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Search Results (3)

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Keywords = IEMD energy entropy

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35 pages, 17290 KB  
Article
A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy
by Peng Liu, Chen Dai, Shuaiqiang Li, Hui Jin, Xinfu Liu and Guijie Liu
J. Mar. Sci. Eng. 2024, 12(12), 2222; https://doi.org/10.3390/jmse12122222 - 4 Dec 2024
Cited by 4 | Viewed by 1820
Abstract
A feature extraction method based on the combination of improved empirical modal decomposition (IEMD) and multi-scale permutation entropy (MPE) is proposed to address the problem of inaccurate recognition and classification of ship noise signals under complex environmental conditions. In order to eliminate the [...] Read more.
A feature extraction method based on the combination of improved empirical modal decomposition (IEMD) and multi-scale permutation entropy (MPE) is proposed to address the problem of inaccurate recognition and classification of ship noise signals under complex environmental conditions. In order to eliminate the end effects, this paper proposes an extended model based on the principle of peak cross-correlation for improved empirical modal decomposition (EMD). In this paper, the IEMD method is used to decompose three ship underwater noise signals to extract the MPE features of the highest order intrinsic modal function (IMF) of energy. The results show that the IEMD-MPE method performs well in extracting the feature information of the signals and has a strong discriminative ability. Compared with the IEMD-aligned entropy (IEMD-PE) method, which describes the signals only at a single scale, the IEMD-MPE method achieves an improvement in the minimum difference distance ranging from 101.36% to 212.98%. In addition, two sets of highly similar ship propulsion noise signals were applied to validate the IEMD-MPE method, and the minimum differences of the experimental results were 0.0814 and 0.0057 entropy units, which verified the validity and generality of the method. This study provides theoretical support for the development of ship target recognition technology for propulsion. Full article
(This article belongs to the Section Ocean Engineering)
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14 pages, 1367 KB  
Article
Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
by Yancai Xiao, Yujia Wang, Huan Mu and Na Kang
Algorithms 2017, 10(2), 67; https://doi.org/10.3390/a10020067 - 10 Jun 2017
Cited by 11 | Viewed by 5377
Abstract
The misalignment of the drive system of the DFIG (Doubly Fed Induction Generator) wind turbine is one of the important factors that cause damage to the gears, bearings of the high-speed gearbox and the generator bearings. How to use the limited information to [...] Read more.
The misalignment of the drive system of the DFIG (Doubly Fed Induction Generator) wind turbine is one of the important factors that cause damage to the gears, bearings of the high-speed gearbox and the generator bearings. How to use the limited information to accurately determine the type of failure has become a difficult study for the scholars. In this paper, the time-domain indexes and frequency-domain indexes are extracted by using the vibration signals of various misaligned simulation conditions of the wind turbine drive system, and the time-frequency domain features—energy entropy are also extracted by the IEMD (Improved Empirical Mode Decomposition). A mixed-domain feature set is constructed by them. Then, SVM (Support Vector Machine) is used as the classifier, the mixed-domain features are used as the inputs of SVM, and PSO (Particle Swarm Optimization) is used to optimize the parameters of SVM. The fault types of misalignment are classified successfully. Compared with other methods, the accuracy of the given fault isolation model is improved. Full article
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15 pages, 1491 KB  
Article
Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM
by Yancai Xiao, Na Kang, Yi Hong and Guangjian Zhang
Entropy 2017, 19(1), 6; https://doi.org/10.3390/e19010006 - 1 Jan 2017
Cited by 56 | Viewed by 8168
Abstract
Misalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, [...] Read more.
Misalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, Solidworks and Adams are used to simulate the different operating conditions of the transmission system of the DFWT to obtain the corresponding characteristic signals. Improved empirical mode decomposition (IEMD), which improves the end effects of empirical mode decomposition (EMD) is used to decompose the signals to get intrinsic mode function (IMF), and the IEMD energy entropy reflecting the working state are extracted as the inputs of the support vector machine (SVM). Particle swarm optimization (PSO) is used to optimize the parameters of SVM to improve the classification performance. The results show that the proposed method can effectively and accurately identify the types of misalignment of the DFWT. Full article
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