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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
关键词: coefficient de-noised cylinder signal-to-noise wavelet decomposition
Shuai MA, Siyu ZENG, Xin DONG, Jining CHEN, Gustaf OLSSON
《环境科学与工程前沿(英文)》 2014年 第8卷 第1期 页码 128-136 doi: 10.1007/s11783-013-0598-9
关键词: influent load prediction wavelet de-noising power spectrum density autoregressive model time-frequency analysis wastewater treatment
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
关键词: comparison wavelet approximation pressure transducer general pressure consistent
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
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
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
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
.: 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
关键词: energy consumption economic growth wavelet analysis granger causality
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
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。
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
关键词: wavelet analysis blast-induced vibration frequency characteristics underground excavation
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
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
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
关键词: two-phase discrete appropriate wavelet-transform criterion
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
● 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
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
关键词: islanding detection neuro-wavelet intelligent water drop (IWD) non-detection zone (NDZ) distributed generation (DG)
Diagnosis method based on wavelet coefficient scale relativity correlation dimension for fault
GU Junjie, TIAN Jin, PENG Xuezhi
《能源前沿(英文)》 2008年 第2卷 第2期 页码 164-168 doi: 10.1007/s11708-008-0031-4
关键词: effective technology monitoring mechanical equipment relativity Correlation
张红岩,张登攀
《中国工程科学》 2003年 第5卷 第4期 页码 61-64
边沿作为图像视觉的最主要特征,成为图像信息获取的重要内容。小波变换具有检测局域突变的能力,而且可以结合多尺度信息进行检测,因此成为图像信息边缘检测的优良工具。根据二维小波变换的特点,分析了利用二维小波进行图像边缘检测的基本原理,并设计了利用二维小波变换进行多尺度边缘匹配的检测算法。基于研究结果,编写了计算机应用程序,进行实例分析。
标题 作者 时间 类型 操作
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
Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment plants
Shuai MA, Siyu ZENG, Xin DONG, Jining CHEN, Gustaf OLSSON
期刊论文
Combustion instability detection using the wavelet detail of pressure fluctuations
JI Junjie, LUO Yonghao
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
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
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
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
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
Diagnosis method based on wavelet coefficient scale relativity correlation dimension for fault
GU Junjie, TIAN Jin, PENG Xuezhi
期刊论文