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小波分析 4

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分层自适应神经模糊推理系统控制器；晶闸管控制串联电容器补偿技术；自动发电控制（AGC）；多目标粒子群优化算法；电力系统动态稳定性；相互联系的多源电力系统 1

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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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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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Bridging the gap: Neuro-Symbolic Computing for advanced AI applications in construction

《工程管理前沿（英文）》   页码 727-735 doi: 10.1007/s42524-023-0266-0

摘要： Deep Learning (DL) has revolutionized the field of Artificial Intelligence (AI) in various domains such as computer vision (CV) and natural language processing. However, DL models have limitations including the need for large labeled datasets, lack of interpretability and explainability, potential bias and fairness issues, and limitations in common sense reasoning and contextual understanding. On the other side, DL has shown significant potential in construction for safety and quality inspection tasks using CV models. However, current CV approaches may lack spatial context and measurement capabilities, and struggle with complex safety and quality requirements. The integration of Neuro-Symbolic Computing (NSC), an emerging field that combines DL and symbolic reasoning, has been proposed as a potential solution to address these limitations. NSC has the potential to enable more robust, interpretable, and accurate AI systems in construction by harnessing the strengths of DL and symbolic reasoning. The combination of symbolism and connectionism in NSC can lead to more efficient data usage, improved generalization ability, and enhanced interpretability. Further research and experimentation are needed to effectively integrate NSC with large models and advance CV technologies for precise reporting of safety and quality inspection results in construction.

关键词： advanced AI in construction     safety and quality inspection     Neuro-Symbolic Computing     Deep Learning     computer vision    

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Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy

《结构与土木工程前沿（英文）》   页码 812-826 doi: 10.1007/s11709-023-0940-7

摘要： A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

关键词： falling weight deflectometer     modulus of subgrade reaction     elastic modulus     metaheuristic algorithms    

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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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Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing

Reza TEIMOURI, Hamed SOHRABPOOR

《机械工程前沿（英文）》 2013年 第8卷 第4期   页码 429-442 doi: 10.1007/s11465-013-0277-3

摘要：

Electrochemical machining process (ECM) is increasing its importance due to some of the specific advantages which can be exploited during machining operation. The process offers several special privileges such as higher machining rate, better accuracy and control, and wider range of materials that can be machined. Contribution of too many predominate parameters in the process, makes its prediction and selection of optimal values really complex, especially while the process is programmized for machining of hard materials. In the present work in order to investigate effects of electrolyte concentration, electrolyte flow rate, applied voltage and feed rate on material removal rate (MRR) and surface roughness (SR) the adaptive neuro-fuzzy inference systems (ANFIS) have been used for creation predictive models based on experimental observations. Then the ANFIS 3D surfaces have been plotted for analyzing effects of process parameters on MRR and SR. Finally, the cuckoo optimization algorithm (COA) was used for selection solutions in which the process reaches maximum material removal rate and minimum surface roughness simultaneously. Results indicated that the ANFIS technique has superiority in modeling of MRR and SR with high prediction accuracy. Also, results obtained while applying of COA have been compared with those derived from confirmatory experiments which validate the applicability and suitability of the proposed techniques in enhancing the performance of ECM process.

关键词： electrochemical machining process (ECM)     modeling     adaptive neuro-fuzzy inference system (ANFIS)     optimization     cuckoo optimization algorithm (COA)    

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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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Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system

Ahmad SHARAFATI, H. NADERPOUR, Sinan Q. SALIH, E. ONYARI, Zaher Mundher YASEEN

《结构与土木工程前沿（英文）》 2021年 第15卷 第1期   页码 61-79 doi: 10.1007/s11709-020-0684-6

摘要： Concrete compressive strength prediction is an essential process for material design and sustainability. This study investigates several novel hybrid adaptive neuro-fuzzy inference system (ANFIS) evolutionary models, i.e., ANFIS–particle swarm optimization (PSO), ANFIS–ant colony, ANFIS–differential evolution (DE), and ANFIS–genetic algorithm to predict the foamed concrete compressive strength. Several concrete properties, including cement content (C), oven dry density (O), water-to-binder ratio (W), and foamed volume (F) are used as input variables. A relevant data set is obtained from open-access published experimental investigations and used to build predictive models. The performance of the proposed predictive models is evaluated based on the mean performance (MP), which is the mean value of several statistical error indices. To optimize each predictive model and its input variables, univariate (C, O, W, and F), bivariate (C–O, C–W, C–F, O–W, O–F, and W–F), trivariate (C–O–W, C–W–F, O–W–F), and four-variate (C–O–W–F) combinations of input variables are constructed for each model. The results indicate that the best predictions obtained using the univariate, bivariate, trivariate, and four-variate models are ANFIS–DE– (O) (MP= 0.96), ANFIS–PSO– (C-O) (MP= 0.88), ANFIS–DE– (O–W–F) (MP= 0.94), and ANFIS–PSO– (C–O–W–F) (MP= 0.89), respectively. ANFIS–PSO– (C–O) yielded the best accurate prediction of compressive strength with an MP value of 0.96.

关键词： foamed concrete     adaptive neuro fuzzy inference system     nature-inspired algorithms     prediction of compressive strength    

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Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems

Abbas PARSAIE,Amir Hamzeh HAGHIABI,Mojtaba SANEIE,Hasan TORABI

《结构与土木工程前沿（英文）》 2017年 第11卷 第1期   页码 111-122 doi: 10.1007/s11709-016-0354-x

摘要： Settlement of sediments behind weirs and accumulation of materials floating on water behind gates decreases the performance of these structures. Weir-gate is a combination of weir and gate structures which solves them Infirmities. Proposing a circular shape for crest of weirs to improve their performance, investigators have proposed cylindrical shape to improve the performance of weir-gate structure and call it cylindrical weir-gate. In this research, discharge coefficient of weir-gate was predicated using adaptive neuro fuzzy inference systems (ANFIS). To compare the performance of ANFIS with other types of soft computing techniques, multilayer perceptron neural network (MLP) was prepared as well. Results of MLP and ANFIS showed that both models have high ability for modeling and predicting discharge coefficient; however, ANFIS is a bit more accurate. The sensitivity analysis of MLP and ANFIS showed that Froude number of flow at upstream of weir and ratio of gate opening height to the diameter of weir are the most effective parameters on discharge coefficient.

关键词： weir-gate     soft computing     crest geometry     circular crest weir     cylindrical shape    

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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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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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标题 作者 时间 类型 操作

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

Combustion instability detection using the wavelet detail of pressure fluctuations

JI Junjie, LUO Yonghao

期刊论文

Bridging the gap: Neuro-Symbolic Computing for advanced AI applications in construction

期刊论文

Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy

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

Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing

Reza TEIMOURI, Hamed SOHRABPOOR

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

Simulation of foamed concrete compressive strength prediction using adaptive neuro-fuzzy inference system

Ahmad SHARAFATI, H. NADERPOUR, Sinan Q. SALIH, E. ONYARI, Zaher Mundher YASEEN

期刊论文

Predication of discharge coefficient of cylindrical weir-gate using adaptive neuro fuzzy inference systems

Abbas PARSAIE,Amir Hamzeh HAGHIABI,Mojtaba SANEIE,Hasan TORABI

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

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