PMID- 34828071
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20211129
IS  - 1099-4300 (Electronic)
IS  - 1099-4300 (Linking)
VI  - 23
IP  - 11
DP  - 2021 Oct 20
TI  - Application of Generalized Composite Multiscale Lempel-Ziv Complexity in 
      Identifying Wind Turbine Gearbox Faults.
LID - 10.3390/e23111372 [doi]
LID - 1372
AB  - Wind turbine gearboxes operate in harsh environments; therefore, the resulting 
      gear vibration signal has characteristics of strong nonlinearity, is 
      non-stationary, and has a low signal-to-noise ratio, which indicates that it is 
      difficult to identify wind turbine gearbox faults effectively by the traditional 
      methods. To solve this problem, this paper proposes a new fault diagnosis method 
      for wind turbine gearboxes based on generalized composite multiscale Lempel-Ziv 
      complexity (GCMLZC). Within the proposed method, an effective technique named 
      multiscale morphological-hat convolution operator (MHCO) is firstly presented to 
      remove the noise interference information of the original gear vibration signal. 
      Then, the GCMLZC of the filtered signal was calculated to extract gear fault 
      features. Finally, the extracted fault features were input into softmax 
      classifier for automatically identifying different health conditions of wind 
      turbine gearboxes. The effectiveness of the proposed method was validated by the 
      experimental and engineering data analysis. The results of the analysis indicate 
      that the proposed method can identify accurately different gear health 
      conditions. Moreover, the identification accuracy of the proposed method is 
      higher than that of traditional multiscale Lempel-Ziv complexity (MLZC) and 
      several representative multiscale entropies (e.g., multiscale dispersion entropy 
      (MDE), multiscale permutation entropy (MPE) and multiscale sample entropy (MSE)).
FAU - Yan, Xiaoan
AU  - Yan X
AD  - School of Mechatronics Engineering, Nanjing Forestry University, Nanjing 210037, 
      China.
FAU - She, Daoming
AU  - She D
AD  - School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China.
FAU - Xu, Yadong
AU  - Xu Y
AUID- ORCID: 0000-0003-0713-4434
AD  - School of Mechanical Engineering, Southeast University, Nanjing 211189, China.
FAU - Jia, Minping
AU  - Jia M
AD  - School of Mechanical Engineering, Southeast University, Nanjing 211189, China.
LA  - eng
GR  - 52005265/National Natural Science Foundation of China/
GR  - 20KJB460002/Natural Science Fund for Colleges and Universities in Jiangsu 
      Province/
GR  - 163040095 and 163040117/Scientific Research Foundation of Nanjing Forestry 
      University/
GR  - BE2019030637/Jiangsu Provincial Key Research and Development Program/
GR  - AM2021002/Macau Young Scholars Program/
PT  - Journal Article
DEP - 20211020
PL  - Switzerland
TA  - Entropy (Basel)
JT  - Entropy (Basel, Switzerland)
JID - 101243874
PMC - PMC8625407
OTO - NOTNLM
OT  - fault diagnosis
OT  - morphological filtering
OT  - multiscale Lempel-Ziv complexity
OT  - softmax
OT  - wind turbine gearbox
COIS- The authors declare no conflict of interest.
EDAT- 2021/11/28 06:00
MHDA- 2021/11/28 06:01
PMCR- 2021/10/20
CRDT- 2021/11/27 01:05
PHST- 2021/08/18 00:00 [received]
PHST- 2021/10/15 00:00 [revised]
PHST- 2021/10/18 00:00 [accepted]
PHST- 2021/11/27 01:05 [entrez]
PHST- 2021/11/28 06:00 [pubmed]
PHST- 2021/11/28 06:01 [medline]
PHST- 2021/10/20 00:00 [pmc-release]
AID - e23111372 [pii]
AID - entropy-23-01372 [pii]
AID - 10.3390/e23111372 [doi]
PST - epublish
SO  - Entropy (Basel). 2021 Oct 20;23(11):1372. doi: 10.3390/e23111372.