Wavelet Packet Decomposition Method Research Articles

Coordinated control algorithm of hydrogen production-battery based hybrid energy storage system for suppressing fluctuation of PV power

Photovoltaic (PV) power generation has issues of volatility and intermittency. Currently, PV plants are generally equipped with 10% rated capacity lithium-ion (Li) battery energy storage systems in China, who often fail to suppress fluctuation in the output power of PV plants effectively and meet the grid-connected standard. The hybrid energy storage system (HESS) combining with hydrogen production and Li battery system can produce hydrogen by water electrolysis during the peak period of PV power generation, effectively improving PV utilization efficiency, while smoothing PV power fluctuation and improving grid connection electricity quality. Firstly, models of the solid oxide electrolysis cell (SOEC) and alkaline electrolysis cell (AEC) systems for hydrogen production, and Li battery energy storage system are established, and the transient response characteristics of each system are analyzed. Secondly, an adaptive wavelet packet decomposition (AWPD) method for PV power signal decomposition is proposed based on the wavelet packet decomposition (WPD) method. Thirdly, a capacity configuration method for HESS are proposed based on the APWD method. Fourthly, a coordinated control strategy for HESS is proposed with the transient response characteristics of different energy storage systems and the state of charge for Li battery system. Finally, the proposed method is validated through simulation experiments based on the actual power data of the PV plant. The results show that the developed methods can effectively utilize partial PV power generation to produce hydrogen, improve PV utilization, and the combined output power of PV plant and HESS can fulfill the grid-connected standard.

Agent-SwinPyramidNet: an enhanced deep learning model with AMTCF-VMD for anomaly detection in oil and gas pipelines

PurposeThe anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.Design/methodology/approachFirst, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.FindingsIn the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.Originality/valueThis paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.

Multi frequency stability optimization of integrated energy systems considering virtual energy storage characteristics of heating networks

With the development of market-oriented reformation, integrated energy system with high proportion of new energy installed capacity are gradually facing the pressure of power fluctuation stabilizing. This paper exploits the weak equilibrium characteristic of thermal system to solve high frequency fluctuation of new energy sources. It establishes a multi-frequency stable operation optimization model of integrated energy system considering the virtual energy storage characteristics of thermal network. Firstly, the model exploits the wavelet packet decomposition method for the frequency decomposition of the fluctuation stabilizing demand faced by the integrated energy system. Secondly, this study investigates the technical characteristics of source-load-storage equipment regulation and heat network temperature-quantity regulation in the thermal system. Finally, considering the equipment life and system fluctuation smoothing needs, an integrated energy system stability optimization model is established. Meanwhile, a modified honey badger algorithm is proposed to realize the case optimization simulation.The result shows that the total operating cost of the system is reduced by 8.45%. As the thermal system regulation replaces the high-frequency regulation function of the energy storage equipment, the service life of battery increased by 67.6%. Therefore, the model effectively solves the integrated energy system fluctuation stability problem with high proportion of new energy installed capacity.

Evaluation of bridge structure damage based on deep learning identification model

Safety monitoring is an important part of bridge engineering construction and operation. At present, there is room for promoting the health monitoring and evaluation of small and medium-sized concrete bridges. In view of this, the study first models the spatial model and physical parameters of the bridge, and then builds the data of vehicle load and vehicle type. To reduce the complexity of data mapping, wavelet packet decomposition is used to analyze the data structure. And the physical field effect analysis is abandoned to directly mine the data relationship at both ends by using deep neural network. The data decomposition results show that the method can discard the temperature-induced effect. And the local decomposition results of the data meet the input of the neural network. The data measured by the sensor is added to the depth learning model for fitting. The overall and local fitting rates are more than 92%. The loss function converges quickly, and there is no gradient explosion. The model predicts the bridge structural damage caused by vehicle stress of four load categories, and the results show that the average fitting rate is 89.72%. Therefore, the identification path of the proposed deep learning model has positive significance for the evaluation of bridge structural damage. The main contribution of the study is to propose a deep learning-based method for bridge structural damage assessment. By modeling the spatial model and physical parameters of the bridge and combining data from vehicle load and vehicle type, the data structure was analyzed using the wavelet packet decomposition method to eliminate temperature-induced effects and data from sensor measurements were added to the deep learning model for fitting. This finding has positive implications for bridge structural damage assessment and can provide effective pathways and methods to monitor and evaluate the health status of small and medium-sized concrete bridges. This has important practical application value for the construction and operation of bridge engineering.

Influence of microtextured parameters of dry gas sealing rings on tribological performance

PurposeThis study aims to research the influence mechanism of microtextured geometric parameters of dry gas seal end face on the tribological behavior under dry frictional conditions.Design/methodology/approachThe microtexture was processed using laser processing, while the diamond-like carbon (DLC) film was applied through magnetron sputtering; the experimental platform of friction vibration was established, the frictional and vibrational properties of different geometric parameters were tested; the data signals of vibrational acceleration and frictional torque were collected and processed using data acquisition instrument. The entropy characteristic parameters of 3D vibrational acceleration were extracted based on wavelet packet decomposition method. The end-face topography was measured with ST400 three-dimensional noncontact surface topography instrument.FindingsThe geometry of pits plays a key role in influencing friction performance; the permutation entropy and fuzzy entropy of the vibration acceleration signal changed with variations in microtextured parameters. A textured surface with appropriately size parameters can trap debris, enhance the dynamic pressure effect, reduce impact between the friction interfaces and improve the frictional vibrational performance. In this research, microtextured surface with Φ150 µm-10% and Φ200 µm-5% can effectively reduce friction and vibration between the end faces of a dry gas seal.Originality/valueDLC film improves the hardness of seal ring end face, and microtexture improves the dynamic effect; the tribological behavior monitoring can be realized by analyzing the characteristics of vibration acceleration sensitive parameter with friction state. The findings will provide a basis for further research in the field of tribology and the microtexture optimization of dry gas seal ring end face.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0389/

Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals

Obstructive sleep apnea (OSA) is a disorder which blocks the upper airway during sleep. The severity of OSA will lead heart attack, stroke and end of life. This proposed study explored the classification of OSA and healthy subjects using brain connectivity analysis from electroencephalogram (EEG) signals. Institute of System and Robotics—University of Coimbra (ISRUC) database were used for acquiring 50 EEG signals using 4 channels and noise removal has been accomplished by 50 Hz notch filter. The Institute of System and Robotics—University of Coimbra (ISRUC) database contained 50 EEG signals, with four channels, and a 50 Hz notch filter was applied to remove noise. Wavelet packet decomposition method was performing the segregation of EEG signals into five bands; Gamma (γ), beta (β), alpha (α), theta (θ) and delta (δ). A total of 4 electrode positions were used for the brain connectivity analysis for each EEG band. Pearson correlation method was effectively used for measuring the correlation between healthy and OSA subjects. The nodes and edges were highlighted the connection between brain and subjects. The highest correlation was achieved in delta band of OSA subjects which starts from 0.7331 to 0.9172 respectively. For healthy subjects, the positive correlation achieved was 0.6995. The delta band has been correlated well with brain when compared other bands. It has been noted that the positive correlation well associated with brain in OSA subjects, which classifies OSA from healthy subjects.

An improved fault diagnosis method for rolling bearings based on wavelet packet decomposition and network parameter optimization

The diagnosis of faults in rolling bearings plays a critical role in monitoring the condition and maintaining the performance of rotating machinery, while also preventing major accidents. In this article, a new approach to diagnosing faults in rolling bearings is proposed, using wavelet packet decomposition (WPD) for features extraction and the chaotic sparrow search optimization algorithms (CSSOAs) to optimize the parameters of a deep belief network (DBN). Firstly, the WPD method is used for the decomposition of vibration signals in rolling bearings, which are decomposed into three layers, and reconstruction is performed on the nodes of the last layer based on the decomposition. Furthermore, the energy characteristics of the reconstructed nodes are then utilized as inputs to DBN, and the CSSOA is employed to optimize the hyperparameters of DBN. Ultimately, a fault diagnosis model combining WPD with optimizing parameters is presented. This model is validated on bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). Experimental results indicate that the average accuracy achieved when modeling with WPD-CSSOA-DBN on the CWRU dataset is , with a root mean square error of 0.0713. On the JNU bearing dataset, the modeling achieves an average accuracy of with a root mean square error of 0.1018. Compared to other methods, this approach demonstrates stronger feature extraction capabilities and outstanding rolling bearing fault diagnosis abilities.

High-resolution art recognition of modern packaging design and images based on texture image classification

Abstract First, this paper proposes the EPNN algorithm to recognize the high-resolution art of texture images. Then, a multi-scale multivariate image analysis method for color-textured surface classification is established. Regarding probabilistic neural networks, a differential evolutionary algorithm is proposed to optimize the smoothing parameters of basic probabilistic neural networks. Texture feature extraction involves extracting energy and statistical features of texture images using tree-structured wavelet packet decomposition and statistical-based methods. The image classification is performed by estimating similarity distances and classifying typical feature clusters. The results show that in terms of recognition ability, the proposed EPNN in this paper achieves an average improvement of 0.216 in CCP compared to the LBP method. In terms of classification ability, the proposed multi-scale multivariate image analysis color texture surface classification method in this paper generally achieves more than 10% higher classification ability than LGP in CUReT and Outex texture image libraries.

Characteristic analysis of epileptic brain network based on attention mechanism

Constructing an efficient and accurate epilepsy detection system is an urgent research task. In this paper, we developed an EEG-based multi-frequency multilayer brain network (MMBN) and an attentional mechanism based convolutional neural network (AM-CNN) model to study epilepsy detection. Specifically, based on the multi-frequency characteristics of the brain, we first use wavelet packet decomposition and reconstruction methods to divide the original EEG signals into eight frequency bands, and then construct MMBN through correlation analysis between brain regions, where each layer corresponds to a specific frequency band. The time, frequency and channel related information of EEG signals are mapped into the multilayer network topology. On this basis, a multi-branch AM-CNN model is designed, which completely matches the multilayer structure of the proposed brain network. The experimental results on public CHB-MIT datasets show that eight frequency bands divided in this work are all helpful for epilepsy detection, and the fusion of multi-frequency information can effectively decode the epileptic brain state, achieving accurate detection of epilepsy with an average accuracy of 99.75%, sensitivity of 99.43%, and specificity of 99.83%. All of these provide reliable technical solutions for EEG-based neurological disease detection, especially for epilepsy detection.

Capacity optimization of hybrid energy storage systems for offshore wind power volatility smoothing

Energy storage devices are frequently included to stabilize the fluctuation of offshore wind power’s output power in order to lessen the effect of intermittency and fluctuation on the electrical grid but doing so will raise operators’ investment costs. To obtain the best economic benefits, this paper presents a hybrid energy storage system based on batteries and super-capacitors and its capacity configuration optimization method. First, the wind power output is divided using the wavelet packet decomposition method, and then power is distributed between the batteries and the super-capacitors. Then, the mathematical model of energy storage system optimization is established to optimize the capacity configuration of hybrid energy storage with the objective of minimizing the daily input cost of energy storage, and the configuration schemes of single energy storage and hybrid energy storage are compared. Finally, using the measured data of a domestic offshore wind farm for simulation, several energy storage schemes are compared to verify the feasibility and effectiveness of the proposed method, at the same time, using the hybrid energy storage devices to participate in the grid-connected stabilization task of offshore wind power is more advantageous than the single energy storage, the results show that the daily input cost of the hybrid energy storage scheme is 2.79% and 3.84% lower than that of the single energy storage scheme using only battery or super-capacitor.

A deep learning method to monitor axial pressure and shear deformation of rubber bearings under coupled compression and shear loading

AbstractLaminated rubber bearings are the key components of base isolated structures. It is important to effectively monitor the axial pressure and shear deformation in rubber bearings. In previous studies, the axial pressure and shear deformation of rubber bearings have been monitored separately using wavelet‐packet‐based method. However, when the axial pressure and deformation of rubber bearings change at the same time, the prediction error of the above method is large. Therefore, this paper proposed a new deep neural network architecture, named P&D‐NET to monitor the axial pressure and shear deformation simultaneously. The P&D‐NET combines wavelet packet decomposition method and fully connected neural network. In this study, the coupling effects of axial pressure and shear deformation monitoring are studied by full‐scale experiments. The details of P&D‐NET are also introduced. Three full‐scale smart rubber bearings (SRB) were tested under different axial pressure and shear deformation to form a real measured dataset. The influence of feature extraction methods, network structures, and weighting coefficient in loss function was investigated to optimize the network. By using the optimal hyperparameters, the root‐mean‐square error (RMSE) of axial pressure prediction and shear deformation prediction in the test set is 1.41 MPa and 17.6%, which has reduced the error by up to 68.8% compared to the previous wavelet‐packet‐based method.

Novel seizure detection algorithm based on multi-dimension feature selection

In machine learning based seizure detection research studies, the number of features directly affects the performance of models. In order to decrease the amount of features under the premise of guaranteeing the performance of the model, a seizure detection algorithm based on multi-dimension feature selection is proposed. Firstly, the wavelet packet decomposition (WPD) method is applied to EEG signal, and different features are extracted from original EEG signals and sub-band signals. Random forest (RF) is then applied to analyze feature importance, in order to find redundant features. The importance of features is accumulated according to three dimensions: channel, sub-band signal and feature individual. Select the dimension with least importance and delete its relevant features. Repeat to find the dimension with least importance and delete its relative features until the least dimension importance exceeds the manual threshold. To evaluate the proposed method, 10-fold cross-validation is performed on the training set. The obtained accuracy (AC), specificity (SP) and sensitivity (SE) are 98.03%, 99.04% and 97.02%, respectively. Event-based real-time seizure detection is performed on the test set, and the obtained AC, SP, SE and FPRE are 99.76%, 99.84%, 87.23% and 0.245 times/h, respectively.

A weak fault identification method of micro-turbine blade based on sound pressure signal with LSTM networks

The working condition of micro-turbine blades is very harsh, which is easy to cause blade faults such as fouling, cracks, and fractures. Therefore, it is of great significance to accurately and timely identify the early faults of turbine blades. In this paper, an early weak fault identification method of micro-turbine blade based on sound pressure signal and long short-term memory (LSTM) networks is proposed. Firstly, the sound pressure signals generated by different turbine structures are analyzed by frequency spectrum. Then, the wavelet packet decomposition method is adopted to extract wavelet packet coefficient energy as the fault feature vector, and the weak fault identification model of the turbine blade is established based on LSTM networks. Finally, different faults of turbine blades under different working conditions are identified and tested by using the sound pressure test platform of the micro-turbine. The results show that it is feasible to identify turbine blade faults based on sound pressure signals. Through multiple test results, it can be seen that the identification accuracy of the proposed method for turbine blade faults reaches more than 98%, indicating that it is effective for weak fault identification of turbine blades under complex working conditions, which can provide reference and guidance for the identification and warning of early weak faults of micro turbine blades.

Study on the influence of various factors on block vibration

The influence of engine speed, torque, oil temperature, and piston liner clearance on the block vibration was studied with experimental test and wavelet packet decomposition method (WPD). The wavelet packet function of db10 and decomposition level of four was confirmed based on the Shannon entropy. The frequency analysis of the block vibration measured at motor-driven condition showed that the spectrum energy was mainly concentrated in the range of 4–5, 10–15, and 20–25 kHz. The energy of each frequency band obtained from WPD was contrasted at different engine speed, torque, oil temperature, and piston-liner clearance conditions. On this basis, the interaction between different factors was further investigated. The sensitive frequency band of block vibration was confirmed for different factors.

A reliable method of wind power fluctuation smoothing strategy based on multidimensional non‐linear exponential smoothing short‐term forecasting

This paper investigates the multi-dimensional non-linear exponential smoothing prediction method under different weight coefficients, so as to realize the short-term prediction of the original wind power output. Based on the current research on wind power fluctuation prediction, this paper firstly proposes a new multi-dimensional nonlinear exponential smoothing prediction method. Based on different weight coefficients, the corresponding short-term forecast volatility is obtained. Then, the wavelet packet decomposition method is used to further realize the secondary smoothing of the prediction wind power fluctuations. Hybrid energy storage system (HESS) plays an important role in wind power fluctuation suppression from different frequency bands. However, the traditional fixed frequency power allocation method significantly degrades the performance of the accuracy of the algorithm. Therefore, this paper proposes a frequency conversion entropy strategy, which combines energy value with wavelet packet decomposition. By calculating the energy components of different frequency bands, the fluctuation can be divided effectively. Finally, simulation and experiment discussions show that the proposed algorithm in this paper can realize the power allocation effectively compared to the traditional one. And it further verifies the feasibility and reliability of the strategy proposed in this work.

Arabic Handwritten Word Recognition System Based on the Wavelet Packet Decomposition

This paper attempts to recognize Arabic handwriting based on the Wavelet Packet Decomposition (WPD) using two different classifiers (Support Vector Machine SVM with three kernels and k-Nearest Neighbors K-NN). The proposed approach of recognizing Arabic handwriting contains three major stages including image preprocessing, extracting the features of the image, and classification. Firstly, the diacritics are removed using the opening morphological operation (i.e image preprocessing). Secondly, extracting the structure of the paragraph using the morphological method. Finally, the word image size is converted into a suitable size for the next stages. To extract features from the image, the WPD method was adopted to extract the features of Arabic handwriting as the transformation method of feature space. This extracts the Arabic global features to be classified in the last stage using the SVM with polynomial kernel and K-NN. The proposed approach of recognizing Arabic handwriting was tested on IFN/ENIT dataset by rescaling images into various sizes, 93.7% when the SVM with polynomial kernel is used, K-NN classifier achieved accuracy rate is 88.4%.

Defect Size Evaluation of Cylindrical Roller Bearings with Compound Faults on the Inner and Outer Races

Faults in cylindrical roller bearings are one of the main contributors to major faults in rotating machinery. The development of bearing fault diagnosis technologies is key to measuring the performance, status, and risk of failure of rolling-element bearings and has attracted extensive attention from industry and academia. When faults arise, they are often not a single fault but a compound fault, such as the simultaneous failure of the inner and outer races. In this paper, a method for evaluating the size of compound faults on the inner and outer races of cylindrical roller bearings is proposed. The dynamic modeling method developed by Gupta is employed to create a dynamic model for compound faults on the inner and outer rings of rolling bearings that allows the time domain signal of the vibration responses of compound faults on the inner and outer races to be obtained. Adopting an improved continuous harmonic wavelet packet decomposition method for the decomposition and reconstruction of the compound fault signal, we arrive at the corresponding single-point fault signal. The relationship between defect size and key metrics of the vibration, such as root mean square acceleration (RMS), peak, crest factor (CF), kurtosis, and level crossing rate (LCR), is investigated. The results show that there is a strong linear correlation between LCR and defect size, which can be used to evaluate the size of the defect. Experimental data for cylindrical roller bearings with compound faults on the inner and outer races are examined to verify the results.

Research on planning optimization of integrated energy system based on the differential features of hybrid energy storage system

At present, it is very important to build the new power system with new energy as the mainstay. Some Regional Integrated Energy Systems (RIES) have installed renewable energy generation equipments with wind turbines and photovoltaics as the main power supply facilities. Due to the volatility in the wind turbine, photovoltaic output power and the customer's electricity load, there is energy waste in the RIES. In order to enhance the economic and renewable energy consumption rate of the park where the RIES is applied, an integrated energy system(IES) planning optimization model is constructed，which considers the mixed storage differentiation characteristics. The model expands the hybrid energy storage system(HESS) on the basis of the original RIES. A two-stage decomposition model combining wavelet packet decomposition method and power decomposition method based on discrete Fourier transform is established, in which the difference between power generation of renewable energy and customer load is decomposed. Depending with the difference in response speed of energy storage devices, the power of different frequencies is moderated. With the comprehensive energy system economy as the goal, the genetic algorithm based on elitist preservation strategy is adopted to optimize the planning model and verify the correctness of the model through simulation. The results show an improvement of 8.23 % and 23.79 % in economy and renewable energy consumption rate compared to the RIES before the HESS expansion.

Online judgment of laser welding process based on time-frequency analyses of in-situ high-speed optical signals

An immediate and accurate judgment of laser welding quality is crucial to automobile, shipbuilding, power battery, and many other manufacturing industries. Optical radiation is the most common phenomenon during laser welding, and its variation implies the quality of welding joints. In this study, the reflected laser radiations from the keyhole and visible light radiations are captured and analyzed utilizing the Wavelet Packet Decomposition method (WPD). The energy spectrums of these in-situ signals are calculated and reconstructed as a feature map which indicates its time-frequency spectrum distribution. This study established a method based on CNN to explore and model the relationships between the features extracted from the photoelectric signals and the welding quality. Experimental results are analyzed and compared and show excellent performance in judging the laser welding process. This study provides a practical framework for in-situ judgment of the laser welding process based on photoelectric signals.

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis.

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average  (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average  (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average  and  values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average  (16.55 dB, utilizing db1 wavelet packet) and largest average  (41.40%, using fk8 wavelet packet). The highest average  and  using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average  also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both  and  values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.