scholarly journals Study on Fault Diagnosis Method of Planetary Gearbox Based on Turn Domain Resampling and Variable Multi-Scale Morphological Filtering

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Chao Zhang
Keyword(s):  
Frequency Conversion ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Operating Efficiency ◽  
Planetary Gearbox ◽  
Noise Interference ◽  
Multi Scale ◽  
Morphological Filtering ◽  
Diagnosis Method ◽  
Simulation Signal

The turn domain resampling (TDR) method is proposed in the paper on the basis of the existing angle domain resampling for solving the problem of non-fixed fault frequency under variable working conditions. TDR can select the appropriate sampling order according to the influence of frequency conversion, which avoided the error caused by the spline interpolation method. It can provide accurate parameters for the subsequent calculation of the equivalent frequency order. Variable multi-scale morphological filtering (VMSMF) method is proposed for the purpose of further reducing the interference of noise in resampling signal to feature extraction. VMSMF adaptively selects structural elements according to the parameter change of impact signal to make its scale more targeted. It only needs to calculate once using the optimal structural unit for a particular impact, and the filtering accuracy and operating efficiency have been greatly improved. The main steps of this article are as follows. First, the TDR is used to resample the original signal as to get the resampling signal which is still submerged by the strong noise. In the second step, VMSMF is used to filter the resampling signal to obtain the signal with less noise interference. Finally, the fault characteristics of the filtering signal was extracted and compared with the possible fault frequency calculated by the sampling parameters provided by resampling, so as to determine the fault type of the planetary gearbox. By analyzing the simulation signal and the experimental signal respectively, this method can find out the corresponding fault characteristics effectively.

scholarly journals Research on fault diagnosis of planetary gearbox based on Variable Multi-Scale Morphological Filtering and improved Symbol Dynamic Entropy

2021 ◽  
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Jianyu Zhang ◽  
Chao Zhang
Keyword(s):  
Working Conditions ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
High Signal ◽  
Structural Element ◽  
Planetary Gearbox ◽  
Noise Interference ◽  
Multi Scale ◽  
Time Domain Signal ◽  
Morphological Filtering

Abstract Under complex working conditions with noise interference, the fault feature of planetary gearbox is difficult to be extracted and the fault mode is difficult to be identified. To tackle this problem, the technologies of Variable Multi-scale Morphological Filtering (VMSMF) and Average Multi-scale Double Symbolic Dynamic Entropy (AMDSDE) are proposed in this paper. VMSMF selects Chebyshev Window as the structural element and automatically selects the optimal scale parameters according to the signal characteristics of the planetary gearbox, which improves the filtering accuracy and calculation efficiency. AMDSDE fully considers the correlation between various state modes. Once combined with relevant knowledge of Mathematical statistics, the algorithm can effectively reduce misjudgment. Firstly, the Turn Domain Resampling (TDR) is used to transform the time domain signal of variable speed into the angle domain signal that is not affected by speed change. Secondly, the proposed VMSMF is used to de-noise the vibration signal, and the fault signal with a high signal-to-noise ratio is obtained. Finally, AMDSDE is used to extract the entropy value of the fault signal and judge the fault type. The proposed technology is verified by four kinds of signals collected from the sun gear of the planetary gearbox under non-stationary working conditions.

scholarly journals Analysis on the Synergy Evolutionary Development of the Collecting, Distributing, and Transporting System of Railway Heavy Haul Transportation

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Fenling Feng ◽  
Dan Lan ◽  
Liuwen Yang
Keyword(s):  
Fourth Order ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Research Result ◽  
Evolutionary Model ◽  
Evolutionary Development ◽  
Heavy Load ◽  
Heavy Haul ◽  
Load Transportation ◽  
The Empirical Analysis

A synergy evolutionary model of the collecting, distributing, and transporting system of railway heavy haul transportation is built by introducing synergy-related concepts and applying synergy evolutionary theory. Then spline interpolation method, numerical differential five-point formula, and method of least squares are used to solve synergistic coefficient, while fourth-order Rugge-kutta method and fourth-order Adams linear implicit formula method are used to solve coevolutionary curve of the system. Finally, the heavy load transportation of Daqin Railway is an example of the empirical analysis. The research result shows that the degree of order of the system and its three subsystems—collecting, transporting, and distributing—increases as the synergetic coefficient of the subsystems increases; otherwise, the degree of the order will decrease. It also shows that this model can better analyze the coevolutionary process of the heavy load collecting, distributing, and transporting system of Daqin Railway, with its rationality and applicability verified.

scholarly journals Integrated approach for ball mill load forecasting based on improved EWT, refined composite multi-scale dispersion entropy and fireworks algorithm optimized SVM

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199126
Author(s):  
Jiacheng Cai ◽  
Lirong Yang ◽  
Changxi Zeng ◽  
Yongkang Chen
Keyword(s):  
Ball Mill ◽  
Spline Interpolation ◽  
Load Forecasting ◽  
Load Prediction ◽  
Filling Rate ◽  
Vibration Signals ◽  
Fireworks Algorithm ◽  
Feature Vectors ◽  
Multi Scale ◽  
Empirical Wavelet Transform

Shell vibration signals generated during grinding have useful information related to ball mill load, while usually contaminated by noises. It is a challenge to recognize load parameters with these signals. In this paper, a novel approach is proposed based on the improved empirical wavelet transform (EWT), refined composite multi-scale dispersion entropy (RCMDE) and fireworks algorithm (FWA) optimized SVM. Firstly, vibration signals are denoised by improved EWT, which uses cubic spline interpolation to calculate envelope spectrum for segmentation. Then, RCMDEs of the denoised signals are calculated as feature vectors. The vectors’ dimensionalities are reduced by principal component analysis (PCA). Finally, a mill load prediction model is established based on the FWA optimized SVM. The reduced feature vectors are fed to the model, thus material-to-ball ratio and filling rate being outputs. Grinding experiments show that the extracted features by RCMDE can effectively distinguish three load states. Meanwhile, experiments also show that FWA reduces the forecasting errors of material-to-balls ratio and filling rate by 1.9% and 2.9% compared with genetic algorithm (GA), as well as by 1.92% and 4.21% compared with particle swarm optimization (PSO) algorithm. It demonstrates that the proposed approach for ball mill load forecasting has high accuracy and stability.

scholarly journals Error Analysis for a Noisy Lacunary Cubic Spline Interpolation and a Simple Noisy Cubic Spline Quasi Interpolation

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Feng-Gong Lang ◽  
Xiao-Ping Xu
Keyword(s):  
Error Analysis ◽  
Error Bounds ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Cubic Spline ◽  
The Other ◽  
Cubic Spline Interpolation ◽  
Numerical Tests ◽  
New Methods ◽  
Noisy Function

We mainly present the error analysis for two new cubic spline based methods; one is a lacunary interpolation method and the other is a very simple quasi interpolation method. The new methods are able to reconstruct a function and its first two derivatives from noisy function data. The explicit error bounds for the methods are given and proved. Numerical tests and comparisons are performed. Numerical results verify the efficiency of our methods.

scholarly journals A Nonlinear Calibration Method Based on Sinusoidal Excitation and DFT Transformation for High-Precision Power Analyzers

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenjian Zhou ◽  
Sheng Yang ◽  
Li Wang ◽  
Hanmin Sheng ◽  
Yang Deng
Keyword(s):  
High Precision ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Calibration Method ◽  
Calibration Error ◽  
Initial Moment ◽  
Calibration Process ◽  
Calibration Methods ◽  
Nonlinear Calibration ◽  
Sinusoidal Excitation

For most high-precision power analyzers, the measurement accuracy may be affected due to the nonlinear relationship between the input and output signal. Therefore, calibration before measurement is important to ensure accuracy. However, the traditional calibration methods usually have complicated structures, cumbersome calibration process, and difficult selection of calibration points, which is not suitable for situations with many measurement points. To solve these issues, a nonlinear calibration method based on sinusoidal excitation and DFT transformation is proposed in this paper. By obtaining the effective value data of the current sinusoidal excitation from the calibration source, the accurate calibration process can be done, and the calibration efficiency can be improved effectively. Firstly, through Fourier transform, the phase value at the initial moment of the fundamental frequency is calculated. Then, the mapping relationship between the sampling value and the theoretical calculation value is established according to the obtained theoretical discrete expression, and a cubic spline interpolation method is used to further reduce the calibration error. Simulations and experiments show that the calibration method presented in this paper achieves high calibration accuracy, and the results are compensation value after calibration with a deviation of ± 3 × 10 − 4 .

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