Wavelet Packet Analysis Research Articles

Load recognition of connecting-shaft rotor system under complex working conditions

A method for qualitatively recognizing the load of the rolling equipment's connecting-shaft rotor system is proposed in this paper due to the complexity of rolling production conditions and the limitations of single source response signals. The method is oriented towards fusing the vibration and motor's current information. First, singular value decomposition and wavelet packet analysis are used to preprocess the two types of response signals. Then, the Bayesian estimation method in feature-level fusion achieves qualitative recognition and analysis of rotor system load types. Corresponding load experiments are completed on a load recognition test platform based on vibration and the motor's current signals. The research results show that the load recognition method based on fusion information can recognize the type of load excitation with a recognition accuracy of 91.7 %, higher than other single-source response signal methods. Therefore, the feasibility of the aforementioned theoretical methods is verified.

Analysis of vibration signals near ground surface during blasting excavation of a tunnel in fractured rock

This study aims to analyze the vibration signals near the ground surface due to the underneath drilling and blasting activities in a fissured rock tunnel. Blasting induced vibration on the ground surface was continuously monitored in a fissured rock tunnel drilling and blasting excavation project in field. Wavelet packet analysis of the vibration signals using Matlab was carried out for signal denoising, differential blasting delay time interval identification, and three-way time-frequency energy analysis. The results show that within a 30 m range from the palm face, the dominant frequency bands of blasting-induced vibrations on the ground surface were concentrated in the range of 0–130 Hz. Two prominent peak frequency bands were identified at 31.25–39.063 Hz (low-frequency band) and 93.75–101.56 Hz (high-frequency band), accounting for 12% of the total energy. Among the three directions of ground surface vibrations, the energy decay was the most significant in the x-direction (tunnel excavation direction), which amounted to 54.29% of the overall energy decay with increasing distance. The energy decay within the 50–80 Hz range was the most pronounced (more than 90%), when the angle between the vibration propagation direction and the fissure or joint direction was 75°. The conclusions provide the insights in the attenuation of blast-induced vibrations in fissured rock and can potentially assist in the design of blasting vibration control.

Monitoring of interface separation damage in buckling-restrained steel plate shear walls using piezoelectric based smart aggregates under cyclic loading

Interface debonding damage is a critical issue for many prefabricated concrete components. Various studies have employed different methods to detect debonding behaviors and provided quantitative damage indexes. In this paper, we propose a novel monitoring approach for detecting interface debonding damage in Engineered Cementitious Composite (ECC) panel-restrained composite steel plate shear walls (SPSWs) using piezoelectric ceramics (PZT). Four SPSWs were designed with different types of concrete, steel plate thicknesses, and bolt arrangements for the cyclic loading experiment. Energy data was collected for time-domain and wavelet packet analysis. The root mean square deviation (RMSD) was then calculated to provide a quantitative damage index for the specimens. The highest damage indices for each specimen were 0.770, 0.963, 0.988, and 1, respectively, which corresponded well with the actual damage scenarios. The experiments verified that using PZT can accurately monitor debonding damage in SPSWs.

Processing and analysis of friction vibration signal of diamond-like carbon film microtextured dry gas seal rings

PurposeThis paper aims to analyze the characteristics of friction vibration signals and identify the vibration excitation source at the start and stop stage of microtextured end face of dry gas seals.Design/methodology/approachThe friction pair consists of a diamond-like carbon (DLC) film microtextured seal ring and a spiral groove seal ring. Friction vibration signal feature extraction method based on harmonic wavelet packet and spectrum analysis was proposed. Signals were collected using acceleration sensor, acquisition card and LabVIEW software. Vibration acceleration signal was decomposed into 32 frequency bands using MATLAB wavelet packet transformation. The 32nd band coefficient was extracted for reconstruction, time-domain and spectral waveforms were obtained and spectra before/after denoising were compared.FindingsThe end face of the DLC film microtextured seal ring generates a good dynamic pressure effect, and the friction and vibration reduction effects are obvious. The harmonic wavelet packet can decompose the vibration signal conveniently and precisely. In the case of this experiment, the frequency of vibration of the seal ring is 7500 HZ.Originality/valueThe results show that the method is effective for the processing of friction vibration signal and the identification of vibration excitation source. The findings will provide ideas for the frictional vibration signal processing and basis for further research in the field of tribology of dry gas seal ring.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0084/

Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels

Building a new tunnel adjacent to an existing tunnel has become a common means of transformation in engineering. Existing tunnels are prone to some deterioration, such as cavities and cracks under long-term traffic load. This kind of deterioration tunnel is prone to collapsing under the action of blasting. Therefore, the vibration caused by blasting should be strictly controlled. Based on the reconstruction project of the Bo Jiling Tunnel, this paper puts forward the method of mechanical cutting in a central position combined with an ordinary detonator to reduce blasting vibrations. ANSYS/LS-DYNA version 19.2, was used to simulate two conditions of full-section blasting and central mechanical cutting blasting. By comparing the stress and velocity of the existing tunnel, the damping effect of mechanical cutting blasting is analyzed. Via field experiments, the superiority of the mechanical cutting method in reducing blasting vibration is further discussed. At the same time, the relationship between the main vibration frequency and the peak velocity of the existing deterioration tunnel is obtained by wavelet packet analysis of the field experimental data. The frequency band energy distribution in each direction of vibration velocity is also obtained. The results show that the central mechanical cutting increases the blasting free surface, and the mechanical cutting method reduces the vibration velocity by 36.3%. The third frequency band (31.25~46.875 Hz) is the most concentrated, which is the dominant frequency band of the signal. The novelty of this paper is to propose mechanical cutting of the central hole instead of traditional blasting for existing deterioration tunnels. The feasibility of this method is verified by numerical simulation and field tests. The relationship between peak vibration velocity, band energy, and tunnel frequency is clarified, which can better control blasting vibration and ensure the safety of existing deterioration tunnels.

Push-out test behavior and damage detection of steel-UHPC composite using PZT sensing

This present study investigates the failure mechanisms and damage evolution at the steel-UHPC interface through static push-out tests and active sensing monitoring on 15 steel-UHPC specimens. To address the ductility issue of embedded studs in UHPC as specified by Eurocode 4 to be at least 6 mm, the selected parameters particularly include large-diameter studs (≥25 mm), high-strength studs, and bolted connectors, which less frequently involved in current research. The test results indicate that large-diameter studs and bolted connectors can enhance the ductility of the steel-UHPC specimens. However, the use of high-strength studs further reduced the ductility of the specimens due to welding quality issues. Furthermore, to explore the evolution of damage at the steel-UHPC interface, the Piezoelectric Transducer (PZT)-based active sensing system was employed to continuously monitor the development of slip damage at the steel-UHPC interface during the static push-out tests and then evaluate the specimen's damage under different loading conditions. By analyzing the wave response of the piezoelectric sensor, the results reveal a strong correlation between the damage index, obtained through wavelet packet analysis, and the progression of interface slip. This correlation enables precise identification of critical damage, such as interface debonding and connector fracture, during the loading process. Utilizing the test outcomes, a quantitative relationship between the damage index and slip for the purpose of structural damage assessment has been built. Finally, based on the test data, this study assessed the applicability of current shear resistance design equations, proposed a refined load-slip relationship for studs, and offered well-founded design recommendations.

Characteristics of head frequency response in blunt impacts: a biomechanical modeling study.

Existing evaluation criteria for head impact injuries are typically based on time-domain features, and less attention has been paid to head frequency responses for head impact injury assessment. The purpose of the current study is, therefore, to understand the characteristics of human body head frequency response in blunt impacts via finite element (FE) modeling and the wavelet packet analysis method. FE simulation results show that head frequency response in blunt impacts could be affected by the impact boundary condition. The head energy peak and its frequency increase with the increase in impact; a stiffer impact block is associated with a higher head energy peak, and a bigger impact block could result in a high proportion of the energy peak. Regression analysis indicates that only the head energy peak has a high correlation with exiting head injury criteria, which implies that the amplitude-frequency aggregation characteristic but not the frequency itself of the head acceleration response has predictability for head impact injury in blunt impacts. The findings of the current study may provide additional criteria for head impact injury evaluation and new ideas for head impact injury protection.

Blasting vibration effect and safety evaluation method of railway cross tunnels

The evolution characteristics of the peak particle velocity and frequency of an existing railway cross tunnel are systematically studied. The analysis results show that there is a difference in the blasting vibration response before and after the new tunnel face is built. In this paper, a set of blasting vibration response evaluation methods that can quantitatively consider the influence of frequency by means of dimensional analysis is introduced. In addition, relevant knowledge on topics such as wavelet packet analysis, signal processing, structural dynamics, and explosion mechanics is applied to research the influence of the frequency. The research results show that the influence of the frequency on the blasting vibration response can be quantitatively analyzed by quantifying the dynamic amplification factor. To resolve the insufficiency of field monitoring, the distribution of the blasting vibration response along the entire section of the existing tunnel is studied through numerical simulation. Based on the theory of distortion energy in material mechanics, a new idea for evaluating the blasting vibration response of a cross tunnel is proposed.

Research on Demagnetization Fault Diagnosis Method of Mine Cutting Permanent Magnet Synchronous Motor

To give timely and accurate diagnosis in the early stage of demagnetization failure for effective control and treatment, based on wavelet packet analysis, principal component analysis (PCA) dimensionality reduction, and least squares support vector machine(LSSVM), the extraction of features and the classification of demagnetization faults are completed. Since it is difficult to collect real data sets of demagnetization faults in practice, a two-dimensional finite element simulation model of permanent magnet synchronous motor (PMSM) under uniform demagnetization and partial demagnetization faults is established based on the Maxwell simulation platform. The wavelet packet analysis is used to extract the demagnetization feature of the A-phase current of the PMSM. Based on PCA dimensionality reduction, the dimensionality reduction of fault features is realized. The LSSVM is used to identify the fault and complete the fault classification. The simulation results show that the method has a high classification accuracy rate for demagnetization faults.

A PZT-Based Smart Anchor Washer for Monitoring Prestressing Force Based on the Wavelet Packet Analysis Method

Prestressed steel strands in prestressed structures offset or reduce the tensile stress caused by external loads, making them the primary load-bearing components. Great concerns have been raised about prestress monitoring due to the growing use of structural health monitoring (SHM). Piezoceramic (PZT) active sensing methods are commonly used in this field. However, there appears to be a problem of “energy saturation” in the utilization of piezoceramic active sensing methods. In this study, a smart anchor washer with semi-cylinders was developed to alleviate the saturation problem. An intelligent monitoring system is formed by combining the upper and lower annular cylinders with two piezoelectric patches. The piezoelectric patch on the upper annular cylinder is used as an actuator to emit signals through the contact interface of the smart anchor washer, which are then received by the piezoelectric patch on the lower annular cylinder. Based on wavelet packet decomposition, we investigate the correlation between the energy of the received signal and the applied tension force. Finally, a prestressing force index is developed for monitoring prestressing force using Shannon entropy. It is found that the index decreases with the increase in tension. The proposed design and index are also sensitive to early monitoring of prestressing force and can be used to monitor the entire prestressing process of steel strands.

Evaluation of Parkinson's disease early diagnosis using single-channel EEG features and auditory cognitive assessment.

Parkinson's disease (PD) often presents with subtle early signs, making diagnosis difficult. F-DOPA PET imaging provides a reliable measure of dopaminergic function and is a primary tool for early PD diagnosis. This study aims to evaluate the ability of machine-learning (ML) extracted EEG features to predict F-DOPA results and distinguish between PD and non-PD patients. These features, extracted using a single-channel EEG during an auditory cognitive assessment, include EEG feature A0 associated with cognitive load in healthy subjects, and EEG feature L1 associated with cognitive task differentiation. Participants in this study are comprised of cognitively healthy patients who had undergone an F-DOPA PET scan as a part of their standard care (n = 32), and cognitively healthy controls (n = 20). EEG data collected using the Neurosteer system during an auditory cognitive task, was decomposed using wavelet-packet analysis and machine learning methods for feature extraction. These features were used in a connectivity analysis that was applied in a similar manner to fMRI connectivity. A preliminary model that relies on the features and their connectivity was used to predict initially unrevealed F-DOPA test results. Then, generalized linear mixed models (LMM) were used to discern between PD and non-PD subjects based on EEG variables. The prediction model correctly classified patients with unrevealed scores as positive F-DOPA. EEG feature A0 and the Delta band revealed distinct activity patterns separating between study groups, with controls displaying higher activity than PD patients. In controls, EEG feature L1 showed variations between resting state and high-cognitive load, an effect lacking in PD patients. Our findings exhibit the potential of single-channel EEG technology in combination with an auditory cognitive assessment to distinguish positive from negative F-DOPA PET scores. This approach shows promise for early PD diagnosis. Additional studies are needed to further verify the utility of this tool as a potential biomarker for PD.

Damage-Identification Method for Bridge Structures Based on Displacement Influence Line and Wavelet Packet Analysis

Damage-Identification Method for Bridge Structures Based on Displacement Influence Line and Wavelet Packet Analysis

Algorithm for diagnosing and warning of vibration caused by loosened screws on motor base using wavelet packet and neural network

Motor base screw loosening is a common problem in motor operation, which, if not dealt with in time, may lead to motor failure and damage. However, few studies have focused on the diagnosis and warning of this problem. Based on wavelet packet and neural network analysis, this paper presents a new algorithm for monitoring, diagnosing, and warning vibration caused by loose screws in the motor base. The vibration signal generated by the base screw loosening is monitored and sampled with sensors, and the wavelet packet is used to decompose, reconstruct, and reduce the noise of the vibration signal to enhance the time–frequency characteristics of the signal. After analyzing the fault data by wavelet, the feature vector characterizing the fault is extracted, and then, the vector and the corresponding fault type are used as the input and output of the neural network, respectively, and the non-mapping relationship between them is built to complete the diagnosis and early warning of the fault. Finally, the method is used to compare the motor base screw loosening operation and normal operation. The experiments show that the new algorithm based on wavelet packet and neural network can complete the health diagnosis and early warning of motor in the early stage of motor base screw loosening, reduce the loss caused by subsequent faults, and provide a new reference scheme for the motor fault diagnosis field.

Analysis and identification of transformer acoustic signal based on compressed sensing

The operation state of the transformer affects the operation of the whole power system. Aiming at the application of online monitoring of transformer operation, based on compressive sensing theory and wavelet packet analysis technology, a transformer acoustic fault diagnosis method based on compressive sensing is proposed. Firstly, the partial Hadamard matrix is constructed as the observation matrix to compress the acoustic signal of the transformer. The energy decomposition of the compressed signal is completed based on the wavelet packet. Finally, the feature selection is completed by considering the energy distribution characteristics using wavelet information entropy. The acoustic signal characteristics of transformer core fault simulation data are extracted by this method, and the fault diagnosis simulation is completed by using a particle swarm -SVM classifier. The results show that this method can obtain higher fault identification accuracy under the condition of a high compression ratio.

Experimental Study on Bridge Structural Damage Identification Based on Quasi-Static Displacement Effects and Wavelet Packet Decomposition

Based on the displacement-induced linearity error curve and the theory of wavelet packet analysis, a bridge structural damage identification method is proposed, integrating two damage indicators: Quasi-Static Displacement-Induced Linearity Error Curve (QSDIL) and Relative Energy Rate of Wavelet Packet Energy Spectrum (RES). This method first constructs the QSDIL damage indicator based on a quasi-static displacement-induced linearity error, which is used for the preliminary localization of the bridge structural damage. Subsequently, relying on the principles of wavelet packet analysis, the method constructs the RES damage indicator for accurate positioning of the damage location in the bridge structure. The proposed method is experimentally validated, and the impact of factors such as single-point damage, multi-point damage, signal noise, lane position, and vehicle weight on the experimental results is investigated. The results indicate that the proposed method exhibits excellent identification performance for the location of structural damage in both single-point and multi-point damage scenarios, with good agreement between experimental and theoretical values. As the signal-to-noise ratio decreases, the accuracy and precision of the RES curve in locating the bridge structural damage position exhibit a nonlinear decreasing trend, with relatively small identification errors observed at noise levels of 90 dB to 100 dB. Different lane positions have a minimal impact on the damage identification effectiveness. With increasing vehicle weight, both QSDIL and RES curves show an increasing trend in peak values, facilitating the localization of bridge structural damage positions.

Study on the Dynamic Stability and Spectral Characteristics of a Toppling Dangerous Rock Mass under Seismic Excitation

To evaluate the dynamic stability of dangerous rock masses under seismic excitation more reasonably, a mass viscoelasticity model was adopted to simulate the two main controlling surfaces of a toppling dangerous rock mass. Based on the principles of structural dynamics, a dynamic response analysis model and motion equations were established for toppling dangerous rock masses. The Newmark-β method was utilized to establish a calculation method for the dynamic stability coefficient of a toppling dangerous rock mass. This method was applied to the WY2 dangerous rock mass developed in a steep cliff zone in Luoyi Village, and the dynamic stability coefficient time history was calculated. Subsequently, the acceleration response signals of the dangerous rock mass in different directions were analyzed using wavelet packet transform. The results show that the sum of the energy proportions of the first to third frequency bands in the n1 and s2 directions exceeded 95%. This suggests that the n1 and s2 directions of the WY2 dangerous rock mass suffered the initial damage under bidirectional seismic actions. Finally, the marginal spectra variations of the acceleration response signals in different directions were analyzed based on the HHT. The results show that the seismic energy in the n1 and s2 directions of the dangerous rock mass was found to be the most significant under seismic loading, indicating that the rock mass experienced the most severe damage along these two directions. This reveals that the failure mode of the dangerous rock mass is inclined toppling, consistent with the results of wavelet packet analysis.

Energy-load Matching and Shockwave Analysis of Electrical Explosion

Electrical explosion shockwave is a technique for inducing formation resonance by the interruption of formation pressure balance. By matching complex nonlinear influencing factors, this paper optimizes the engineering parameters and estimates the influence range of the shockwave. It achieves the objective of efficient shockwave acquisition and amplitude control. The test apparatus rapidly discharges the stored energy of various capacitance-voltage schemes, causing the load of various diameter-length structures to generate shaped shockwaves. By characterizing the discharge circuit and the shockwave signal, the energy and load matching was studied. Extraction of shockwave signal characteristics for decomposition and reconstruction analysis. The matching results indicate that the 10 MW loop oscillation is generated within 5 μs of discharge time, with the load diameter structure getting a major effect on the deposited energy. The 50 MPa shaped shockwave is generated in a 100 mm diameter region. And the peak pressure is influenced primarily by the load length structure and voltage scheme. The optimal load structure is D = 0.2 mm and L = 25 mm, controlling 2 to 5 times the input power’s enthalpy and increasing the voltage to create the shockwave more efficiently. The results of shockwave analysis indicate that the signal’s time-domain characteristics correspond precisely with its propagation path. The wavelet packet analysis indicates that the shockwave possesses a broad frequency and a high peak. The detection signal is composed of the principal shock wave, the radiation pressure wave, and the resonance error. The results provide theoretical basis for engineering application.

Research on Common Fault Diagnosis of Mud Pump Based on Wavelet Packet Analysis

The utilization of vibration signals to diagnose faults in centrifugal pumps is a crucial research direction in this field. Traditional frequency domain processing methods, such as Fast Fourier Transform (FFT), may not be effective in analyzing complex signals where multiple types of faults occur simultaneously at the same frequency. This paper proposes the use of wavelet packet analysis as an alternative. By analyzing fault conditions caused by blade damage and clearance wear in a mud pump and comparing the results to those obtained through the FFT analysis method, the superior accuracy of wavelet packet analysis is demonstrated.

Comparative analysis of ANN performance of four feature extraction methods used in the detection of epileptic seizures

Epilepsy, a prevalent neurological disorder characterized by disrupted brain activity, affects over 70 million individuals worldwide, as reported by the World Health Organization (WHO). The development of computer-aided diagnosis systems has become vital in assessing epilepsy severity promptly and initiating timely treatment. These systems enable the detection of epileptic seizures by analyzing the electrical activity in the EEG recordings of the patients. In addition, it helps doctors to choose suitable treatment by quickly determining the type, duration, and characteristics of seizures and increases the patient's quality of life. The proposed computer-aided diagnosis system in this study comprises three modules: preprocessing, feature extraction, and classification. The initial module employs a low-pass Chebyshev II filter to eliminate noise artifacts from signal recordings. The second module involves deriving feature vectors using Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis. The third module employs the Artificial Neural Networks method for epileptic seizure detection. This study not only enables the comparison of feature extraction efficacy among Bispectrum Analysis, Empirical Mode Decomposition, Discrete Wavelet Transform, and Wavelet Packet Analysis techniques, but it also reveals that Bispectrum Analysis and Empirical Mode Decomposition yield the highest accuracy rate. The method achieves 100% accuracy in detecting epileptic seizures. Additionally, sensitivity analysis has been conducted to enhance the success of Discrete Wavelet Transform and Wavelet Packet Analysis methods and to identify significant features.

A Multi-Antenna Spectrum Sensing Method Based on CEEMDAN Decomposition Combined with Wavelet Packet Analysis

In many practical communication environments, the presence of uncertain and hard-to-estimate noise poses significant challenges to cognitive radio spectrum sensing systems, especially when the noise distribution deviates from the Gaussian distribution. This paper introduces a cutting-edge multi-antenna spectrum sensing methodology that synergistically integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), wavelet packet analysis, and differential entropy. Signal feature extraction commences by employing CEEMDAN decomposition and wavelet packet analysis to denoise signals collected by secondary antenna users. Subsequently, the differential entropy of the preprocessed signal observations serves as the feature vector for spectrum sensing. The spectrum sensing module utilizes the SVM classification algorithm for training, while incorporating elite opposition-based learning and the sparrow search algorithm with genetic variation to determine optimal kernel function parameters. Following successful training, a decision function is derived, which can obviate the need for threshold derivation present in conventional spectrum sensing methods. Experimental validation of the proposed methodology is conducted and comprehensively analyzed, conclusively demonstrating its remarkable efficacy in enhancing spectrum sensing performance.