The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

1853 Ogledov
992 Prenosov
Izvoz citacije: ABNT
WANG, Haotian ;SUN, Jian ;DUAN, Xiusheng ;SHAN, Ganlin ;YANG, Wen .
The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 64, n.1, p. 17-25, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/the-application-of-lcs-and-information-entropy-as-a-novel-fusion-algorithm-for-degradation-feature-extraction/>. Date accessed: 14 aug. 2020. 
doi:http://dx.doi.org/10.5545/sv-jme.2017.4671.
Wang, H., Sun, J., Duan, X., Shan, G., & Yang, W.
(2018).
The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction.
Strojniški vestnik - Journal of Mechanical Engineering, 64(1), 17-25.
doi:http://dx.doi.org/10.5545/sv-jme.2017.4671
@article{sv-jmesv-jme.2017.4671,
	author = {Haotian  Wang and Jian  Sun and Xiusheng  Duan and Ganlin  Shan and Wen  Yang},
	title = {The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {64},
	number = {1},
	year = {2018},
	keywords = {degradation feature extraction; information fusion; LCS; information entropy; hydraulic pump},
	abstract = {Feature extraction is significant for the prognostics and health management (PHM) of hydraulic pumps. In this paper, a novel fusion algorithm is proposed based on local characteristic-scale decomposition (LCD), composite spectrum (LCS). and information entropy. To make full use of feature information, the LCS is proposed based on the modification of traditional composite spectral algorithm. LCS high-order power entropy and high-order singular entropy, which are relatively defined in Shannon entropy (SE) and Tsallis entropy (TE), are extracted as initial features. Furthermore, the method of feature fusion is presented to modify the features’ conciseness and to improve the performance. Results of the analysis in the experiment indicate that the proposed method is available, and the fused feature is effective in evaluating the pump degradation process.},
	issn = {0039-2480},	pages = {17-25},	doi = {10.5545/sv-jme.2017.4671},
	url = {https://www.sv-jme.eu/sl/article/the-application-of-lcs-and-information-entropy-as-a-novel-fusion-algorithm-for-degradation-feature-extraction/}
}
Wang, H.,Sun, J.,Duan, X.,Shan, G.,Yang, W.
2018 June 64. The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 64:1
%A Wang, Haotian 
%A Sun, Jian 
%A Duan, Xiusheng 
%A Shan, Ganlin 
%A Yang, Wen 
%D 2018
%T The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction
%B 2018
%9 degradation feature extraction; information fusion; LCS; information entropy; hydraulic pump
%! The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction
%K degradation feature extraction; information fusion; LCS; information entropy; hydraulic pump
%X Feature extraction is significant for the prognostics and health management (PHM) of hydraulic pumps. In this paper, a novel fusion algorithm is proposed based on local characteristic-scale decomposition (LCD), composite spectrum (LCS). and information entropy. To make full use of feature information, the LCS is proposed based on the modification of traditional composite spectral algorithm. LCS high-order power entropy and high-order singular entropy, which are relatively defined in Shannon entropy (SE) and Tsallis entropy (TE), are extracted as initial features. Furthermore, the method of feature fusion is presented to modify the features’ conciseness and to improve the performance. Results of the analysis in the experiment indicate that the proposed method is available, and the fused feature is effective in evaluating the pump degradation process.
%U https://www.sv-jme.eu/sl/article/the-application-of-lcs-and-information-entropy-as-a-novel-fusion-algorithm-for-degradation-feature-extraction/
%0 Journal Article
%R 10.5545/sv-jme.2017.4671
%& 17
%P 9
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 64
%N 1
%@ 0039-2480
%8 2018-06-26
%7 2018-06-26
Wang, Haotian, Jian  Sun, Xiusheng  Duan, Ganlin  Shan, & Wen  Yang.
"The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction." Strojniški vestnik - Journal of Mechanical Engineering [Online], 64.1 (2018): 17-25. Web.  14 Aug. 2020
TY  - JOUR
AU  - Wang, Haotian 
AU  - Sun, Jian 
AU  - Duan, Xiusheng 
AU  - Shan, Ganlin 
AU  - Yang, Wen 
PY  - 2018
TI  - The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2017.4671
KW  - degradation feature extraction; information fusion; LCS; information entropy; hydraulic pump
N2  - Feature extraction is significant for the prognostics and health management (PHM) of hydraulic pumps. In this paper, a novel fusion algorithm is proposed based on local characteristic-scale decomposition (LCD), composite spectrum (LCS). and information entropy. To make full use of feature information, the LCS is proposed based on the modification of traditional composite spectral algorithm. LCS high-order power entropy and high-order singular entropy, which are relatively defined in Shannon entropy (SE) and Tsallis entropy (TE), are extracted as initial features. Furthermore, the method of feature fusion is presented to modify the features’ conciseness and to improve the performance. Results of the analysis in the experiment indicate that the proposed method is available, and the fused feature is effective in evaluating the pump degradation process.
UR  - https://www.sv-jme.eu/sl/article/the-application-of-lcs-and-information-entropy-as-a-novel-fusion-algorithm-for-degradation-feature-extraction/
@article{{sv-jme}{sv-jme.2017.4671},
	author = {Wang, H., Sun, J., Duan, X., Shan, G., Yang, W.},
	title = {The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {64},
	number = {1},
	year = {2018},
	doi = {10.5545/sv-jme.2017.4671},
	url = {https://www.sv-jme.eu/sl/article/the-application-of-lcs-and-information-entropy-as-a-novel-fusion-algorithm-for-degradation-feature-extraction/}
}
TY  - JOUR
AU  - Wang, Haotian 
AU  - Sun, Jian 
AU  - Duan, Xiusheng 
AU  - Shan, Ganlin 
AU  - Yang, Wen 
PY  - 2018/06/26
TI  - The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 64, No 1 (2018): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2017.4671
KW  - degradation feature extraction, information fusion, LCS, information entropy, hydraulic pump
N2  - Feature extraction is significant for the prognostics and health management (PHM) of hydraulic pumps. In this paper, a novel fusion algorithm is proposed based on local characteristic-scale decomposition (LCD), composite spectrum (LCS). and information entropy. To make full use of feature information, the LCS is proposed based on the modification of traditional composite spectral algorithm. LCS high-order power entropy and high-order singular entropy, which are relatively defined in Shannon entropy (SE) and Tsallis entropy (TE), are extracted as initial features. Furthermore, the method of feature fusion is presented to modify the features’ conciseness and to improve the performance. Results of the analysis in the experiment indicate that the proposed method is available, and the fused feature is effective in evaluating the pump degradation process.
UR  - https://www.sv-jme.eu/sl/article/the-application-of-lcs-and-information-entropy-as-a-novel-fusion-algorithm-for-degradation-feature-extraction/
Wang, Haotian, Sun, Jian, Duan, Xiusheng, Shan, Ganlin, AND Yang, Wen.
"The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 64 Number 1 (26 June 2018)

Avtorji

Inštitucije

  • Mechanical Engineering College, China 1
  • Luoyang Electronic Equipment Test Center of China, China 2

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 64(2018)1, 17-25

https://doi.org/10.5545/sv-jme.2017.4671

Feature extraction is significant for the prognostics and health management (PHM) of hydraulic pumps. In this paper, a novel fusion algorithm is proposed based on local characteristic-scale decomposition (LCD), composite spectrum (LCS). and information entropy. To make full use of feature information, the LCS is proposed based on the modification of traditional composite spectral algorithm. LCS high-order power entropy and high-order singular entropy, which are relatively defined in Shannon entropy (SE) and Tsallis entropy (TE), are extracted as initial features. Furthermore, the method of feature fusion is presented to modify the features’ conciseness and to improve the performance. Results of the analysis in the experiment indicate that the proposed method is available, and the fused feature is effective in evaluating the pump degradation process.

degradation feature extraction; information fusion; LCS; information entropy; hydraulic pump