PMID- 34203708
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210704
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 23
IP  - 6
DP  - 2021 Jun 15
TI  - Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN.
LID - 10.3390/e23060751 [doi]
LID - 751
AB  - This paper proposes a data-driven method-based fault diagnosis method using the 
      deep convolutional neural network (DCNN). The DCNN is used to deal with sensor 
      and actuator faults of robot joints, such as gain error, offset error, and 
      malfunction for both sensors and actuators, and different fault types are 
      diagnosed using the trained neural network. In order to achieve the above goal, 
      the fused data of sensors and actuators are used, where both types of fault are 
      described in one formulation. Then, the deep convolutional neural network is 
      applied to learn characteristic features from the merged data to try to find 
      discriminative information for each kind of fault. After that, the fully 
      connected layer does prediction work based on learned features. In order to 
      verify the effectiveness of the proposed deep convolutional neural network model, 
      different fault diagnosis methods including support vector machine (SVM), 
      artificial neural network (ANN), conventional neural network (CNN) using the 
      LeNet-5 method, and long-term memory network (LTMN) are investigated and compared 
      with DCNN method. The results show that the DCNN fault diagnosis method can 
      realize high fault recognition accuracy while needing less model training time.
FAU - Pan, Jinghui
AU  - Pan J
AD  - School of Automation and Electrical Engineering, University of Science and 
      Technology Beijing, Beijing 100083, China.
FAU - Qu, Lili
AU  - Qu L
AD  - School of Mechatronic Engineering and Automation, Foshan University, Foshan 
      528231, China.
FAU - Peng, Kaixiang
AU  - Peng K
AD  - School of Automation and Electrical Engineering, University of Science and 
      Technology Beijing, Beijing 100083, China.
LA  - eng
GR  - 2019YFB1309900/National Key Research and Development Program of China/
PT  - Journal Article
DEP - 20210615
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC8232324
OTO - NOTNLM
OT  - actuator fault
OT  - deep convolutional neural network
OT  - fault diagnosis
OT  - robot joints
OT  - sensor fault
COIS- The authors declare no conflict of interest.
EDAT- 2021/07/03 06:00
MHDA- 2021/07/03 06:01
PMCR- 2021/06/15
CRDT- 2021/07/02 01:18
PHST- 2021/05/04 00:00 [received]
PHST- 2021/06/05 00:00 [revised]
PHST- 2021/06/08 00:00 [accepted]
PHST- 2021/07/02 01:18 [entrez]
PHST- 2021/07/03 06:00 [pubmed]
PHST- 2021/07/03 06:01 [medline]
PHST- 2021/06/15 00:00 [pmc-release]
AID - e23060751 [pii]
AID - entropy-23-00751 [pii]
AID - 10.3390/e23060751 [doi]
PST - epublish
SO  - Entropy (Basel). 2021 Jun 15;23(6):751. doi: 10.3390/e23060751.