PMID- 35632056
OWN - NLM
STAT- MEDLINE
DCOM- 20220531
LR  - 20220716
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 10
DP  - 2022 May 10
TI  - Multi-Sensor Fusion by CWT-PARAFAC-IPSO-SVM for Intelligent Mechanical Fault 
      Diagnosis.
LID - 10.3390/s22103647 [doi]
LID - 3647
AB  - A new method of multi-sensor signal analysis for fault diagnosis of centrifugal 
      pump based on parallel factor analysis (PARAFAC) and support vector machine (SVM) 
      is proposed. The single-channel vibration signal is analyzed by Continuous 
      Wavelet Transform (CWT) to construct the time-frequency representation. The 
      multiple time-frequency data are used to construct the three-dimension data 
      matrix. The 3-level PARAFAC method is proposed to decompose the data matrix to 
      obtain the six features, which are the time domain signal (mode 3) and frequency 
      domain signal (mode 2) of each level within the three-level PARAFAC. The eighteen 
      features from three direction vibration signals are used to test the data 
      processing capability of the algorithm models by the comparison among the 
      CWT-PARAFAC-IPSO-SVM, WPA-PSO-SVM, WPA-IPSO-SVM, and CWT-PARAFAC-PSO-SVM. The 
      results show that the multi-channel three-level data decomposition with PARAFAC 
      has better performance than WPT. The improved particle swarm optimization (IPSO) 
      has a great improvement in the complexity of the optimization structure and 
      running time compared to the conventional particle swarm optimization (PSO.) It 
      verifies that the proposed CWT-PARAFAC-IPSO-SVM is the most optimal hybrid 
      algorithm. Further, it is characteristic of its robust and reliable superiority 
      to process the multiple sources of big data in continuous condition monitoring in 
      the large-scale mechanical system.
FAU - Chen, Hanxin
AU  - Chen H
AD  - School of Artificial Intelligence, Nanchang Institute of Science and Technology, 
      Nanchang 330108, China.
AD  - School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, 
      Wuhan 430074, China.
FAU - Li, Shaoyi
AU  - Li S
AD  - School of Artificial Intelligence, Nanchang Institute of Science and Technology, 
      Nanchang 330108, China.
AD  - School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, 
      Wuhan 430074, China.
LA  - eng
GR  - 51775390/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20220510
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Algorithms
MH  - Factor Analysis, Statistical
MH  - *Support Vector Machine
MH  - *Wavelet Analysis
PMC - PMC9147989
OTO - NOTNLM
OT  - IPSO
OT  - PARAFAC
OT  - SVM
OT  - fault diagnosis
OT  - slurry pump
COIS- The authors declare no conflict of interest.
EDAT- 2022/05/29 06:00
MHDA- 2022/06/01 06:00
PMCR- 2022/05/10
CRDT- 2022/05/28 01:41
PHST- 2022/03/08 00:00 [received]
PHST- 2022/04/12 00:00 [revised]
PHST- 2022/04/19 00:00 [accepted]
PHST- 2022/05/28 01:41 [entrez]
PHST- 2022/05/29 06:00 [pubmed]
PHST- 2022/06/01 06:00 [medline]
PHST- 2022/05/10 00:00 [pmc-release]
AID - s22103647 [pii]
AID - sensors-22-03647 [pii]
AID - 10.3390/s22103647 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 May 10;22(10):3647. doi: 10.3390/s22103647.