PMID- 37300061
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230613
LR  - 20230613
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 11
DP  - 2023 Jun 5
TI  - Research on Rolling Bearing Fault Diagnosis Based on Digital Twin Data and 
      Improved ConvNext.
LID - 10.3390/s23115334 [doi]
LID - 5334
AB  - This article introduces a novel framework for diagnosing faults in rolling 
      bearings. The framework combines digital twin data, transfer learning theory, and 
      an enhanced ConvNext deep learning network model. Its purpose is to address the 
      challenges posed by the limited actual fault data density and inadequate result 
      accuracy in existing research on the detection of rolling bearing faults in 
      rotating mechanical equipment. To begin with, the operational rolling bearing is 
      represented in the digital realm through the utilization of a digital twin model. 
      The simulation data produced by this twin model replace traditional experimental 
      data, effectively creating a substantial volume of well-balanced simulated 
      datasets. Next, improvements are made to the ConvNext network by incorporating an 
      unparameterized attention module called the Similarity Attention Module (SimAM) 
      and an efficient channel attention feature referred to as the Efficient Channel 
      Attention Network (ECA). These enhancements serve to augment the network's 
      capability for extracting features. Subsequently, the enhanced network model is 
      trained using the source domain dataset. Simultaneously, the trained model is 
      transferred to the target domain bearing using transfer learning techniques. This 
      transfer learning process enables the accurate fault diagnosis of the main 
      bearing to be achieved. Finally, the proposed method's feasibility is validated, 
      and a comparative analysis is conducted in comparison with similar approaches. 
      The comparative study demonstrates that the proposed method effectively addresses 
      the issue of low mechanical equipment fault data density, leading to improved 
      accuracy in fault detection and classification, along with a certain level of 
      robustness.
FAU - Zhang, Chao
AU  - Zhang C
AD  - College of Mechanical Engineering, Inner Mongolia University of Science and 
      Technology, Baotou 014010, China.
AD  - Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of 
      Electromechanical Systems, Baotou 014010, China.
FAU - Qin, Feifan
AU  - Qin F
AUID- ORCID: 0009-0003-8877-5811
AD  - College of Mechanical Engineering, Inner Mongolia University of Science and 
      Technology, Baotou 014010, China.
AD  - Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of 
      Electromechanical Systems, Baotou 014010, China.
FAU - Zhao, Wentao
AU  - Zhao W
AD  - College of Mechanical Engineering, Inner Mongolia University of Science and 
      Technology, Baotou 014010, China.
AD  - Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of 
      Electromechanical Systems, Baotou 014010, China.
FAU - Li, Jianjun
AU  - Li J
AD  - Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of 
      Electromechanical Systems, Baotou 014010, China.
AD  - College of Information Engineering, Inner Mongolia University of Science and 
      Technology, Baotou 014010, China.
FAU - Liu, Tongtong
AU  - Liu T
AD  - College of Mechanical Engineering, Inner Mongolia University of Science and 
      Technology, Baotou 014010, China.
AD  - Inner Mongolia Key Laboratory for Intelligent Diagnosis and Control of 
      Electromechanical Systems, Baotou 014010, China.
LA  - eng
GR  - 51965052/National Natural Science Foundation of China/
GR  - 51865045/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20230605
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10256063
OTO - NOTNLM
OT  - ConvNext
OT  - digital twin
OT  - fault detection
OT  - rolling bearing
OT  - transfer learning
COIS- The authors declare no conflict of interest.
EDAT- 2023/06/10 15:13
MHDA- 2023/06/12 06:42
PMCR- 2023/06/05
CRDT- 2023/06/10 01:22
PHST- 2023/04/17 00:00 [received]
PHST- 2023/05/25 00:00 [revised]
PHST- 2023/05/31 00:00 [accepted]
PHST- 2023/06/12 06:42 [medline]
PHST- 2023/06/10 15:13 [pubmed]
PHST- 2023/06/10 01:22 [entrez]
PHST- 2023/06/05 00:00 [pmc-release]
AID - s23115334 [pii]
AID - sensors-23-05334 [pii]
AID - 10.3390/s23115334 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Jun 5;23(11):5334. doi: 10.3390/s23115334.