Next Article in Journal
Tsirelson’s Bound Prohibits Communication through a Disconnected Channel
Previous Article in Journal
Big Data Blind Separation
Open AccessArticle

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

by Zhang Dang 1,2,3, Yong Lv 1,2,*, Yourong Li 1,2 and Cancan Yi 1,2
1
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
2
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
3
National Demonstration Center for Experimental Mechanical Education, Wuhan University of Science and Technology, Wuhan 430081, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(3), 152; https://doi.org/10.3390/e20030152
Received: 22 January 2018 / Revised: 12 February 2018 / Accepted: 23 February 2018 / Published: 27 February 2018
(This article belongs to the Section Complexity)
Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes. Then, multiscale permutation entropy calculation is performed to calculate the complexity of each DMD mode. Modes corresponding to the noise components are discarded by threshold technology, and we reconstruct the modes whose entropies are smaller than a threshold to recover the signal. By applying the algorithm to rolling bearing simulation signals and comparing with the result of wavelet transform, the effectiveness of the proposed method can be verified. Finally, the proposed method is applied to the experimental rolling bearing signals. Results demonstrated that the proposed approach has a good application prospect in noise reduction and fault feature extraction. View Full-Text
Keywords: dynamic mode decomposition; sparse optimization; non-convex regularization; multiscale permutation entropy; feature extraction dynamic mode decomposition; sparse optimization; non-convex regularization; multiscale permutation entropy; feature extraction
Show Figures

Figure 1

MDPI and ACS Style

Dang, Z.; Lv, Y.; Li, Y.; Yi, C. Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy. Entropy 2018, 20, 152.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop