A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

Gearbox Fault Diagnosis using Adaptive Zero Phase Time-varying Filter Based on Multi-scale Chirplet Sparse Signal Decomposition

When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

Production of High Purity Multi-Walled Carbon Nanotubes from Catalytic Decomposition of Methane

Acid-based purification process of multi-walled carbon nanotubes （MWNTs） produced via catalytic decomposition of methane with NiO/TiO2 as a catalyst is described. By combining the oxidation in air and the acid refluxes, the impurities, such as amorphous carbon, carbon nanoparticles, and the NiO/TiO2 catalyst, are eliminated. Scanning electron microscopy （SEM） and transmission electron microscopy （TEM） images confirm the removal of the impurities. The percentage of the carbon nanotubes purity was analyzed using thermal gravimetric analysis （TGA）. Using this process, 99.9 wt% purity of MWNTs was obtained.

Measurement and Decomposition of Multi-Dimensional Poverty in China

Based on China Family Panel Studies(CFPS) data and global MPI standard,this paper measures and analyzes multi-dimensional poverty in China. The study finds that the level of multi-dimensional poverty in China is not high and tends to decrease over time.Uneven regional development significantly affects multi-dimensional poverty. The poor are deprived in health, education and other aspects, but indicator contributions vary among specific groups of people. Overlap between economic poverty and multi-dimensional poverty has a trend of inter-temporal reduction. China's development-centered poverty reduction policy has achieved great results and significantly improved the development capabilities of the poor. Development-oriented approach is China's important experience in poverty reduction, and forebodes China's bright prospect of achieving its goal to complete building a moderately prosperous society by 2020.

Multi-polarization reconstruction from compact polarimetry based on modified four-component scattering decomposition

An improved algorithm for multi-polarization reconstruction from compact polarimetry （CP） is proposed. According to two fundamental assumptions in compact polarimetric reconstruction, two improvements are proposed. Firstly, the four-component model-based decomposition algorithm is modified with a new volume scattering model. The decomposed helix scattering component is then used to deal with the non-reflection symmetry condition in compact polarimetric measurements. Using the decomposed power and considering the scattering mechanism of each component, an average relationship between copolarized and crosspolarized channels is developed over the original polarization state extrapolation model. E-SAR polarimetric data acquired over the Oberpfaffenhofen area and JPL/AIRSAR polarimetric data acquired over San Francisco are used for verification, and good reconstruction results are obtained, demonstrating the effectiveness of the proposed algorithm.

Modal identification of multi-degree-of-freedom structures based on intrinsic chirp component decomposition method

Modal parameter identification is a mature technology.However,there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination.This paper proposes a new time-frequency method based on intrinsic chirp component decomposition(ICCD)to address these issues.In this method,a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction.The effectiveness and accuracy of the proposed method are illustrated using three examples:a cantilever beam structure with intensive noise contamination or environmental interference,a four-degree-of-freedom structure with two closely spaced modes,and an impact test on a cantilever rectangular plate.By comparison with the identification method based on the empirical wavelet transform(EWT),it is shown that the presented method is effective,even in a high-noise environment,and the dynamic characteristics of closely spaced modes are accurately determined.

The Multi-Fractal Singularity Value Decomposition and Its Application in Extraction of Gravity Anomaly Associated with Gold Mineralization in Tongshi Gold Field

Because of the complication of geological procedures,the recorded data have the feature of nonlinear.The multi-fractal singularity value decomposition (MSVD) was used to decomposed the gravity data.In this paper,the MSVD was utilized to extract the gravity anomaly associated with the gold mineralization in Tongshi gold field in the southwest of Shandong province.The results showed that the Tongshi complex with negative circular gravity anomaly is an important ore-controlling factor.And the positive ring gravity anomaly distributed

The Adomian Decomposition Method and the Differential Transform Method for Numerical Solution of Multi-Pantograph Delay Differential Equations

In this paper, the Adomian Decomposition Method (ADM) and the Differential Transform Method (DTM) are applied to solve the multi-pantograph delay equations. The sufficient conditions are given to assure the convergence of these methods. Several examples are presented to demonstrate the efficiency and reliability of the ADM and the DTM;numerical results are discussed, compared with exact solution. The results of the ADM and the DTM show its better performance than others. These methods give the desired accurate results only in a few terms and in a series form of the solution. The approach is simple and effective. These methods are used to solve many linear and nonlinear problems and reduce the size of computational work.

MULTI-SCALE DECOMPOSITION OF BOUGUER GRAVITY ANOMALY AND SEISMIC ACTIVITY IN NORTH CHINA

Bouguer gravity anomaly in North China is decomposed with multi scale decomposition technique of wavelet transform. Gravity anomalies produced by anomalous density bodies of various scales are revealed from surface to Moho. Characteristics of anomalies of different orders and corresponding structural features are discussed. The result shows that details of wavelet transform of different orders reflect the distribution features of rock density at different depths and in various scales. In most cases, the two sides of a fault especially a deep and large fault in North China differ greatly in rock density. This difference records the history of the formation and evolution of the crust. Deep structural setting for the \%M\%s≥7.0 strong earthquakes in this region is also discussed.

Multi-scale spatial relationships between soil total nitrogen and influencing factors in a basin landscape based on multivariate empirical mode decomposition

The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.

Decomposition of Point-Symmetry Using Ordinal Quasi Point-Symmetry for Ordinal Multi-Way Tables

For multi-way tables with ordered categories, the present paper gives a decomposition of the point-symmetry model into the ordinal quasi point-symmetry and equality of point-symmetric marginal moments. The ordinal quasi point-symmetry model indicates asymmetry for cell probabilities with respect to the center point in the table.

Solution of Nonlinear Integro Differential Equations by Two-Step Adomian Decomposition Method (TSAM)

The Adomian decomposition method (ADM) can be used to solve a wide range of problems and usually gets the solution in a series form. In this paper, we propose two-step Adomian Decomposition Method (TSAM) for nonlinear integro-differential equations that will facilitate the calculations. In this modification, compared to the standard Adomian decomposition method, the size of calculations was reduced. This modification also avoids computing Adomian polynomials. Numerical results are given to show the efficiency and performance of this method.

Banach空间中Mann-Ishikawa迭代和Multi-Step迭代的等价性问题

本文研究了一致光滑Banach空间中迭代算法等价性的问题.利用泛函分析的方法,获得了广义强连接Φ伪压缩算子在具误差的修正的Mann-Ishikawa迭代和具误差的修正的multi-step迭代下收敛等价性的结果,推广了目前的相关结果.

Real-time multi-step prediction control for BP network with delay

Real time multi step prediction of BP network based on dynamical compensation of system characteristics is suggested by introducing the first and second derivatives of the system and network outputs into the network input layer, and real time multi step prediction control is proposed for the BP network with delay on the basis of the results of real time multi step prediction, to achieve the simulation of real time fuzzy control of the delayed time system.

面向STEP-NC数控加工的Multi-agent工艺协同设计研究

提出了一个基于特征的Multi-agent工艺协同设计系统,简要介绍了Multi-agent技术、该系统的体系结构和特征技术及特征继承关系,详细描述了系统中各代理的功能及系统工作流程。

Strategies for multi-step-ahead available parking spaces forecasting based on wavelet transform

A new methodology for multi-step-ahead forecasting was proposed herein which combined the wavelet transform(WT), artificial neural network(ANN) and forecasting strategies based on the changing characteristics of available parking spaces(APS). First, several APS time series were decomposed and reconstituted by the wavelet transform. Then, using an artificial neural network, the following five strategies for multi-step-ahead time series forecasting were used to forecast the reconstructed time series: recursive strategy, direct strategy, multi-input multi-output(MIMO) strategy, DIRMO strategy(a combination of the direct and MIMO strategies), and newly proposed recursive multi-input multi-output(RECMO) strategy which is a combination of the recursive and MIMO strategies. Finally, integrating the predicted results with the reconstructed time series produced the final forecasted available parking spaces. Three findings appear to be consistently supported by the experimental results. First, applying the wavelet transform to multi-step ahead available parking spaces forecasting can effectively improve the forecasting accuracy. Second, the forecasting resulted from the DIRMO and RECMO strategies is more accurate than that of the other strategies. Finally, the RECMO strategy requires less model training time than the DIRMO strategy and consumes the least amount of training time among five forecasting strategies.

Nonlinear system PID-type multi-step predictive control

A compound neural network was constructed during the process of identification and multi-step prediction. Under the PID-type long-range predictive cost function, the control signal was calculated based on gradient algorithm. The nonlinear controller’s structure was similar to the conventional PID controller. The parameters of this controller were tuned by using a local recurrent neural network on-line. The controller has a better effect than the conventional PID controller. Simulation study shows the effectiveness and good performance.

Combinative Algorithms for the Multidisciplinary Collaborative Simulation of Complex Mechatronic Products Based on Major Step and Convergent Integration Step

Multidisciplinary collaborative simulation （MCS） is an important area of research in the domain of multidisciplinary design optimization （MDO）.Although previous research for MCS has to some extent addressed some issues like using of multiple tools,integration stability,control of step size,data synchronization,etc,further work is still necessary to study how to achieve improved precision.A theoretical model is formulated to describe and analyze the integration process of MCS.A basic algorithm with equal major steps is proposed based on the model,along with two methods of implementation for the model,namely the serial method and the parallel method.A further algorithm based on convergent integration step is proposed,which has a more flexible strategy for run-time integration.The influence of interpolation techniques on simulation performance is studied as well.Simulations of the performance of various algorithms with different interpolation techniques are performed for both a simple numerical example and a complex mechatronic product.The novel algorithm based on convergent integration step,when used with a high-order interpolation technique,has better performance in terms of precision and efficiency.The innovation of this paper is mainly on the validation of high precision of the proposed convergent integration step algorithm.