Wavelet Packet Algorithm Research Articles

Design of a new wide frequency measurement method for power system

Abstract Aiming at the wide application and complex characteristics of the new power system, this paper proposes a design of broadband measurement method and realizes the application of broadband signal analysis and broadband measurement by improving the wavelet packet algorithm. As a multi-resolution analysis method widely used in the field of signal processing, the wavelet transform is unique in that it can perform local analysis in both time and frequency domains at the same time and has significant advantages in analyzing time-varying non-stationary signals. This paper will start from the algorithm requirements of electronic measurement devices based on the basic theory of wavelet transform, build a wide frequency measurement method, and comprehensively simulate and analyze the test signal. By combining the SVMD (Successive Variational Mode Decomposition) adaptive extraction algorithm with frequency band decomposition, the main sub-harmonic components in the frequency band are accurately extracted and measured, which verifies the feasibility and superiority of the algorithm.

Efficient simulation of spatially correlated non-stationary ground motions by wavelet-packet algorithm and spectral representation method

Efficient simulation of spatially correlated non-stationary ground motions by wavelet-packet algorithm and spectral representation method

Research on the identification method based on energy distribution of adaptive WPD for railway tunnel lining void

ABSTRACT As the main disease of railway tunnels, void disease affects the safe operation of railways. The traditional detection technology recognition void relies on the intuitive characteristics of the signal, there are still problems such as poor void edge recognition capability and low intelligence level. To solve this problem, this study used Comsol software to establish a sound-solid coupling finite element model of the tunnel, extracted the acoustic signals of voids under various conditions, analysed the acoustic signal characteristics under different conditions and proposed the acoustic sensitive frequency band of tunnel lining void. The test model of Partial tunnel lining is established by the method of layering pouring. The wavelet packet algorithm is improved by the maximum cross correlation coefficient, the optimal wavelet basis decomposition algorithm is proposed. The layer number of the wavelet packet decomposition (WPD) algorithm was calculated based on the sensitive frequency band and the energy distribution of the sub-signal is obtained. Then, the energy distribution of the sensitive frequency band is used to estimate the void boundary. The experimental results show that the acoustic frequency band of the tunnel void mainly distributes in the range of 0–10,000 Hz and the sensitive frequency band of the void concentrated around 0–3000 Hz. According to the sampling frequency and the sensitive frequency band of the void, the sub-signal energy distribution of each layer is obtained. The energy distribution of sub-signal 1 has strong void boundary recognition ability. The research results will be beneficial to the damage detection and state assessment of tunnel lining.

A novel lidar signal noise reduction algorithm based on improved deep belief network

To reduce noise in the lidar return signal, an improved deep belief network (DBN) denoising algorithm is proposed in this study. In the traditional implementation process of DBN, a multi-layer fully connected network is realized by stacking restricted Boltzmann machines (RBMs). However, the RBM is an undirected graph model, and there is no clear causal relationship between random variable nodes. The denoising autoencoder (DAE) can avoid this problem and produce field generalization performance by adding random contamination during training and stacking, thereby achieving better performance than the traditional DBN. In this study, a new multi-layer DBN called DADBN is implemented by stacking DAE and RBM. First, the multi-layer DAE is placed in the beginning layer of the network as the primary filter of the signal to provide data dimensionality reduction and feature extraction. Then, the RBM is used as the lower layer, the hidden layer is calculated according to the initial value of the visible layer by the contrast of the divergence algorithm, and the visible layer is reconstructed from the samples of the hidden layer. And the sparse representation penalty is added to the RBM model to solve the assimilation phenomenon of hidden layer nodes in the RBM model. It not only optimizes the log-likelihood function when training data, but also makes the probability of each hidden layer node being activated tend to a minimum value, so as to sparsely activate the sparse hidden layer nodes. The weight matrix W is obtained by computing the results of the hidden layer twice. Finally, the original input signal was reconstructed by Gibbs sampling layer and the reconstructed signal was decoded by the decoder to achieve the purpose of noise reduction. To verify its effectiveness, this method is compared with four other denoising methods: wavelet packet algorithm, complete ensemble empirical modal decomposition (CEEMDAN), wavelet transform and empirical mode decomposition (WT-EMD), and wavelet transform-variational mode decomposition (WT-VMD). The noise reduction effect of DADBN is better than that of the other four methods, thus effectively eliminating complex noise in the lidar detection signal.

Research on Signal Feature Extraction of Natural Gas Pipeline Ball Valve Based on the NWTD-WP Algorithm.

The measured signals of internal leakage detection of the large-diameter pipeline ball valve in natural gas pipeline systems usually contain background noise, which will affect the accuracy of internal leakage detection and sound localization of internal leakage points due to the interference of noise. Aiming at this problem, this paper proposes an NWTD-WP feature extraction algorithm by combining the wavelet packet (WP) algorithm and the improved two-parameter threshold quantization function. The results show that the WP algorithm has a good feature extraction effect on the valve leakage signal, and the improved threshold quantization function can avoid the defects of the traditional soft threshold function and hard threshold function, such as discontinuity and the pseudo-Gibbs phenomenon, when reconstructing the signal. The NWTD-WP algorithm is effective in extracting the features of the measured signals with low signal/noise ratio. The denoise effect is much better than that of the traditional soft and hard threshold quantization functions. It proved that the NWTD-WP algorithm can be used for studying the existing safety valve leakage vibration signals in the laboratory and the internal leakage signals of the scaled-down model of the large-diameter pipeline's ball valve.

Concrete implantable bar enabled smart sensing technology for structural health monitoring

This work proposes a concrete implantable bar (CIB) enabled smart sensing technology for the detection of concrete beam damage. Instead of the conventional technique that posts the sensors on the host structure externally or embeds them inside the concrete before casting, this developed technology enables an implantable manner to install specially designed sensors with unique stable and replaceable functions. The CIB integrates a line array of tubular piezoceramic shells (TPSs) that can excite and receive probe signal in concrete. The working principle of this CIB was explained theoretically, and its basic performance regarding the conversion efficiency between the input and output signals was investigated numerically. To assess the structural state comprehensively, a singular value decomposition (SVD)-based wavelet packet algorithm was adopted. This algorithm relied on the classical wavelet packet analysis to construct an energy matrix at a certain frequency band, and then revealed the structural damage degree using the singular values of this matrix. The feasibility of the proposed methods was verified using a reinforced concrete (RC) beam specimen subjected to a four-point bending test, wherein a pair of CIBs, one as an actuator and another as a sensor, were implanted in the beam to sample data from different loading processes. The assessment results using the SVD-based algorithm well characterize the beam state throughout the loading process; and show higher accuracy than those using a convention method in this continuous monitoring case.

Application of Lifting Wavelet Packet Decomposing Algorithm in EMC Simulation of Automobile

The purpose of this paper is to extract data features and denoise the interference excitation source in vehicle electromagnetic compatibility test. The lifting wavelet packet algorithm inherits the multi-resolution characteristics of the classic (first generation) wavelet transform. The transform is only carried out in the time domain, which can achieve in situ operation. It has the advantages of small space occupation, fast transformation speed, easy inversion, etc. It can use energy conservation criteria to extract characteristic energy to identify the conducted interference sources in the vehicle, and the obtained characteristic spectrum is used as the modulation array of the excitation source of the vehicle numerical simulation. In this paper, the collected interference signals are decomposed into lifting wavelet packets, and then the characteristic energy is extracted to identify the conducted interference sources in the vehicle. Signal to noise ratio (SNR), root mean square error (RMSE) and peak error (PE) are used to verify the consistency between the simulation signal and the original signal. The results show that the lifting wavelet packet algorithm has a strong ability to identify the conducted interference sources in the vehicle.

Frequency spectrum and energy refinement characteristics of blasting vibration signals in raw water pipeline tunnel excavation

The analysis of time-frequency variation and energy refinement characteristics of blasting vibration signals has contributed to understanding the propagation law of blasting vibration wave and reducing the possible losses. Combined with the measured data of tunnel blasting excavation and based on the newly constructed wavelet function, the spectrum distribution and energy refinement characteristics of tunnel blasting vibration signals are deeply explored and studied. The results demonstrated that compared to the Fourier spectrum, the innovative method of scale energy spectrum can not only acquire the dominant frequency of the blasting vibration signals, but also the obtained spectrum curve is smoother and can clearly reflect the change trend of the signal spectrum. The newly constructed biorthogonal wavelet has the characteristics of high vanishing moment, high regularity and matching with the waveform variation of the measured blasting vibration signals, and can describe the subtle variation characteristics of blasting vibration signal frequency. The continuous wavelet transform energy spectrum can reflect the three-dimensional energy distribution of blasting vibration signal in the time-scale domain, and the occurrence time of frequency, the frequency duration interval and time range of blasting vibration signal can also be acquired. Wavelet packet algorithm can precisely calculate the energy distribution of each frequency component in the signal, the tunnel blasting vibration signals (YBJ1, YBJ2) generated near the power tower presents low frequency, while the signals (YBF3,YBF4) far away from the power tower presents relatively high frequency. Measures should be taken to control the vibration and resonance of power tower caused by tunnel blasting. This research is of great significance for recognizing the propagation law of vibration waves, reducing the impact of blasting on surrounding buildings, and ensuring the safety of tunnel construction and surrounding buildings.

Special affine wavelet packets: Theory and applications

In the article “Special affine multiresolution analysis and the construction of orthonormal wavelets in L2(R)” (Shah and Lone, 2022), we have introduced a novel multiresolution analysis (MRA) in the realm of the special affine Fourier domain. In continuation to the study, we present the construction of wavelet packets associated with the special affine MRA and investigate their orthonormal properties. Subsequently, we derive an orthogonal decomposition for the proposed special affine wavelet packet subspaces Uj,kM by employing the orthonormal properties of special affine wavelet packets. Besides, a fast wavelet packet algorithm associated with special affine wavelet packets is also presented. Towards the culmination, we extend the scope of the present study by exploring the applications of the special affine wavelet packets to image compression techniques.

Application of Wavelet Packet and Fuzzy Algorithm in Power System Short Circuit Fault Classification

Short-circuit fault, also known as a lateral fault, is one of the common types of faults in power transmission lines. This paper proposes an accurate method for identifying short-circuit fault types for power transmission line systems. According to the three-phase A, B, and C values, short-circuit faults can be subdivided into ten classes. An MDL power system with controllable short-circuit fault types is designed and tested. The method of using wavelet packet to analyze short-circuit fault waveforms and using the fuzzy controller to analyze eigenvectors is also proposed. Wavelet packet analysis makes up for the defect that wavelet transform usually only decomposes low frequencies and retains high frequency. Therefore, more accurate frequency band information and eigenvectors are obtained through wavelet packet transform, and then, the fuzzy controller is used to identify the eigenvectors, which can effectively detect and discriminate short-circuit fault types. Accurate identification of short-circuit fault types can improve transmission lines’ overall operational efficiency and promote the power system’s development.

Faulty feeder detection method for SLG faults in distribution networks based on comprehensive fault characteristics across entire frequency spectrum

In the event of a single line-to-ground fault in a non-effectively grounded distribution network, reliable detection and isolation of faulty feeders aid in ensuring safe network operation. To improve the accuracy and reliability of faulty-feeder detection, this paper proposes a faulty-feeder-detection method based on the entire-frequency-domain fault characteristics. First, the zero-sequence current variation characteristics of feeders are analyzed using a wavelet packet algorithm; subsequently, the frequency-spectrum energy and zero-sequence current direction are employed to build a detection criterion. Second, the frequency-spectrum energy and direction criteria are combined to obtain a comprehensive detection criterion across the entire frequency spectrum. Third, the ensemble-learning algorithm is utilized to construct the proposed comprehensive criterion. An extreme-gradient-boosting algorithm is employed for efficient modeling along with large amounts of simulated data that are used as training dataset. To verify the reliability and generalization capability of the proposed method, PSCAD simulation, RTDS, and practical fault data are employed considering different topologies, parameters, and fault conditions of various distribution networks. The results obtained in this study reveal the proposed method to improve the accuracy of faulty-feeder detection significantly relative to conventional approaches, which demonstrates that the proposed method shows considerable reliability and generalization potential for practical utility.

Wavelet packet coefficient of transient first half wave line selection method for single-phase grounding fault

Overvoltage will occur after single-phase grounding fault in distribution network, endangering system insulation. It is of great significance to find out the fault line as soon as possible to ensure the safe operation of the system. Wavelet modulus maxima can be used to detect the sudden change singularity of transient current. However, the judgement of single mode maximum value is easily influenced by the form of fault and interference, leading to misjudgement. In this paper, the transient characteristics of single-phase grounding fault are analyzed, and the magnitude and phase characteristics of the first half wave are given; Based on wavelet packet algorithm, several key problems such as boundary effect, wavelet selection and decomposition scale are explored; Taking the zero sequence voltage as the reference to determine the fault interval, the zero sequence current of each line is decomposed by three-scale wavelet packet using db6 wavelet, and the wavelet coefficients of the characteristic frequency band are extracted for comparison. The fault line is the one with the largest amplitude and different phase from others. The distribution system model of neutral grounding through arc suppression coil is built. Simulation is carried out for several cases with great difficulty in line selection, such as short circuit when phase voltage crosses zero, high resistance grounding and long line fault. The experimental results prove the reliability of this method.

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy.

The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.

Scintillation detector fault diagnosis based on wavelet packet analysis and multi-classification support vector machine

This paper presents a scintillation detector fault diagnosis method based on wavelet packet analysis and multi-classification support vector machine(multi-SVM) . In the proposed method, a wavelet packet algorithm is used to analyze waveform characteristics of output signals caused by different faults of detectors, then characteristic vectors can be extracted. The multi-SVM is employed to establish a fault recognition model and fault types can be concluded. Performances of the proposed method are validated by experimental data obtained from the plastic scintillation detector for the single fault diagnosis and hybrid fault diagnosis. The experimental results show that faults can be diagnosed automatically and quickly by analyzing signal waveform features.

An adaptive wavelet packet denoising algorithm for enhanced active acoustic damage detection from wind turbine blades

The development of a viable structural health monitoring (SHM) technology for the operational condition monitoring of wind turbine blades is of great interest to the wind industry. In order for any SHM technology to achieve the technical readiness and performance required for an operational implementation, advanced signal processing algorithms need to be developed to adaptively remove noise and retain the underlying signals of interest that describe the damage-related information. The wavelet packet transform decomposes a measured time domain signal into a time-frequency representation enabling the removal of noise that may overlap with the signal of interest in time and/or frequency. However, the traditional technique suffers from several assumptions limiting its applicability in an operational SHM environment, where the noise conditions commonly exhibit erratic behavior. Furthermore, an exhaustive number of options exist when selecting the parameters used in the technique with limited guidelines that can help select the most appropriate options for a given application. Appropriately defining the technique tends to be a daunting task resulting in a general avoidance of the approach in the field of SHM.This work outlines an adaptive wavelet packet denoising algorithm applicable to numerous SHM technologies including acoustics, vibrations, and acoustic emission. The algorithm incorporates a blend of non-traditional approaches for noise estimation, threshold selection, and threshold application to augment the denoising performance of real-time structural health monitoring measurements. Appropriate wavelet packet parameters are selected through a simulation considering the trade-off between signal to noise ratio improvement and amount of signal energy retained. The wavelet parameter simulation can be easily replicated to accommodate any SHM technology where the underlying signal of interest is known, as is the case in most active-based approaches including acoustic and wave-propagation techniques. The finalized adaptive wavelet packet algorithm is applied to a comprehensive dataset demonstrating an active acoustic damage detection approach on a ~46 m wind turbine blade. The quality of the measured data and the damage detection performance obtained from simple spectral filtering is compared with the proposed wavelet packet technique. It is shown that the damage detection performance is enhanced in all but one test case by as much as 60%, and the false detection rate is reduced. The approach and the subsequent results presented in this paper are expected to help enable advancement in the performance of several established SHM technologies and identifies the considered acoustics-based SHM approach as a noteworthy option for wind turbine blade structural health monitoring.

Research on Harmonic Detection System Based on Wavelet Packet Transform

This paper introduces the basic concepts of harmonics in power system, and expounds the basic principles of harmonic detection algorithms such as Fourier transform, short wavelet transform and wavelet packet transform. The effectiveness of the proposed algorithm is verified by simulation, which shows that the improved wavelet packet algorithm can achieve the uniform division of the harmonic signal frequency band, and improve the detection accuracy of the harmonic signal. Wavelet packet transform could detect the fundamental and harmonic components of the original signal, which is most suitable for the detection of harmonic signals.

Research on Harmonic Analysis and Detection Based on Improved Wavelet Packet Inverter

This paper introduces the basic concepts of harmonics in power system, and expounds the basic principles of harmonic detection algorithms such as Fourier transform, short wavelet transform and wavelet packet transform. The effectiveness of the proposed algorithm is verified by simulation, which shows that the improved wavelet packet algorithm can achieve the uniform division of the harmonic signal frequency band, and improve the detection accuracy of the harmonic signal. Wavelet packet transform could detect the fundamental and harmonic components of the original signal, which is most suitable for the detection of harmonic signals.

Localization of CO2 gas leakages through acoustic emission multi-sensor fusion based on wavelet-RBFN modeling

CO2 leakage from transmission pipelines in carbon capture and storage systems may seriously endanger the ecological environment and human health. Therefore, there is a pressing need of an accurate and reliable leak localization method for CO2 pipelines. In this study, a novel method based on the combination of a wavelet packet algorithm and a radial basis function network (RBFN) is proposed to realize the leak location. Multiple acoustic emission (AE) sensors are first deployed to collect leakage signals of CO2 pipelines. The characteristics of the leakage signals from the AE sensors under different pressures are then analyzed in both time and frequency domains. Further, leakage signals are decomposed into three layers using wavelet decomposition theory. Wavelet packet energy and maximum value, and time difference calculated by cross-correlation are selected as the input feature vectors of the RBFN. Experiments were carried out on a laboratory-scale test rig to verify the validity and correctness of the proposed method. Leakage signals at different positions under different pressures were obtained on the CO2 pipeline leakage test bench. Compared with the time difference of arrival method, the relative error obtained using the proposed method is less than 2%, which has certain engineering application prospects.

A New Method for Multipath Filtering in GPS Static High-Precision Positioning.

It is well known that multipath is one of the main sources of errors in GPS static high precision positioning of short baselines. Most algorithms for reducing multipath manipulate the GPS double difference (DD) observation residuals as input signal in GPS signal processing. In the traditional multipath mitigation methods, applying the wavelet transform (WT) to decompose the GPS DD observation residuals for identifying the multipath disturbance cannot effectively filter out the white noise of the high frequency part of the signal, and it is prone to edge effect. In this paper, for extracting multipath, a wavelet packet algorithm based on two-dimensional moving weighted average processing (WP-TD) is proposed. This algorithm can not only effectively filter out the white noise of the high frequency part of the signal, but also weaken the influence of the edge effect. Furthermore, considering the repeatability of multipath error in static positioning, we propose a method for determining the level of wavelet packet decomposition layers which make multipath extraction more effectively. The experimental results show that the corrected positioning accuracy is 14.14% higher than that of the traditional wavelet transform when applying the obtained multipath to DD coordinate sequences for position correction.

Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems

With the promotion of the latest technologies and the new requirement of humanitarian, the wireless multi-sensor system is applied broadly. This paper studies the data fusion of the industrial wireless sensor networks (IWSNs), in order to acquire more thoughtful data for the prognosis and diagnosis of the monitored device. These authors propose a combination of back propagation neural network (BP NN) and Wavelet Packet algorithm for data fusion. This proposed algorithm is based on each cluster head, which is modelled with a three layers NN. A case study using the ball bearing test data, which is from the Bearing Data Center of the Case Western Reserve University, and to verify the effectiveness of the proposed algorithm. With MATLAB 2016b version, the raw data feature is extracted by the Wavelet Packet and the feature fusion is based on BP NN at sink node. The simulation results show that the proposed algorithm is effective in fault diagnosis of wind turbine.