A novel signature extracting approach for inductive oil debris sensors based on symplectic geometry mode decomposition

Measurement ◽  
2021 ◽  
Vol 185 ◽  
pp. 110056
Author(s):  
Bing Yu ◽  
Nan Cao ◽  
Tianhong Zhang
Keyword(s):  
Symplectic Geometry ◽  
Mode Decomposition ◽  
Oil Debris

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

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 476 ◽  
Author(s):  
Zhi Zheng ◽  
Ge Xin
Keyword(s):  
Feature Extraction ◽  
Symplectic Geometry ◽  
Spectral Entropy ◽  
Hydraulic Pump ◽  
Mode Decomposition ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Pump Signal ◽  
The Difference ◽  
Fault Feature Extraction

Aiming at fault feature extraction of a hydraulic pump signal, a new method based on symplectic geometry mode decomposition (SGMD) and power spectral entropy (PSE) is proposed. First, the SGMD is applied to decompose a multi-component fault signal, then the N symplectic geometry components (SGCs) can be obtained. Second, the N SGCs are reconstructed as a signal of interest and, consequently, the power spectral entropy of each constructed signal is computed to quantify the complexity and uncertainty of their spectra. Finally, the difference value (D-value) between the adjacent entropies is used as a SGCs criterion, whose turning point indicates the most information of reconstructed signal. Hydraulic pump signals are tested and verified, and results demonstrate that the proposed method can extract the richest fault feature information of hydraulic pump signals effectively.

Gearbox compound fault diagnosis based on a combined MSGMD-MOMEDA method

2021 ◽  
Author(s):  
Jianqun Zhang ◽  
Qing Zhang ◽  
Xianrong Qin ◽  
Yuantao Sun ◽  
Jun Zhang
Keyword(s):  
Background Noise ◽  
Symplectic Geometry ◽  
Original Method ◽  
Minimum Entropy ◽  
Mode Decomposition ◽  
Accuracy And Precision ◽  
Fault Features ◽  
Simulation And Experiment ◽  
Diagnosis Method ◽  
Weak Fault

Abstract Weak fault detection is a complex and challenging task when two or more faults (compound fault) with discordant severity occur in different parts of a gearbox. The weak fault features are prone to be submerged by the severe fault features and strong background noise, which easily lead to a missed diagnosis. To solve this problem, a novel diagnosis method combining muti-symplectic geometry mode decomposition and multipoint optimal minimum entropy deconvolution adjusted (MSGMD-MOMEDA) is proposed for gearbox compound fault in this paper. Specifically, different fault components are separated by the improved symplectic geometry mode decomposition (SGMD), namely, multi-SGMD (MSGMD) method. The weak fault features are enhanced by the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). In the process of research, a new scheme of selecting key parameters of MOMEDA is proposed, which is a key step in applying MOMEDA. Compared with SGMD, the proposed MSGMD has two main improvements, including suppressing mode mixing and preventing the generation of the pseudo components. Compared with the original method of selecting parameters based on multipoint kurtosis, the proposed MOMEDA parameters selecting scheme has more merits of high accuracy and precision. The analysis results of two cases of simulation and experiment signal reveal that the MSGMD-MOMEDA method can accurately diagnose the gearbox compound fault even under strong background noise.

scholarly journals An early fault diagnosis method of gear based on improved symplectic geometry mode decomposition

Measurement ◽  
2020 ◽  
Vol 151 ◽  
pp. 107140 ◽  
Author(s):  
Jian Cheng ◽  
Yu Yang ◽  
Xin Li ◽  
Haiyang Pan ◽  
Junsheng Cheng
Keyword(s):  
Fault Diagnosis ◽  
Symplectic Geometry ◽  
Mode Decomposition ◽  
Diagnosis Method

scholarly journals Symplectic Geometry and Its Applications on Time Series Analysis

2020 ◽  
Author(s):  
Min Lei
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Symplectic Geometry ◽  
Principal Component ◽  
Symplectic Space ◽  
Hamiltonian Matrix ◽  
The Third ◽  
Series Analysis ◽  
Mode Decomposition ◽  
Basic Concepts

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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