Defects Recognition of 3D Braided Composite Based on Dual-Tree Complex Wavelet Packet Transform

Textile-reinforced composites,due to their excellent highstrength-to-low-mass ratio, provide promising alternatives to conventional structural materials in many high-tech sectors. 3D braided composites are a kind of advanced composites reinforced with 3D braided fabrics; the complex nature of 3D braided composites makes the evaluation of the quality of the product very difficult. In this investigation,a defect recognition platform for 3D braided composites evaluation was constructed based on dual-tree complex wavelet packet transform( DT-CWPT) and backpropagation( BP) neural networks. The defects in 3D braided composite materials were probed and detected by an ultrasonic sensing system. DT-CWPT method was used to analyze the ultrasonic scanning pulse signals,and the feature vectors of these signals were extracted into the BP neural networks as samples. The type of defects was identified and recognized with the characteristic ultrasonic wave spectra. The position of defects for the test samples can be determined at the same time. This method would have great potential to evaluate the quality of 3D braided composites.

Implementation of Wavelet Packet Transform for Detection and Analysis of Stator Faults in Induction Machine

Execution of an online detection technique for induction motor fault diagnosis and research at the current period of time is discussed in this paper. Wavelet packets transform (WPT)-based algorithm is used by the detection method for investigating and identification of many disruptions that happen in three-phase induction motors. The association of the coefficients of the WPT of line currents with the help of a main wavelet at the secondary level of resolution with a threshold discovered through an experiment at the time of the vital position can used to observe the motor reference point. The propagation of wavelet analysis and disintegration of the signal into an equivalent bandwidth which can attain a good disintegration of the solution than what wavelet analysis do is called as Wavelet packet analysis. In order to overcome accidental failing, the on-line fault diagnostics technology for the reduction of incipient errors is a must.

FEATURE EXTRACTION OF VIBRATION SIGNALS BASED ON WAVELET PACKET TRANSFORM

A method is proposed for the analysis of vibration signals from components of rotating machines, based on the wavelet packet transformation (WPT) and the underlying physical concepts of modulation mechanism.The method provides a finer analysis and better time-frequency localization capabilities than any other analysis methods.Both details and approximations are split into finer components and result in better-localized frequency ranges corresponding to each node of a wavelet packet tree.For the purpose of feature extraction, a hard threshold is given and the energy of the coefficients above the threshold is used, as a criterion for the selection of the best vector.The feature extraction of a vibration signal is accomplished by computing the reconstruction signal and its spectrum.When applied to a rolling bear vibration signal feature extraction, the proposed method can lead to be very effective.

Ignition Pattern Analysis for Automotive Engine Trouble Diagnosis Using Wavelet Packet Transform and Support Vector Machines

Engine spark ignition is an important source for diagnosis of engine faults.Based on the waveform of the ignition pattern,a mechanic can guess what may be the potential malfunctioning parts of an engine with his/her experience and handbooks.However,this manual diagnostic method is imprecise because many spark ignition patterns are very similar.Therefore,a diagnosis needs many trials to identify the malfunctioning parts.Meanwhile the mechanic needs to disassemble and assemble the engine parts for verification.To tackle this problem,an intelligent diagnosis system was established based on ignition patterns.First,the captured patterns were normalized and compressed.Then wavelet packet transform(WPT) was employed to extract the representative features of the ignition patterns.Finally,a classification system was constructed by using multi-class support vector machines(SVM) and the extracted features.The classification system can intelligently classify the most likely engine fault so as to reduce the number of diagnosis trials.Experimental results show that SVM produces higher diagnosis accuracy than the traditional multilayer feedforward neural network.This is the first trial on the combination of WPT and SVM to analyze ignition patterns and diagnose automotive engines.

Damage Detection Methods for Offshore Platforms Based on Wavelet Packet Transform

The wavelet packet transform is used for the damage detection of offshore platforms. When some damage occurs, the dynamic response parameters of the structure will shift subtly. However, in some cases, the dynamic parameters, such as dynamic response, are not sensitive, and it is very difficult to predict the existence of damage. The present paper aims to describe how to find small damage by the use of wavelet packet transform. As the wavelet packet transform can be used to quickly find the singularity of the response signal on different scales, the acceleration signal of a damaged offshore platform in the time domain is transformed through the wavelet packet. Experimental results show that the Daubechies 4 wavelet transform can be used to detect damage.

Motion Classification of EMG Signals Based on Wavelet Packet Transform and LS-SVMs Ensemble

This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform (WPT) is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines (SVMs) are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

A NOVEL ALGORITHM OF MULTI-SENSOR IMAGE FUSION BASED ON WAVELET PACKET TRANSFORM

In order to enhance the image information from multi-sensor and to improve the abilities of the information analysis and the feature extraction, this letter proposed a new fusion approach in pixel level by means of the Wavelet Packet Transform (WPT). The WPT is able to decompose an image into low frequency band and high frequency band in higher scale. It offers a more precise method for image analysis than Wavelet Transform (WT). Firstly, the proposed approach employs HIS (Hue, Intensity, Saturation) transform to obtain the intensity component of CBERS (China-Brazil Earth Resource Satellite) multi-spectral image. Then WPT transform is employed to decompose the intensity component and SPOT (Systeme Pour I'Observation de la Therre ) image into low frequency band and high frequency band in three levels. Next, two high frequency coefficients and low frequency coefficients of the images are combined by linear weighting strategies. Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approach can fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM (Histogram Matched)-based fusion algorithm and WT-based fusion approach.

Multicomponent Kinetic Determination by Wavelet Packet Transform Based Elman Recurrent Neural Network Method

This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. The Elman recurrent network was applied to non-linear multivariate calibration. In this case, by means of optimization, the wavelet function, decomposition level and number of hidden nodes for WPTERNN method were selected as D4, 5 and 5 respectively. A program PWPTERNN was designed to perform multicomponent kinetic determination. The relative standard error of prediction(RSEP) for all the components with WPTERNN, Elman RNN and PLS were 3.23%, 11.8% and 10.9% respectively. The experimental results show that the method is better than the others.

Seismic signal analysis based on the dual-tree complex wavelet packet transform

We tried to apply the dual-tree complex wavelet packet transform in seismic signal analysis. The complex wavelet packet transform (CWPT) combine the merits of real wavelet packet transform with that of complex continuous wavelet transform (CCWT). It can not only pick up the phase information of signal, but also produce better ″focal- izing″ function if it matches the phase spectrum of signals analyzed. We here described the dual-tree CWPT algo- rithm, and gave the examples of simulation and actual seismic signals analysis. As shown by our results, the dual-tree CWPT is a very effective method in analyzing seismic signals with non-linear phase.

Impulse Response Identification Based on Varying Scale Orthogonal Wavelet Packet Transform

In this paper, by applying a group of specific orthogonal wavelet packet to Eykho?algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPTis provided. In comparison to Eykho? algorithm, the new algorithm has better practicability andwider application range. Simulation results show that the proposed impulse response identificationalgorithm can be applied to both deterministic and random systems, and is of higher identificationprecision, stronger anti-noise interference ability and better system dynamic tracking property.

OFDM Scheme Based on Wavelet Packet Transform-Oriented Graded Multi-Service

In this paper, a concept of image grading transmission is put forward to enhance data rate and to improve the usage of subcarriers in orthogonal frequency division multiplexing (OFDM). The idea originates from the wavelet packets representative of an image in which information is graded in terms of different priorities. The graded image facilitates more efficient use of adaptive subcarriers and bits allocation. The results of simulation in typical mobile environment prove that the output signal noise ratio (SNR) of the graded image excels that of the ungraded image by 1—2 dB under the same channel condition.

A method to compress vibration signals using wavelet packet transformation combined with sub-band vector quantization

A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.

Performance comparison of neural network training methods based on wavelet packet transform for classification of five mental tasks

In this study, performances comparison to discriminate five mental states of five artificial neural network (ANN) training methods were investigated. Wavelet Packet Transform (WPT) was used for feature extraction of the relevant frequency bands from raw electroencephalogram (EEG) signals. The five ANN training methods used were (a) Gradient Descent Back Propagation (b) Levenberg-Marquardt (c) Resilient Back Propagation (d) Conjugate Learning Gradient Back Propagation and (e) Gradient Descent Back Propagation with movementum.

Novel Adaptive Beamforming Algorithm Based on Wavelet Packet Transform

An analysis of the received signal of array antennas shows that the received signal has multi-resolution characteristics, and hence the wavelet packet theory can be used to detect the signal. By emplying wavelet packet theory to adaptive beamforming, a wavelet packet transform-based adaptive beamforming algorithm (WP-ABF) is proposed . This WP-ABF algorithm uses wavelet packet transform as the preprocessing, and the wavelet packet transformed signal uses least mean square algorithm to implement the adaptive beamforming. White noise can be wiped off under wavelet packet transform according to the different characteristics of signal and white under the wavelet packet transform. Theoretical analysis and simulations demonstrate that the proposed WP-ABF algorithm converges faster than the conventional adaptive beamforming algorithm and the wavelet transform-based beamforming algorithm. Simulation results also reveal that the convergence of the algorithm relates closely to the wavelet base and series; that is, the algorithm convergence gets better with the increasing of series, and for the same series of wavelet base the convergence gets better with the increasing of regularity.

Structural Damage Identification via Pseudo Strain Energy Density and Wavelet Packet Transform

Based on strain signals,a new time-domain methodology for detecting the beam local damage has been developed.The pseudo strain energy density(PSED) is defined and used to build two major damage indexes,the average pseudo strain energy density(APSED) and the average pseudo strain energy density rate(APSEDR).Probability and mathematical statistics are utilized to derive a standardized damage index.Furthermore,by applying the analytic relation between the strain energy release rate and the stress intensity factor,an analytic solution of crack depth is derived.For the dynamic strain signals,the wavelet packet transform is used to pre-process measured data.Finally,a numerical simulation indicates that this method can effectively identify the damage location and its absolute severity.

A New HFRT Algorithm Based on Maximal Overlap Discrete Wavelet Packet Transformation

The high frequency resonant technique (HFRT) algorithm is a popular technique for fault-detection and is widely applied in mechanism systems and industrial constructions. In this paper, a new HFRT algorithm based on maximal overlap discrete wavelet packet transformation (MODWPT) is developed. By the simulation test for soil embedded pipes fault-detection, it is shown that the performance of newly proposed HFRT algorithms is more sensitive to early defects than the traditional HFRT methods based on the Hilbert transform.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network

In view of the fact that the wavelet packet transform(WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis(PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current(HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data,and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error(SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms,without being affected by the sampling frequency.