501811
ER -