Aviation Safety Evaluation by Wavelet Kernel-Based Support Vector Machine

In order to obtain the excellent evaluation effects, the wavelet kernel function is used as the kernel function of support vector machine,and the model is defined as wavelet kernel-based support vector machine.Thus, wavelet kernel-based support vector machine is applied to aviation safety evaluation. The two dimensional input vector of the training samples is employed to construct the training samples.The traditional radial basis function kernel-based support vector machine is used to compare with the wavelet kernel-based support vector machine.The testing results show that the evaluation error of the wavelet kernel-based support vector machine belongs to the range from 0.015 to 0.04,and the evaluation error of the traditional radial basis function kernel-based support vector machine belongs to the range from 0.02 to 0.07.Then,we can conclude that aviation accidents evaluation accuracy of wavelet kernel-based support vector machine is higher than those of traditional radial basis function kernel-based support vector machine

Fault diagnosis of gearboxes using wavelet support vector machine, least square support vector machine and wavelet packet transform

This work focuses on a method which experimentally recognizes faults of gearboxes using wavelet packet and two support vector machine models. Two wavelet selection criteria are used. Some statistical features of wavelet packet coefficients of vibration signals are selected. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Eventually, the gearbox faults are classified using these statistical features as input to least square support vector machine (LSSVM) and wavelet support vector machine (WSVM). Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of fault classification demonstrate that the WSVM identified the fault categories of gearbox more accurately and has a better diagnosis performance as compared to the LSSVM

Rotor fault diagnosis based on wavelet packet energy spectrum and adaptive fuzzy weighted support vector machine

In this study, a novel application of a wavelet packet energy-weighted support vector machine (WPE-WSVM) is proposed to perform fault classification of helicopter rotor. Because the helicopter rotor fault signal is weak, it is difficult to extract fault feature. The wavelet package is adopted to decompose the vibration signals on the fuselage into different frequency bands, and to eliminate the noise. And then single signal was reconstructed to extract the energy in each frequency band serving as fault feature vectors. And support vector machine was applied for classifying the failure mode of the helicopter rotor. For classification task support vector machine is used due to its good robustness and generalization performances. But the classification accuracy of standard support vector machine is relative slow when the number of samples of different classes is dramatically different. So a fuzzy weighted support vector machine was proposed, which added weight coefficient to samples of different classes. A comparative analysis of standard support vector machine and proposed fuzzy weighted support vector machine is done. The proposed fuzzy weighted support vector machine improved the classification accuracy of class with fewer samples. The proposed method is sufficiently accurate, fast, and robust, which makes it suitable for use in helicopter rotor fault diagnosis applications

Wavelet-support vector machine for forecasting palm oil price

This study examines the feasibility of applying Wavelet-Support Vector Machine (W-SVM) model in forecasting palm oil price. The conjunction method wavelet-support vector machine (W-SVM) is obtained by the integration of discrete wavelet transform (DWT) method and support vector machine (SVM). In W-SVM model, wavelet transform is used to decompose data series into two parts; approximation series and details series. This decomposed series were then used as the input to the SVM model to forecast the palm oil price. This study also utilizes the application of partial correlation-based input variable selection as the preprocessing steps in determining the best input to the model. The performance of the W-SVM model was then compared with the classical SVM model and also artificial neural network (ANN) model. The empirical result shows that the addition of wavelet technique in W-SVM model enhances the forecasting performance of classical SVM and performs better than ANN

Efficient Item Image Retrieval System

Content based image retrieval system is a very effective means of searching and retrieving similar images from large database. This method is faster and easy to implement compare to text based image retrieval method. Ability to extract discriminative low level feature from these images and use them with appropriate classifier is factor in determining retrieval result. In this work efficient item image retrieval system is proposed. The system utilizes Haar wavelet transform, Phase Congruency and Support Vector Machine. Haar wavelet transform acted on image to form four sub-images. Texture feature is extracted from smaller image blocks from detailed bands and it was combined with shape feature from approximation band to form feature vector. Feature distance margin is achieved between query image and images in the database using Support Vector Machine (SVM). The effectiveness of the system is confirmed from output retrieval results

Compressor fault diagnosis based on SVM and GA

Due to growth of mechanization and automation, today’s industrial systems are becoming more complex. A small breakdown of any non-redundant machine component affects the operation of the entire system. Compressors are utilized widely in the oil and chemical industry. Great attention has been paid to the condition monitoring and fault diagnosis of the Compressor by the field engineers and technicians. In this study, an effective and reliable method based on vibration analysis and with signal processing and classification techniques is presented for troubleshooting of a centrifugal Compressor. Among different time – frequency methods, wavelet transformation extracts information about the signal time scale through a series of convolution operation between the measured signals and the basis wavelet which was used as a preprocessing. The used mother wavelet is (db4) in which the original signal is switched to multiple details signals; then it features are taken from pre-processed signals and they were introduced to support vector machines as input. Kernel function used here in the support vector machine is RBF in which the parameters of support vector machine were optimized using Genetic Algorithm for better performance to increase the accuracy of classification. The highest accuracy was obtained as 100 %. The obtained accuracy clearly indicates high safety margin of the multistage centrifugal pump for fault detection

Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes

Fault diagnosis of gearboxes using wavelet support vector machine, least square support vector machine and wavelet packet transform

This work focuses on a method which experimentally recognizes faults of gearboxes using wavelet packet and two support vector machine models. Two wavelet selection criteria are used. Some statistical features of wavelet packet coefficients of vibration signals are selected. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Eventually, the gearbox faults are classified using these statistical features as input to least square support vector machine (LSSVM) and wavelet support vector machine (WSVM). Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of fault classification demonstrate that the WSVM identified the fault categories of gearbox more accurately and has a better diagnosis performance as compared to the LSSVM

Wavelet frame accelerated reduced vector machine for efficient image analysis

We propose a new approach for face and facial feature detection combined with the advantages of the Morphable Model. The presented method reduces the runtime complexity of a Support Vector Machine classifier and the new training algorithm is fast and simple. This is achieved by an Over-Complete Wavelet Transform that finds optimally sparse approximations of the Support Set Vectors. The wavelet-based approach provides an upper bound on the distance between the decision function of the Support Vector Machine and our classifier. The obtained classifier is fast since the used Haar wavelet approximations of the Support Set Vectors allow efficient Integral Image-based kernel evaluations. This provides a set of double-cascaded classifiers of increasing accuracy for an early rejection. The algorithm yields an excellent runtime performance that is achieved by hierarchically discriminating with respect to the number and approximation accuracy of incorporated Reduced Set Vectors. The proposed algorithm is applied to the problem of face and facial feature detection, but it can also be used for other image-based classifications. The algorithm presented, provides a 530-fold speed-up over the Support Vector Machine, enabling face detection at more than 25 fps on a standard PC. Summarizing, we propose very fast and efficient to train classifiers that improve the detection performance by involving the advantages of the Morphable Model. On one hand to improve the fitting algorithm of the Morphable Model by automatic anchor point detection and on the other hand to use the Morphable Model for improving the training by synthetic data sets and to reduced the False Acceptance Rate