Wavelet Packet Subband Research Articles

Fault Diagnosis of Commutation Failure Using Wavelet Transform and Wavelet Neural Network in HVDC Transmission System

Commutation failure is the most common fault on the inverter side of the high voltage direct current (HVDC) transmission system. In the actual engineering operation, there are many reasons for the commutation failure. In order to diagnose the causes in real-time and effectively, this article proposes a new method for the fault diagnosis of the commutation failure based on the combination of the wavelet transform and wavelet neural network (WNN). First, by analyzing the changes in electrical characteristics caused by the failure of commutation, the voltage signal of the inverter side commutation bus is finally selected as the feature signal for diagnosis. Then, the wavelet transform is used as a tool for extracting fault feature quantities, and the root mean square (rms) value of the wavelet detail coefficients, wavelet energy skewness, and wavelet energy spectrum information entropy of the inverter side commutation bus voltage signal after wavelet decomposition are used as three feature parameters. At the same time, wavelet packet decomposition is performed on the inverter side commutation bus voltage signal, and the wavelet packet subband energy, energy entropy, and energy skewness are extracted as the other three feature vectors. Finally, the simulation test data shows that the proposed fault diagnosis method can accurately identify the specific fault causes and has high reference value and specific engineering guiding significance for improving the reliability of the HVDC transmission system. Compared with the traditional BP neural network method for fault diagnosis of commutation failure, the proposed diagnosis method considers the fault type more comprehensively, the diagnosis speed is faster, and the recognition accuracy rate is higher in the case of a small number of samples.

Fault Diagnosis of Hydraulic Seal Wear and Internal Leakage Using Wavelets and Wavelet Neural Network

The piston seal wear in hydraulic cylinder is one of the main factors that give rise to an internal leakage. This paper focuses on diagnosing piston seal wear and subsequent internal leakage from a double acting seal combination seal used in the support oil cylinder of a QY110 mobile crane. Wavelet transform is applied as a feature extractor to transform the raw oil pressure data into a feature vector consisting of wavelet packet subband energy, energy entropy, energy variance, and root mean square of the wavelet detailed coefficient $d_{4}$ . This feature vector feeds into the wavelet neural network serving as a pattern recognizer for automatically classifying the fault patterns. We demonstrate with the leakage experiment and simulation data that the proposed fault detection and identification (FDI) scheme is capable of effectively detecting and classifying the piston seal wear with excellent accuracy. Our comparison studies reveal that the proposed FDI tandem produces much more accurate result than that from back-propagation neural network. This paper is supplement to and enrichment of existing studies on fault simulation and diagnosis associated with hydraulic cylinder leakage problems.

Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis.

Kurtogram can adaptively select the resonant frequency band, and then the characteristic fault frequency can be obtained by analyzing the selected band. However, the kurtogram is easily affected by random impulses and noise. In recent years, improvements to kurtogram have been concentrated on two aspects: (a) the decomposition method of the frequency band; and (b) the selection index of the optimal frequency band. In this article, a new method called Teager Energy Entropy Ratio Gram (TEERgram) is proposed. The TEER algorithm takes the wavelet packet transform (WPT) as the signal frequency band decomposition method, which can adaptively segment the frequency band and control the noise. At the same time, Teager Energy Entropy Ratio (TEER) is proposed as a computing index for wavelet packet subbands. WPT has better decomposition properties than traditional finite impulse response (FIR) filtering and Fourier decomposition in the kurtogram algorithm. At the same time, TEER has better performance than the envelope spectrum or even the square envelope spectrum. Therefore, the TEERgram method can accurately identify the resonant frequency band under strong background noise. The effectiveness of the proposed method is verified by simulation and experimental analysis.

Shannon Entropy of Binary Wavelet Packet Subbands and Its Application in Bearing Fault Extraction.

The fast spectrum kurtosis (FSK) algorithm can adaptively identify and select the resonant frequency band and extract the fault feature via the envelope demodulation method. However, the FSK method has some limitations due to its susceptibility to noise and random knocks. To overcome this shortage, a new method is proposed in this paper. Firstly, we use the binary wavelet packet transform (BWPT) instead of the finite impulse response (FIR) filter bank as the frequency band segmentation method. Following this, the Shannon entropy of each frequency band is calculated. The appropriate center frequency and bandwidth are chosen for filtering by using the inverse of the Shannon entropy as the index. Finally, the envelope spectrum of the filtered signal is analyzed and the faulty feature information is obtained from the envelope spectrum. Through simulation and experimental verification, we found that Shannon entropy is—to some extent—better than kurtosis as a frequency-selective index, and that the Shannon entropy of the binary wavelet packet transform method is more accurate for fault feature extraction.

A high payload steganography mechanism based on wavelet packet transformation and neutrosophic set

In this paper a steganographic method is proposed to improve the capacity of the hidden secret data and to provide an imperceptible stego-image quality. The proposed steganography algorithm is based on the wavelet packet decomposition (WPD) and neutrosophic set. First, an original image is decomposed into wavelet packet coefficients. Second, the generalized parent–child relationships of spatial orientation trees for wavelet packet decomposition are established among the wavelet packet subbands. An edge detector based on the neutrosophic set named (NSED) is then introduced and applied on a number of subbands. This leads to classify each wavelet packet tree into edge/non-edge tree to embed more secret bits into the coefficients in the edge tree than those in the non-edge tree. The embedding is done based on the least significant bit substitution scheme. Experimental results demonstrate that the proposed method achieves higher embedding capacity with better imperceptibility compared to the published steganographic methods.

ECG compression method based on adaptive quantization of main wavelet packet subbands

In this study, a new compression algorithm for ECG signal is proposed based on selecting important subbands of wavelet packet transform (WPT) and applying subband-dependent quantization algorithm. To this end, first WPT was applied on ECG signal and then more important subbands are selected according to their Shannon entropy. In the next step, content-based quantization and denoising method are applied to the coefficients of the selected subbands. Finally, arithmetic coding is employed to produce compressed data. The performance of the proposed compression method is evaluated using compression rate (CR), percentage root-mean-square difference (PRD) as signal distortion, and wavelet energy-based diagnostic distortion (WEDD) as diagnostic distortion measures on MIT-BIH Arrhythmia database. The average CR of the proposed method is 29.1, its average PRD is <2.9 % and WEDD is <3.2 %. These results demonstrated that the proposed method has a good performance compared to the state-of-the-art compression algorithms.

Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases

The paper addresses image feature characterization and the structuring of large and heterogeneous image databases through the stochasticity or randomness appearance. Measuring stochasticity involves finding suitable representations that can significantly reduce statistical dependencies of any order. Wavelet packet representations provide such a framework for a large class of stochastic processes through an appropriate dictionary of parametric models. From this dictionary and the Kolmogorov stochasticity index, the paper proposes semantic stochasticity templates upon wavelet packet sub-bands in order to provide high level classification and content-based image retrieval. The approach is shown to be relevant for texture images.

HWVP: hierarchical wavelet packet descriptors and their applications in scene categorization and semantic concept retrieval

Wavelet packet transform is an effective texture analysis approach by sub-band filtering. Different texture patterns have distinctive responses to the sub-bands of wavelet packets. The responses are valuable for texture description. Utilizing all the responses of the sub-bands of different resolutions can improve texture pattern discrimination power. In this paper, effective texture descriptors based on hierarchical wavelet packet (HWVP) transform are proposed. The subtle sub-bands of wavelet packet transform improve the discrimination power of HWVP descriptors for the images in different categories. Scene categorization performances of the HWVP descriptors under various decomposition levels and wavelet bases are discussed. Performances of HWVP descriptors of global and local images with different partition patterns are also analyzed. The advantages of HWVP descriptors attribute to the following two aspects. Firstly sub-band filtering is helpful for improving the discrimination power of HWVP descriptors to capture the subtle differences of texture patterns. Secondly hierarchical feature representation makes the HWVP descriptors robust to resolution variations. Comparisons are made with some existing robust descriptors on scene categorization and semantic concept retrieval. Experimental results on the widely used OT, Scene-13, Sport Event, and TRECVID 2007 datasets show the effectiveness of the proposed HWVP descriptors.

Near-field Sub-band Beamforming for Damage Detection in Bridges

Traditional structural health monitoring techniques based on the vibration response of bridge structures are limited because of several factors — including a poorly formed aggregate system model, very low SNR, and unrealistic boundary conditions. Many times, these methods rely on global parameters to describe the dynamic behavior of local structural elements. In this paper, we proposed a novel efficient SHM technique that employs the use of compactly supported sub-band space/ frequency and time/frequency analysis using local vibration characteristics. To overcome the problem of the low-error sensitivity of features extracted from vibration signals, a near-field adaptive beamforming approach was used. This technique allows the sensor array to `scan' local portions of the structure, resulting in accurate spatial selectivity on the array and high signal-to-noise ratio for any given scan direction. Moreover, the sensor array was in direct contact with the vibrating structure, and thus the measured source is in the near-field of the array. Therefore, compensation of the sensor output was needed. Sub-band analysis and adaptive beamforming were integrated in a wavelet packet sub-band framework. We utilized the 3D energy map of the optimized sub-band signals for any given scan direction and for each sub-band center frequency as the damage detection feature. We validated our method using classical finite elements approximation to the dynamic behavior of the system. The comparison of a simulated undamaged simply supported beam with a damaged equivalent revealed that damage is localized in frequency and aligned with the direction of the simulated damage. The energy signature comparison after the beamforming stage validates these results, localizing the damage in areas of high probability around the direction of the damage. Automatic analysis of the energy comparison map was possible using a pseudo density estimation of the damage location, allowing for an automatic damage detection procedure. The focus of our research was aimed toward the adaptation of this damage detection method to real highway bridges.

A wavelet packet based block-partitioning image coding algorithm with rate-distortion optimization

As an elegant generalization of wavelet transform, wavelet packet (WP) provides an effective representation tool for adaptive waveform analysis. Recent work shows that image-coding methods based on WP decomposition can achieve significant gain over those based on a usual wavelet transform. However, most of the work adopts a tree-structured quantization scheme, which is a successful technique for wavelet image coding, but not appropriate for WP subbands. This paper presents an image-coding algorithm based on a rate-distortion optimized wavelet packet decomposition and on an intraband block-partitioning scheme. By encoding each WP subband separately with the block-partitioning algorithm and the JPEG2000 context modeling, the proposed algorithm naturally avoids the difficulty in defining parent-offspring relationships for the WP coefficients, which has to be faced when adopting the tree-structured quantization scheme. The experimental results show that the proposed algorithm significantly outperforms SPIHT and JPEG2000 schemes and also surpasses state-of-the-art WP image coding algorithms, in terms of both PSNR and visual quality.

A noise robust feature extraction algorithm using joint wavelet packet subband decomposition and AR modeling of speech signals

This paper presents a noise robust feature extraction algorithm NRFE using joint wavelet packet decomposition (WPD) and autoregressive (AR) modeling of a speech signal. In opposition to the short time Fourier transform (STFT)-based time–frequency signal representation, wavelet packet decomposition can lead to better representation of non-stationary parts of the speech signal (e.g. consonants). The vowels are well described with an AR model as in LPC analysis. The proposed Root-Log compression scheme is used to perform the computation of the wavelet packet parameters. The separately extracted WPD and AR-based parameters are combined together and then transformed with the usage of linear discriminant analysis (LDA) to finally produce a lower dimensional output feature vector. The noise robustness is improved with the application of proposed wavelet-based denoising algorithm with a modified soft thresholding procedure and time–frequency adaptive threshold. The proposed voice activity detector based on a skewness-to-kurtosis ratio of the LPC residual signal is used to effectively perform a frame-dropping principle. The speech recognition results achieved on Aurora 2 and Aurora 3 databases show overall performance improvement of 44.7% and 48.2% relative to the baseline MFCC front-end, respectively.

Information-theoretic wavelet packet subband selection for texture classification

Wavelet packet decomposition has been successfully applied to image analysis and classification. The most common approach for wavelet packet-based texture classification is to decompose texture images with wavelet packet transform and to extract energy values for all subbands as features for the subsequent classification. Due to the overcomplete representation provided by the wavelet packet transform, it is suitable to select a set of subbands for sparse representation of the texture for classification. For better classification results, it is desired that the energy features corresponding to the selected subbands are as independent from each other as possible. However, most of the current subband selection methods do not take the dependence between energy values from different subbands into account. In this paper, we investigate the dependence between energy values from different subbands, which may be from the same wavelet basis, or from different wavelet bases. Based on the theoretical analysis and simulation, we propose an information-theoretic measure, mutual information, for selecting subbands for sparse representation of textures for classification. Experimental results show that the proposed method yields a sparse representation of the textures and achieves lower classification error rates than the conventional methods, simultaneously.

Optimum bit allocation in subband coding with nonideal reconstruction filters

Subband perceptual audio coding aims at providing the best subjective quality by decomposing the input signal into adjacent subbands. Every frequency component is then separately coded with a different number of bits in order to properly shape the quantization noise density power spectrum. In this letter, an optimum bit allocation algorithm is presented. As well as being applicable to either uniform or nonuniform frequency decompositions, it allows us to take into account the nonideal selectivity of the reconstruction filters. The effects of leakage for both short and long FIR filters have been investigated referring to a wavelet packet subband decomposition.

Wavelet image coding using trellis coded space-frequency quantization

The progress in wavelet image coding have brought the field into its maturity. Major developments in the process are rate-distortion (R-D) based wavelet packet transformation, zerotree quantization, subband classification and trellis-coded quantization, and sophisticated context modeling in entropy coding. Drawing from past experience and recent in sights, we propose a new wavelet image coding technique with trellis coded space-frequency quantization (TCSFQ). TCSFQ aims to explore space-frequency characterizations of wavelet image representations via R-D optimized zerotree pruning, trellis-coded quantization, and context modeling in entropy coding. Experiments indicate that the TCSFQ coder achieves twice as much compression as the baseline JPEG coder does at the same peak signal to noise ratio (PSNR), making it better than all other coders described in the literature.

Data compression, data fusion and Kalman filtering in wavelet packet sub-bands of a multisensor tracking system

In a multisensor target tracking system in which centralised Kalman filtering is employed, the amount of data to be transmitted from the sensors to the central processor demands a huge communication cost. In addition, the amount of data received by the central processor further imposes an enormous computational load on the Kalman filter. The authors propose the use of wavelet packet decomposition on the observed data vectors so that the insignificant sub-band components may be suppressed before being transmitted resulting in the saving of communication cost. Furthermore, optimum fusion is applied to the sub-band components before the data vectors are reconstructed. Two tracking schemes, TRCD and TSCD, are proposed. In TRCD, Kalman filtering is applied to the reconstructed data vector, whereas in TCSD, Kalman filtering is only applied to the components in the dominant sub-band of the decomposition. Simulation results show that, in terms of communications and/or computation economy, these two schemes offer attractive alternatives without over-sacrificing performance.