Fault Feature Extraction of Hydraulic Pumps Based on Symplectic Geometry Mode Decomposition and Power Spectral Entropy

Zheng, Zhi; Xin, Ge

doi:10.3390/e21050476

Cited by 27 publications

(14 citation statements)

References 32 publications

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“…The analysis of eigenvalues are also called as the symplectic geometry spectrums analysis (SGSA) [6,18,19]. The corresponding components are also regarded as symplectic geometry mode decomposition (SGMD) [7,8,20,21]. According to the symplectic geometry spectrums above, if the number of the chosen symplectic principal components is k, the corresponding principal eigenvector matrix p can be constructed by using the first k eigenvectors of the matrix P in the matrix Q.…”

Section: Symplectic Principal Component Analysis (Spca) Of a Time Seriesmentioning

confidence: 99%

“…Besides, the symplectic geometry method also further integrate other approaches to better investigate the fault extraction and identification for rotating systems, such as symplectic geometry mode decomposition [19] with power spectral entropy [7] as well as Lagrange multiplier [20], symplectic transformation based variational Bayesian learning [21].…”

Section: Applicationsmentioning

confidence: 99%

“…Different from the conventional Euclidean geometry that measures 1-dimensional lengths and angles, the symplectic geometry studies the metric properties (such as area) and can preserve the system structure in the phase space [4]. Apart from applications on the classical dynamical systems to solve the equation problems, symplectic geometry has been also used on the studies of nonlinear time series [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Symplectic Geometry and Its Applications on Time Series Analysis

Lei¹

2021

Structure Topology and Symplectic Geometry

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This chapter serves to introduce the symplectic geometry theory in time series analysis and its applications in various fields. The basic concepts and basic elements of mathematics relevant to the symplectic geometry are introduced in the second section. It includes the symplectic space, symplectic transformation, Hamiltonian matrix, symplectic principal component analysis (SPCA), symplectic geometry spectrum analysis (SGSA), symplectic geometry mode decomposition (SGMD), and symplectic entropy (SymEn), etc. In addition, it also briefly reviews the applications of symplectic geometry on time series analysis, such as the embedding dimension estimation, nonlinear testing, noise reduction, as well as fault diagnosis. Readers who are familiar with the mathematical preliminaries may omit the second section, i.e. the theory part, and go directly to the third section, i.e. the application part.

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Section: Symplectic Principal Component Analysis (Spca) Of a Time Seriesmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Symplectic Geometry and Its Applications on Time Series Analysis

Lei¹

2021

Structure Topology and Symplectic Geometry

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“…The probability distribution in energy entropy is associated with power energy distribution of the transformed (decomposed) components in the time domain [89]. By contrast, the probability distribution in spectral entropy is related to the power spectrum distribution of the transformed components in the frequency domain [90]. These two entropy measures are practical for distinguishing machine health conditions, usually in combination with signal timefrequency analysis techniques, such as wavelet analysis [89], Fourier analysis [91], and Hilbert transform [92].…”

Section: A Entropy Measure As a Feature Indicatormentioning

confidence: 99%

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Huo

Martínez-García

Zhang

et al. 2020

IEEE Trans. Instrum. Meas.

137

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Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault diagnostic systems. The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future datadriven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems.

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“…With the increase of power equipment, the possibility of equipment failure and the resulting loss rises accordingly. Therefore, it has become an important research issue to study safer and more effective state monitoring methods, so as to improve the monitoring sensitivity, realize fault warning, and improve the generalization of fault diagnosis systems [ 1 , 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Entropy Feature Extraction Method of Running Power Equipment Sound

Zhai

Yang

Peng

et al. 2020

Entropy

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The equipment condition monitoring based on computer hearing is a new pattern recognition approach, and the system formed by it has the advantages of noncontact and strong early warning abilities. Extracting effective features from the sound data of the running power equipment help to improve the equipment monitoring accuracy. However, the sound of running equipment often has the characteristics of serious noise, non-linearity and instationary, which makes it difficult to extract features. To solve this problem, a feature extraction method based on the improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and multiscale improved permutation entropy (MIPE) is proposed. Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from the sound of running power equipment. The noise IMFs are then identified and eliminated through mutual information (MI) and mean mutual information (meanMI) of IMFs. Next, the normalized mutual information (norMI) and MIPE are calculated respectively, and norMI is utilized to weigh the corresponding MIPE result. Finally, based on the separability criterion, the weighted MIPE results are feature-dimensionally reduced to obtain the multiscale entropy feature of the sound. The experimental results show that the classification accuracies of the method under the conditions of no noise and 5 dB reach 96.7% and 89.9%, respectively. In practice, the proposed method has higher reliability and stability for the sound feature extraction of the running power equipment.

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Fault Feature Extraction of Hydraulic Pumps Based on Symplectic Geometry Mode Decomposition and Power Spectral Entropy

Cited by 27 publications

References 32 publications

Symplectic Geometry and Its Applications on Time Series Analysis

Symplectic Geometry and Its Applications on Time Series Analysis

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Multiscale Entropy Feature Extraction Method of Running Power Equipment Sound

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