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: 20 dec. 2024. 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. 20 Dec. 2024
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)
Strojniški vestnik - Journal of Mechanical Engineering 64(2018)1, 17-25
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.