Wavelet Support Vector Machine Research Articles

Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine

Fault diagnosis for wind turbine transmission systems is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine transmission systems. In this paper, a novel fault diagnosis method based on manifold learning and Shannon wavelet support vector machine is proposed for wind turbine transmission systems. Firstly, mixed-domain features are extracted to construct a high-dimensional feature set characterizing the properties of non-stationary vibration signals from wind turbine transmission systems. Moreover, an effective manifold learning algorithm with non-linear dimensionality reduction capability, orthogonal neighborhood preserving embedding (ONPE), is applied to compress the high-dimensional feature set into low-dimensional eigenvectors. Finally, the low-dimensional eigenvectors are inputted into a Shannon wavelet support vector machine (SWSVM) to recognize faults. The performance of the proposed method was proved by successful fault diagnosis application in a wind turbine's gearbox. The application results indicated that the proposed method improved the accuracy of fault diagnosis.

LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information

Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM.

Soft Sensing Based on Hilbert-Huang Transform and Wavelet Support Vector Machine

At present, much more soft sensing have been widely used in industrial process control to improve the quality of product and assure safety in production. A novel method using Hilbert-Huang transform(HHT) combined with wavelet support vector machine(WSVM) is put forward.Firstly the method analyzes the intrinsic mode function (IMF) obtained after the empirical mode decomposition (EMD), then extracts IMF energy feature as the input feature vectors of the wavelet support vector machine. Based on the wavelet analysis and conditions of the support vector kernel function, a novel multi-dimension admissible support vector wavelet kernel function is presented, which is a multidimensional wavelet kernel, thus enhancing the generalization ability of the SVM. The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that HHT-WSVM approach has a better prediction and generalization. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2817 Full Text: PDF

Structure Fault Diagnosis of Tower Crane Based on Wavelet Packet Analysis and Support Vector Machines

First establish a dynamic model of tower crane in the load lifting process, the lifting load is solved under two work conditions.Then establish the FEM(finite element analysis) model of the tower crane under the normal and the damage condition. Get the dynamic displacement of the normal and the damage status under the lifting dynamic load. With wavelet packet decomposition and SVM(Support vector machines) multi-classification algorithm, a multi-fault classifier is constructed, and applied to the fault diagnosis of tower body. The results of the study show that the multi-fault classifier has such advantages as simple algorithm and excellent capability of fault classification, and it can not only diagnose the structural damage status, but also determine the positions of structural damage. This will be a new search on tower crane structural health diagnosis.

Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment

A novel fault diagnosis method using dimension reduction with linear local tangent space alignment is proposed in this paper. With this method, the mixed-domain feature set is first constructed to completely characterize the property of each fault by combining Empirical Mode Decomposition (EMD) with the Autoregression (AR) model coefficients. Then, Linear Local Tangent Space Alignment (LLTSA) is used to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination. By using the tangent space in the neighborhood of a data point to represent the local geometry, and aligning those local tangent spaces in the low-dimensional space (which is linearly mapped from the raw high-dimensional space), LLTSA can not only gain a perfect approximation of low-dimensional intrinsic geometric structure within the high-dimensional observation data, but can also enhance local within-class relations. Finally, the Littlewoods-Paley wavelet support vector machine (LPWSVM) is proposed to perform fault classification with the obtained low-dimensional eigenvectors. Compared with the existing methods, the proposed approach has improved the fault diagnosis precision. The experiments on deep groove ball bearings fault diagnosis demonstrated the advantage and effectiveness of the proposed fault diagnosis approach.

Financial Index Prediction Based on Wavelet Analysis and SVM

The analysis of the time sequence can be two ways in the time domain and frequency domain. But many financial time series exhibit strong non-stationary and long memory, which makes many traditional individually focused on the research and analysis of the time domain or frequency domain method is no longer applicable. In this paper, wavelet analysis and support vector machines for use in the time domain and frequency domain have the ability to characterize the local signal characteristics, location and mutation of the singular points and irregular mutation analysis, these mutations detected the degree of significance.

Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM

This paper presents the establishing of intelligent system for broken-rotor-bar (BRB) diagnosis based on a novel combination of both, stationary wavelet packet transform (SWPT) and multiclass wavelet support vector machines (MWSVM). The SWPT is used for feature extraction under lower sampling rate. In fact, it is demonstrated through experimental results that the use of the lower sampling rate does not affect the performance of SWPT to detect BRB, while requiring much less computation and low cost implementation. The multiclass SVM (MSVM) is used to automatically recognize the faults. Different MSVM strategies are compared with various kernel functions in terms of classification accuracy, training and testing complexity. The classification results show that the wavelet kernel function detects the faulty conditions with a higher accuracy.

Application of Stationary Wavelet Support Vector Machines for the Prediction of UK Economic Recession and Expansion Periods

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches.

A New Wavelet Based SVM Classifier for Wild Fire Detection Using Decision Fusion Framework in Video

There has been an increasing interest in the study of video based fire detection algorithms as video based surveillance systems become widely available for indoor and outdoor monitoring applications. Although many video based smoke-detection algorithms have been developed and applied in various experimental or real life applications, but the standard method for evaluating their quality has not yet been proposed. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. In this project, the wavelet support vector machine (WSVM)-based model is used for Wild fire detection (WFD). Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. The new wavelet kernel is proposed to improve the generalization ability of the support vector machine (SVM). More-over, the proposed model utilizes the principle of wavelet analysis to facilitate nonlinear characteristic extraction of the image data. To reduce misclassification due to fog, an efficient fog removal scheme using adaptive normalization method. Index Terms—Active fusion, wildfire detection using video, Smoke detection, Wavelets Support vector machine, Video processing.

基于小波包和SVM的风机齿轮箱故障诊断方法

基于小波包和SVM的风机齿轮箱故障诊断方法

Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms

The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wavelet transform coefficients and wavelet transform energy distribution constitute feature vectors. These vectors are then trained and tested using SVM multi-class algorithms. Experimental results demonstrate that the SVM multi-class algorithms, which use the Gaussian radial basis function, exponential radial basis function, and hyperbolic tangent function as basis functions, are suitable methods for power quality disturbance classification.

Analog Circuit Fault Diagnosis Based on Morphological Wavelet and Least Squares Support Vector Machine

Analog Circuit Fault Diagnosis Based on Morphological Wavelet and Least Squares Support Vector Machine

Shadow Casting Out Of Plane (SCOOP) Candidates for Human and Vehicle Detection in Aerial Imagery

In this paper, we propose a method for detecting humans and vehicles in imagery taken from a UAV. This is a challenging problem due to a limited number of pixels on target, which makes it more difficult to distinguish objects from background clutter, and results in much larger search space. We propose a method for constraining the search based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of ground plane normal, the orientation of shadows cast by out of plane objects in the scene, and the relationship between object heights and the size of their corresponding shadows. We use the aforementioned information in a geometry-based shadow, and ground-plane normal blob detector, which provides an initial estimation for locations of shadow casting out of plane (SCOOP) objects in the scene. These SCOOP candidate locations are then classified as either human or clutter using a combination of wavelet features and a Support Vector Machine. To detect vehicles, we similarly find potential vehicle candidates by combining SCOOP and inverted-SCOOP candidates and then classify them using wavelet features and SVM. Our method works on a single frame, and unlike motion detection based methods, it bypasses the entire pipeline of registration, motion detection, and tracking. This method allows for detection of stationary and slowly moving humans and vehicles while avoiding the search across the entire image, allowing accurate and fast localization. We show impressive results on sequences from VIVID and CLIF datasets and provide comparative analysis.

Classification of Green Bristle Grass, Yellow Foxtail and Chinese Pennisetum Seeds via HATR-FT-IR Combined with Chemometrics

Fourier transform infrared (FT-IR) and horizontal attenuated total reflectance (HATR) technique are used to obtain the FT-IR spectra of the seed of green bristle grass (the seed fromSetaria viridis(L.) Beauv), yellow foxtail seed (the seed fromSetaria glauca(L.) Beauv), and the Chinese pennisetum seed (the seed fromSetaria faberiiHerrum). In order to extrude the difference among them, cluster analysis is considered to identify the three kinds of plant seeds. Because they belong to the sibling plant seeds, and have similar chemical components and close FT-IR spectra. The result of Cluster analysis is not satisfactory. The discrete wavelet transformation (DWT) and a support vector machine (SVM) were used for further study. The compression detail 3 and 4 in DWT are used to extract the feature vectors, which are used to train SVM. The trained SVM is used to classify seed of green bristle grass, yellow foxtail seed and Chinese pennisetum seed. The seed samples are collected from different places around the country. With 40 testing samples we could effectively identify the sibling plants, seed of green bristle grass, yellow foxtail seed and Chinese pennisetum seed by FT-IR with discrete wavelet feature extraction and SVM classification.

Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately is very important to ensure the reliable operation of rotating machinery. In this paper, a multi-fault classification model based on the kernel method of support vector machines (SVM) and wavelet frame, wavelet basis were introduced to construct the kernel function of SVM, and wavelet support vector machine (WSVM) is presented. To seek the optimal parameters of WSVM, particle swarm optimization (PSO) is applied to optimize unknown parameters of WSVM. In this work, the vibration signals measured from rolling element bearings are preprocessed using empirical model decomposition (EMD). Moreover, a distance evaluation technique is performed to remove the redundant and irrelevant information and select the salient features for the classification process. Hence, a relatively new hybrid intelligent fault detection and classification method based on EMD, distance evaluation technique and WSVM with PSO is proposed. This method is validated on a rolling element bearing test bench and then applied to the bearing fault diagnosis for electric locomotives. Compared with the commonly used SVM, the WSVM can achieve a greater accuracy. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on the vibration signals.

A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

A novel intelligent diagnosis model based on wavelet support vector machine (WSVM) and immune genetic algorithm (IGA) for gearbox fault diagnosis is proposed. Wavelet support vector machine is a powerful novel tool for solving the diagnosis problem with small sampling, nonlinearity and high dimension. Immune genetic algorithm is developed in this study to determine the optimal parameters for WSVM with the highest accuracy and generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by empirical mode decomposition (EMD). The experimental results indicate that this proposed approach is an effective method for gearbox fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the artificial neural network and the SVM which has randomly extracted parameters.

Precipitation Forecast Based on WA-SVM Theory

In order to improve the precision of medium-long term rainfall forecast, the rainfall estimation model was set up based on wavelet analysis and support vector machine (WA-SVM). It decomposed the original rainfall series to different layers through wavelet analysis, forecasted each layer by means of SVM, and finally obtained the forecast results of the original time series by composition. The model was used to estimate the monthly rainfall sequence in the watershed. Comparing with other method which only uses support vector machine(SVM), it indicates that the estimated accuracy was improved obviously.

3D ultrasound image segmentation using wavelet support vector machines.

Transrectal ultrasound (TRUS) imaging is clinically used in prostate biopsy and therapy. Segmentation of the prostate on TRUS images has many applications. In this study, a three-dimensional (3D) segmentation method for TRUS images of the prostate is presented for 3D ultrasound-guided biopsy. This segmentation method utilizes a statistical shape, texture information, and intensity profiles. A set of wavelet support vector machines (W-SVMs) is applied to the images at various subregions of the prostate. The W-SVMs are trained to adaptively capture the features of the ultrasound images in order to differentiate the prostate and nonprostate tissue. This method consists of a set of wavelet transforms for extraction of prostate texture features and a kernel-based support vector machine to classify the textures. The voxels around the surface of the prostate are labeled in sagittal, coronal, and transverse planes. The weight functions are defined for each labeled voxel on each plane and on the model at each region. In the 3D segmentation procedure, the intensity profiles around the boundary between the tentatively labeled prostate and nonprostate tissue are compared to the prostate model. Consequently, the surfaces are modified based on the model intensity profiles. The segmented prostate is updated and compared to the shape model. These two steps are repeated until they converge. Manual segmentation of the prostate serves as the gold standard and a variety of methods are used to evaluate the performance of the segmentation method. The results from 40 TRUS image volumes of 20 patients show that the Dice overlap ratio is 90.3% ± 2.3% and that the sensitivity is 87.7% ± 4.9%. The proposed method provides a useful tool in our 3D ultrasound image-guided prostate biopsy and can also be applied to other applications in the prostate.

Residual Subsidence Prediction of Abandoned Mine Goaf Based on Wavelet Support Vector Machines

The prediction of residual subsidence is the fundament of stability evaluation of buildings foundation in the abandoned mine goaf, so how to get the residual subsidence with high precision is significant to reclaim the goaf for buildings. In this paper, a novel prediction model named wavelet support vector machines (WT-SVM) is proposed to forecast residual subsidence. Aiming at the stochastic fluctuation of the subsidence series, the measured data of residual subsidence were separated into components, namely, trend, oscillating sequence and stochastic signal, via wavelet multi-resolution analysis; then, the prediction model was established based on SVM regression algorithm, respectively, and the sum of the total corresponding prediction values were regarded as the final results of the residual subsidence. The predicting results of WT-SVM, SVM and BP neural network (BP-NN) were compared by a case study. The conclusions are as follows: WT-SVM model is obviously superior to other models in terms of the aspects of prediction precision, step and stability, which indicates the feasibility and effectivity of WT-SVM in predicting residual subsidence of the abandoned mine goaf.

Mass Lesion Detection Using Wavelet Decomposition Transform and Support Vector Machine

This paper describes the ongoing efforts by the author to provide efficient and accurate classification for mass lesions in mammogram images. A study of the characteristics of true masses compared to the falsely detected masses is carried out using wavelet decomposition transform combining with support vector machine (SVM). In this approach, four main wavelet features are extracted from different regions of interest in order to distinguish between TP and FP detected regions. A study of detecting regions of interest, extracting the wavelet features and choosing the optimal learning parameters for support vector machine are also presented in this paper. The combined between the wavelet features and SVM presented here can successfully reduces the FP ratio to 0.05 clusters/image, with accurate TP ratio 94%.