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Combustion instability detection using the wavelet detail of pressure fluctuations

JI Junjie, LUO Yonghao

《能源前沿（英文）》 2008年 第2卷 第1期   页码 116-120 doi: 10.1007/s11708-008-0019-0

摘要： A combustion instability detection method that uses the wavelet detail of combustion pressure fluctuations is put forward. To confirm this method, combustion pressure fluctuations in a stoker boiler are recorded at stable and unstable combustion with a pressure transducer. Daubechies one-order wavelet is chosen to obtain the wavelet details for comparison. It shows that the wavelet approximation indicates the general pressure change in the furnace, and the wavelet detail magnitude is consistent with the intensity of turbulence and combustion noise. The magnitude of the wavelet detail is nearly constant when the combustion is stable, however, it will fluctuate much when the combustion is unstable.

关键词： comparison     wavelet approximation     pressure transducer     general pressure     consistent    

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A case study on sample average approximation method for stochastic supply chain network design problem

Yuan WANG, Ruyan SHOU, Loo Hay LEE, Ek Peng CHEW

《工程管理前沿（英文）》 2017年 第4卷 第3期   页码 338-347 doi: 10.15302/J-FEM-2017032

摘要： This study aims to solve a typical long-term strategic decision problem on supply chain network design with consideration to uncertain demands. Existing methods for these problems are either deterministic or limited in scale. We analyze the impact of uncertainty on demand based on actual large data from industrial companies. Deterministic equivalent model with nonanticipativity constraints, branch-and-fix coordination, sample average approximation (SAA) with Bayesian bootstrap, and Latin hypercube sampling were adopted to analyze stochastic demands. A computational study of supply chain network with front-ends in Europe and back-ends in Asia is presented to highlight the importance of stochastic factors in these problems and the efficiency of our proposed solution approach.

关键词： supply chain network     stochastic demand     sampling average approximation     Bayesian bootstrap     Latin hypercube sampling    

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Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet

Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU

《机械工程前沿（英文）》 2017年 第12卷 第3期   页码 406-419 doi: 10.1007/s11465-017-0419-0

摘要：

Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.

关键词： wind turbine     planet gear fault     feature extraction     spectral kurtosis     time wavelet energy spectrum    

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Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration

null

《机械工程前沿（英文）》 2014年 第9卷 第2期   页码 130-141 doi: 10.1007/s11465-014-0298-6

摘要：

Fault diagnosis of rolling element bearings requires efficient signal processing techniques. For this purpose, the performances of envelope detection with fast Fourier transform (FFT) and continuous wavelet transform (CWT) of vibration signals produced from a bearing with defects on inner race and rolling element, have been examined at low signal to noise ratio. Both simulated and experimental signals from identical bearings have been considered for the purpose of analysis. The bearings have been modeled as spring-mass-dashpot systems and the simulated signals have been obtained considering transfer functions for the bearing systems subjected to impulsive loads due to the defects. Frequency B spline wavelets have been applied for CWT and a discussion on wavelet selection has been presented for better effectiveness. Results show that use of CWT with the proposed wavelets overcomes the short coming of FFT while processing a noisy vibration signals for defect detection of bearings.

关键词： Fault detection     spline wavelet     continuous wavelet transform     fast Fourier transform    

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.: evidence from wavelet analysis

Alper ASLAN, Nicholas APERGIS, Selim YILDIRIM

《能源前沿（英文）》 2014年 第8卷 第1期   页码 1-8 doi: 10.1007/s11708-013-0290-6

摘要： This study investigates the dynamic causal relationship between energy consumption and economic growth in the U.S. at different time scales. The main novelty of the study is that this paper complements the existing studies on the nexus between energy consumption and economic growth by employing the wavelet transformation to obtain different time scales in order to investigate causality between energy consumption and economic growth. This method is first developed by Ramsey and Lampart. Their approach consists of first decomposing the series into time scales by wavelet filters and testing causality of each time scale with the pertinent time scale of the other series separately. The data span from 1973q1 to 2012q1 on a quarterly basis. The main empirical insight is that the causal relationship is stronger at finer time scales, whereas the relationship is less and less apparent at longer time horizons. The results indicate that energy consumption causes economic growth, while the reverse is not true at the original frequency of the data. At the very finest scale the same result arises. However, at coarser scales feedback is observed. In particular, at intermediate time scales the evidence indicates that energy consumption causes economic growth, while the reverse is also true. These empirical findings are expected to be of high importance in terms of the effective design and implementation of energy and environmental policies, especially when a number of countries in the pursuit of high economic growth targets do not pay any serious attention on environmental issues.

关键词： energy consumption     economic growth     wavelet analysis     granger causality    

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自适应小波阈值去噪在重力仪信号处理中的应用

赵立业,周百令,李坤宇

《中国工程科学》 2006年 第8卷 第3期   页码 49-52

摘要：

为了有效地消除各种外界干扰噪声对高精度海洋重力仪测量值的影响，提高重力异常测量值的精度，在分析了小波阈值及自适应小波阈值去噪算法的基础上，将其应用到高精度海洋重力仪系统数据处理中，并与自适应卡尔曼滤波做了对比，以处理后信号的信噪比作为衡量3种数据处理方法优劣的依据。理论分析和仿真实验表明，自适应小波阈值去噪方法、传统的小波阈值去噪方法和自适应卡尔曼滤波都能在一定程度上消除噪声信号对重力仪测量信号的影响，但在相同情况下，自适应小波阈值去噪方法具有明显的优越性。

关键词： 重力仪     信号处理     小波变换     自适应阈值去噪     自适应卡尔曼滤波    

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Prediction of characteristic blast-induced vibration frequency during underground excavation by using wavelet

Tae Un PAK; Guk Rae JO; Un Chol HAN

《结构与土木工程前沿（英文）》 2022年 第16卷 第8期   页码 1029-1039 doi: 10.1007/s11709-022-0861-x

摘要： Blast-induced vibration produces a very complex signal, and it is very important to work out environmental problems induced by blasting. In this study, blasting vibration signals were measured during underground excavation in carbonaceous shale by using vibration pickup CB-30 and FFT analyzer AD-3523. Then, wavelet analysis on the measured results was carried out to identify frequency bands reflecting changes of blasting vibration parameters such as vibration velocity and energy in different frequency bands. Frequency characteristics are then discussed in view of blast source distance and charge weight per delay. From analysis of results, it can be found that peak velocity and energy of blasting vibration in frequency band of 62.5–125 Hz were larger than ones in other bands, indicating the similarity to characteristics in the distribution band (31–130 Hz) of main vibration frequency. Most frequency bands were affected by blasting source distance, and the frequency band of 0–62.5 Hz reflected the change of charge weight per delay. By presenting a simplified method to predict main vibration frequency, this research may provide significant reference for future blasting engineering.

关键词： wavelet analysis     blast-induced vibration     frequency characteristics     underground excavation    

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Shape reconstruction of parallelogram flaw

ZHENG Gangfeng, WU Bin, HE Cunfu

《机械工程前沿（英文）》 2008年 第3卷 第1期   页码 17-22 doi: 10.1007/s11465-008-0015-4

摘要： To reconstruct the shape of the scatterer in elastic media, the authors deduce the Born approximation solution of the two-dimensional scattering problem, which includes the shape factor that embodies all information about the shape of the scatterer. Accordingly, the change in the shape of the scatterer only necessitates the number of the corresponding new shape factors. For a parallelogram void in a long Al rod, its shape factor can be obtained. In view of the definition of a characteristic function, the shape factor has a corresponding integral representation. Obviously, the shape factor can be considered as a Fourier transform of the characteristic function, which is reconstructed from the inverse Fourier transform. The integral equation is considered as the basic equation to reconstruct the shape of the scatterer. The identification of the geometrical character of a flaw is then given by the two dimensional inverse Born approximation in a low-frequency range. For the parallelogram void, a theoretical calculating identification is performed. At the same time, the numerical results are obtained by the finite element method.

关键词： approximation     scatterer     scattering problem     information     inverse    

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Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

《机械工程前沿（英文）》 2015年 第10卷 第3期   页码 277-286 doi: 10.1007/s11465-015-0348-8

摘要：

This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal’s condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients’ energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters’ space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.

关键词： fault diagnosis     spur gearbox     wavelet packet decomposition     random forest    

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De-noising of diesel vibration signal using wavelet packet and singular value decomposition

DUAN Li-xiang, ZHANG Lai-bin, WANG Zhao-hui

《机械工程前沿（英文）》 2006年 第1卷 第4期   页码 443-447 doi: 10.1007/s11465-006-0055-6

摘要： The vibration signals of diesel include excess noise that must be eliminated before extraction of characteristic parameters. Firstly, the effects of vibration-signal de-noising among Fourier transform, wavelet decomposition and wavelet packet decomposition are compared. Secondly, singular value decomposition is applied to de-noising vibration signals. Finally, a new de-noise method integrated with wavelet packet and singular value is presented. In this method, vibration signals are decomposed by wavelet packet, and the wavelet packet coefficient is de-noised by singular value decomposition again. The results indicate that the new de-noising method is the best. The SNR (signal-to-noise ratio) of the vibration signals of a diesel cylinder lid is the highest. The diesel vibration waveforms of combustion and valve become clear and the extracted characteristic parameters become more precise.

关键词： coefficient     de-noised     cylinder     signal-to-noise     wavelet decomposition    

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flow regime identification in horizontal tube bundles under vertical upward cross-flow condition using wavelet

HUANG Xinghua, WANG Li, JIA Feng

《能源前沿（英文）》 2008年 第2卷 第3期   页码 333-338 doi: 10.1007/s11708-008-0043-0

摘要： A wavelet-transform based approach for flow regime identification in horizontal tube bundles under vertical upward cross-flow condition was presented. Tests on two-phase flow pattern of R134a were conducted under low mass velocity and flow boiling conditions over ranges of mass flux 4–25 kg/ms, vapor quality 0.02–0.90. Time series of differential pressure fluctuations were measured and analyzed with discrete wavelet transform. Different time-scale characteristics in bubbly flow, churn flow and annular flow were analyzed. The wavelet energy distributions over scales were found to be appropriate for flow regime identification. Based on the wavelet energy distribution over characteristic scales, a criterion of flow regime identification was proposed. The comparison with experiment results show that it is feasible to use the discrete wavelet transform as the tool of flow regime identification in horizontal tube bundles under vertical upward cross-flow condition.

关键词： two-phase     discrete     appropriate     wavelet-transform     criterion    

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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

《环境科学与工程前沿（英文）》 2023年 第17卷 第2期 doi: 10.1007/s11783-023-1622-3

摘要：

● A novel deep learning framework for short-term water demand forecasting.

关键词： Short-term water demand forecasting     Long-short term memory neural network     Convolutional Neural Network     Wavelet multi-resolution analysis     Data-driven models    

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Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based

Mehrdad TARAFDAR HAGH,Homayoun EBRAHIMIAN,Noradin GHADIMI

《能源前沿（英文）》 2015年 第9卷 第1期   页码 75-90 doi: 10.1007/s11708-014-0337-3

摘要： In this paper, a passive neuro-wavelet based islanding detection technique for grid-connected inverter-based distributed generation was developed. The weight parameters of the neural network were optimized by intelligent water drop (IWD) to improve the capability of the proposed technique in the proposed problem. The proposed method utilizes and combines wavelet analysis and artificial neural network (ANN) to detect islanding. Connecting distributed generator to the distribution network has many benefits such as increasing the capacity of the grid and enhancing the power quality. However, it gives rise to many problems. This is mainly due to the fact that distribution networks are designed without any generation units at that level. Hence, integrating distributed generators into the existing distribution network is not problem-free. Unintentional islanding is one of the encountered problems. Discrete wavelet transform (DWT) is capable of decomposing the signals into different frequency bands. It can be utilized in extracting discriminative features from the acquired voltage signals. In passive schemes with a large non-detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The simulation results from Matlab/Simulink shows that the proposed method has a small non-detection zone, and is capable of detecting islanding accurately within the minimum standard time.

关键词： islanding detection     neuro-wavelet     intelligent water drop (IWD)     non-detection zone (NDZ)     distributed generation (DG)    

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广义近似空间与粗糙分类代数

刘永红

《中国工程科学》 2006年 第8卷 第3期   页码 39-48

摘要：

提出了广义近似空间、粗近似公理、干扰集公理、粗糙集的分类原则、粗选原则、不确定偶集原则、精选原则、对策分类、量子逻辑分类、bit量子对称分类、不可比集、bit空间集、协议关系、粗糙集函数、粗糙分类代数和粗糙单代数等基本概念，并提出了一个猜想；展示了一些新观点；基于协议关系构造了粗糙商代数和粗糙子代数，给出了回避-归并算法及算例。

关键词： 广义近似空间     粗糙集     粗糙分类代数     协议关系     粗糙单代数     粗糙商代数     粗糙子代数    

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连续非线性最优控制问题的勒让德—伽辽金逼近方法 Article

Xue-song CHEN

《信息与电子工程前沿（英文）》 2017年 第18卷 第10期   页码 1479-1487 doi: 10.1631/FITEE.1601101

摘要： 该方法基于伽辽金逼近理论（Galerkin approximation）求解广义哈密尔顿—雅可比—贝尔曼（Hamilton-Jacobi-Bellman, GHJB）方程。

关键词： 广义哈密尔顿—雅可比—贝尔曼方程；非线性最优控制；伽辽金逼近（Galerkin approximation）；勒让德多项式（Legendre polynomials）    

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标题 作者 时间 类型 操作

Combustion instability detection using the wavelet detail of pressure fluctuations

JI Junjie, LUO Yonghao

期刊论文

A case study on sample average approximation method for stochastic supply chain network design problem

Yuan WANG, Ruyan SHOU, Loo Hay LEE, Ek Peng CHEW

期刊论文

Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet

Yun KONG, Tianyang WANG, Zheng LI, Fulei CHU

期刊论文

Identification of faults through wavelet transform vis-à-vis fast Fourier transform of noisy vibration

null

期刊论文

.: evidence from wavelet analysis

Alper ASLAN, Nicholas APERGIS, Selim YILDIRIM

期刊论文

自适应小波阈值去噪在重力仪信号处理中的应用

赵立业,周百令,李坤宇

期刊论文

Prediction of characteristic blast-induced vibration frequency during underground excavation by using wavelet

Tae Un PAK; Guk Rae JO; Un Chol HAN

期刊论文

Shape reconstruction of parallelogram flaw

ZHENG Gangfeng, WU Bin, HE Cunfu

期刊论文

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Diego CABRERA,Fernando SANCHO,René-Vinicio SÁNCHEZ,Grover ZURITA,Mariela CERRADA,Chuan LI,Rafael E. VÁSQUEZ

期刊论文

De-noising of diesel vibration signal using wavelet packet and singular value decomposition

DUAN Li-xiang, ZHANG Lai-bin, WANG Zhao-hui

期刊论文

flow regime identification in horizontal tube bundles under vertical upward cross-flow condition using wavelet

HUANG Xinghua, WANG Li, JIA Feng

期刊论文

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

期刊论文

Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based

Mehrdad TARAFDAR HAGH,Homayoun EBRAHIMIAN,Noradin GHADIMI

期刊论文

广义近似空间与粗糙分类代数

刘永红

期刊论文

连续非线性最优控制问题的勒让德—伽辽金逼近方法

Xue-song CHEN

期刊论文