Wavelet Packet Analysis Research Articles

Entropy Analysis for Damage Quantification of Hysteretic Dampers Used as Seismic Protection of Buildings

Relative wavelet energy entropy (RWEE) is proposed to detect and quantify damage to hysteretic dampers used for the passive seismic control of building structures. Hysteretic dampers have the role of dissipating most of the energy input of an earthquake. Minor or moderate earthquakes do not exhaust the energy dissipation capacity of the dampers, yet they damage them. For this reason, continuous or periodic damper-health evaluation is required to decide if they need to be replaced. Such evaluation calls for the application of efficient structural health monitoring techniques (SHM). This paper focuses on the well-known vibration technique, which is applied to a particular type of hysteretic damper called Web Plastifying Damper (WPD), patented by the University of Granada. Vibration signals, properly recorded by piezoelectric sensors attached around the damaged area of the dampers, are decomposed by means of wavelet packet analysis. Then, the relative wavelet energy entropy of these decompositions is used to calculate the proposed index. Validation of RWEE for this particular application involved dampers installed in two different specimens of reinforced concrete structures subjected to earthquake sequences of increasing intensity. When compared with a well-established mechanical energy-based damage index, results demonstrate that RWEE is a successful and low-cost technique for reliable in-situ monitoring of dampers.

Experimental study on alarming of concrete micro-crack initiation based on wavelet packet analysis

Wavelet packet analysis and digital image correlation (DIC) have been applied extensively owing to their respective advantages. In this work, a novel approach is presented to realize micro-damage alarming and identification of concrete by the coupling of wavelet packet transform and DIC technology. DIC measurement system is utilized to measure the damage strain field of static three-point bending of concrete specimen. According to excellent characteristic of time-frequency localization of wavelet packet analysis, the strain nephograms are decomposed by wavelet packet transform. Through wavelet packet analysis, the eigenvectors of the component energy are constructed, based on which the alarming index of concrete micro-crack initiation is obtained. The experimental results show that our novel approach can effectively predict the micro-crack initiation and can automatically achieve alarming. Furthermore, the initiation position of micro-cracks and micro-damage degree can be accurately determined by damage strain field. It is beneficial to damage prediction and safety control of concrete structures.

Structure damage identification under ambient excitation based on wavelet packet analysis

There are significant changes in vibration responses when various damages appear in structures. Therefore, structural vibration responses contain rich structure damage information. In this paper, virtual impulse response of structure under ambient excitation is obtained through the inversion of a transfer function, and is decomposed into components by wavelet packet transform. Then the energy of these wavelet packet components is calculated to obtain the wavelet packet energy spectrum. And a novel damage index i.e. Energy Spectrum Anomaly Measure is developed to identify the existence of damage. Damage extent can also be inferred from the relative value of Energy Spectrum Anomaly Measure at various locations. Numerical results are presented to evaluate the effectiveness and reliability of the proposed index. It is shown that the proposed damage index works well in identifying structure damages free from the effect of various excitation and there is a positive correlation between the index and the damage severity.

Research on the characteristics of the dynamic functional connectivity network related to aging

Brain aging can affect the strength of functional connectivity between brain regions. In recent years, studies have shown that functional connectivity is fluctuant over time, and can reflect more physiological and pathological information. Therefore, in the study resting state functional magnetic resonance imaging (fMRI) data of 32 elderly subjects and 36 younger subjects were selected, and the sliding window technique was used to estimate dynamic functional connectivity network. Then, the dependency of fluctuating energy difference on frequency band was studied using wavelet packet analysis, conducting the linear regression with age at the same time. Results showed that the fluctuating energy in older group was significantly higher than that in the young group in low frequency, and it was significantly lower than that in the young people in high frequency. These results suggested that the dynamic functional connectivity between networks in the elderly exist slow wave phenomenon, which may be related to the decreased reaction rate of the elderly. This article provides new ideas and methods for the research about brain aging, and promotes a theoretical basis for further understanding of the physiological significance of brain dynamic functional connectivity.

Detection of Debonding Between Fiber Reinforced Polymer Bar and Concrete Structure Using Piezoceramic Transducers and Wavelet Packet Analysis

Fiber reinforced polymer (FRP), a composite material with high corrosion resistance and high strength-to-weight ratio, has been increasingly used in reinforced concrete structures. The effectiveness of the structures depends on the bonding behavior between FRP composites and concrete structures. Therefore, detection of the debonding between the FRP materials and the hosting concrete structure is of great importance to ensure the structural safety. This paper proposes a stress wave-based active sensing approach to monitor the debonding process of FRP bar with the hosting concrete structure. One shear-type lead zirconate titanate (PZT) patch bonded on the outer surface of the FRP bar was used as an actuator to generate stress wave. Two smart aggregates (SAs), which were fabricated by sandwiching a shear type PZT patch between two protection marble pieces, were embedded in the hosting concrete structure to detect the wave response. The occurrence of debonding between the FRP bar and the hosting concrete structure attenuates the wave propagation. An FRP bar reinforced concrete specimen was designed and fabricated in laboratory. A pullout test was conducted to simulate different degrees of debonding damage. The attenuation of the stress wave due to debonding was clearly observed from the signal received by SAs in both time and frequency domain. Furthermore, a damage index based on wavelet packet analysis was developed to evaluate the debonding status. Experimental results demonstrate that the proposed method has potentials to detect different degrees of debonding damage of FRP bar reinforced concrete composite structures.

Analysis of Energy Density Spectrum of Lowvelocity Impact in Honeycomb Sandwich Panel with FBG

As a kind of structure commonly used in satellite, carbon fiber composite honeycomb sandwich panels are vulnerable to various kinds of damage, especially low-velocity impact damage in the process of using. In this paper, an impact monitoring system based on Fiber Bragg Grating (FBG) sensor is constructed. The frequency domain information of the signal can be obtained by Fourier transform. Monitoring results of different impact points show that the natural frequency of structure is 110Hz and 380Hz, which amplitude of different natural frequency is changed with position of impact point. The energy status of impact response signal can be obtained by wavelet packet analysis. The monitoring results of the same impact point shows that the change of the sensor signal is obvious in thirteenth order and energy size is related to the distance and angle between the impact point and sensor.

Wave propagation simulation and its wavelet package analysis for debonding detection of circular CFST members

In order to investigate the interface debonding defects detection mechanism between steel tube and concrete core of concrete-filled steel tubes (CFSTs), multi-physical fields coupling finite element models constituted of a surface mounted Piezoceramic Lead Zirconate Titanate (PZT) actuator, an embedded PZT sensor and a circular cross section of CFST column are established. The stress wave initiation and propagation induced by the PZT actuator under sinusoidal and sweep frequency excitations are simulated with a two dimensional (2D) plain strain analysis and the difference of stress wave fields close to the interface debonding defect and within the cross section of the CFST members without and with debonding defects are compared in time domain. The linearity and stability of the embedded PZT response under sinusoidal signals with different frequencies and amplitudes are validated. The relationship between the amplitudes of stress wave and the measurement distances in a healthy CFST cross section is also studied. Meanwhile, the responses of PZT sensor under both sinusoidal and sweep frequency excitations are compared and the influence of debonding defect depth and length on the output voltage is also illustrated. The results show the output voltage signal amplitude and head wave arriving time are affected significantly by debonding defects. Moreover, the measurement of PZT sensor is sensitive to the initiation of interface debonding defects. Furthermore, wavelet packet analysis on the voltage signal under sweep frequency excitations is carried out and a normalized wavelet packet energy index (NWPEI) is defined to identify the interfacial debonding. The value of NWPEI attenuates with the increase in the dimension of debonding defects. The results help understand the debonding defects detection mechanism for circular CFST members with PZT technique.

Study on the natural gas pipeline safety monitoring technique and the time-frequency signal analysis method

Hydrate plugging and leakage in natural gas pipelines have been big issues for the industry with the globally increasing demand for natural gas. A natural gas pipeline safety monitoring technique is studied in this paper, which is based on acoustic excitation. In this technique hydrate plugging and leakage can be monitored online at multiple locations and distinguished by “energy-pattern” method based on the wavelet packet analysis. The position of a hydrate plugging or leakage can be achieved by correlation algorithm. The results of experiments and modeling work show that the technique can locate hydrate plugging or leakage at multiple positions with good accuracy and distinguish them effectively.

Road excitation classification for semi-active suspension system based on system response

Vehicle performance is largely affected by the properties of the suspension system, where semi-active suspension has been widely used in mass production of vehicles owing to its characteristics such as internal stability and low energy consumption. To solve the contradiction between ride comfort and road handling, road estimation based semi-active suspension has received considerable attention in recent years. In order to provide accurate estimation for advanced control strategies applications, this paper aims to develop a new method that can provide precise road class estimation based on measurable suspension system response (i.e. sprung mass acceleration, unsprung mass acceleration and rattle space). The response signal is first decomposed using wavelet packet analysis, and features in both time and frequency domains are subsequently extracted. Then, minimum redundancy maximum relevance (mRMR) is utilized to select superior features. Finally, a probabilistic neural network (PNN) classifier is applied to determine road classification output. The most representative semi-active control strategy, i.e. skyhook control, is used to validate this method, and simulation results with varying conditions including different control parameters and sprung mass are compared. The results show that unsprung mass acceleration is most suitable for road classification, and more robust to varying conditions in comparison to other responses.

Online Condition Monitoring of a Rail Fastening System on High-Speed Railways Based on Wavelet Packet Analysis.

The rail fastening system is an important part of a high-speed railway track. It is always critical to the operational safety and comfort of railway vehicles. Therefore, the condition detection of the rail fastening system, looseness or absence, is an important task in railway maintenance. However, the vision-based method cannot identify the severity of rail fastener looseness. In this paper, the condition of rail fastening system is monitored based on an automatic and remote-sensing measurement system. Meanwhile, wavelet packet analysis is used to analyze the acceleration signals, based on which two damage indices are developed to locate the damage position and evaluate the severity of rail fasteners looseness, respectively. To verify the effectiveness of the proposed method, an experiment is performed on a high-speed railway experimental platform. The experimental results show that the proposed method is effective to assess the condition of the rail fastening system. The monitoring system significantly reduces the inspection time and increases the efficiency of maintenance management.

Influence of tunnel excavation blasting vibration on earth's surface based on wavelet packet analysis

In this work, based on the blasting vibration live data collected, the energy distributions at different frequency bands for the blasting vibration signals were obtained by the wavelet packet analysis technology. By comparing the results of each point analysis it was found that the tunnel excavation blasting seismic wave energy mainly concentrated in the tangential and radial directions; along the direction of the tunnelling, with the increase of the distance between the tunnel faces, the main frequency of the seismic wave decreased and the energy concentration frequency band became narrower and developed to the low frequency band; however, along the vertical direction of the tunnel, the main frequency decreases with the decrease of the depth and, at the same time, the wave energy concentration frequency band became narrower and developed to the low frequency band. The results show that wavelet packet analysis is an effective approach to estimating the structure safety under blasting vibration conditions.

Automatic licence plate detection using adaptive neuro fuzzy inference system and wavelet packets

This paper presents an automatic licence plate detection (ALPD) system to improve the road safety and to monitor the vehicle movement on the streets. The system comprises a surveillance camera mounted at possible speeding points or at illegal parking zones which captures images and processes them to identify the errant vehicle owners for initiating appropriate legal action. The proposed scheme is cost-efficient and substantially reduces the manpower required to monitor the streets. ALPD uses a combination of K-Means Clustering and adaptive neuro-fuzzy inference system (ANFIS) to detect the Licence Plate from a given image. Wavelet Packet analysis and ANFIS are used to identify the alphanumeric characters. The scheme was tested on a sample of 52 test images pertaining to different configurations of the licence plate in terms of its location or font and using diverse colours of the vehicles, and was determined to have an accuracy of 98.3% and an average computational time of 900 ms. The algorithm was found to be robust, time efficient and has high recognition rate compared to many other schemes currently in practice. The use of Wavelet Packet analysis also makes the proposed scheme immune to variations pertaining to the font of the characters used for the licence plates.

Pattern Identification of Subthalamic Local Field Potentials in Parkinson’s Disease

The local field potentials (LFP) in Parkinson’s disease (PD), which contain abundant information related to disease and symptoms, are important for clinical treatment. The amplitudes of oscillations in LFP and the balance between them were found to be involved in brain functional state of PD patients. The LFP recorded from subthalamic nucleus before and after medication treatment were selected in this study. The power spectral ratio between frequencies and the percentage of energy corresponding to the wavelet packet nodes in the overall signal energy based on wavelet packet analysis related to symptoms were extracted as features. The brain states related to medication treatment in patients were classified using machine learning. The Naive Bayesian classifier and support vector machine (SVM) classifier were used to classify the states of off and on medication conditions. The results showed that the Naive Bayesian classifier was better than SVM classifier with higher accuracy. The specificity of Naive Bayesian classifier reached to 82.4%. The method proposed in this paper can accurately identify the brain functional state of PD patients.

Investigation of Stability Alarming for Retaining Wall Structures with Damage

To warn of the stability of retaining wall structures with damage, a simplified mechanical model and a finite element model of this retaining wall-soil coupling system are established. Via finite element model updating, a baseline finite element model of the wall-soil system is acquired. A damage alarming index ERSD (Energy Ratio Standard Deviation) is proposed via the wavelet packet analysis of a virtual impulse response function of dynamic responses to this baseline finite element model. The internal relationships among the alarming index, earth pressure, and damage stability of the wall are analyzed. Then, a damage stability alarming method for the retaining walls is advanced. To verify the feasibility and validity of this alarming method, vibration tests on the baseline finite element model of a pile plate retaining wall are performed. The ERSD is used as an alarm for the damage stability of the wall. Analysis results show that, with an increase in the ERSD, the stability of the wall changes from a stable state to an unstable one. The wall reaches a critical stable state when the alarming index reaches its threshold value. Thus, the damage stability of this pile plate retaining wall can be alarmed via ERSD.

Damage identification based on wavelet packet analysis method

Damage identification based on wavelet packet analysis method

Monitoring of Concrete Cylinders With and Without Steel Fibers Under Compression Using Piezo-Ceramic Smart Aggregates

An active sensing approach is employed to analyze the damage behavior of concrete cylinders under compression using smart aggregates. The smart aggregate sensor used in this study is developed by embedding a soldered water-proof piezo-ceramic patch inside stiff cement mortar. These smart aggregates are embedded at the desired locations in the concrete cylinders. A pair of smart aggregates is used with one acting as an actuator and other as a sensor. Wavelet packet analysis is used to analyze the recorded signals. Variation of cracking pattern observed at different levels of loading modifies the signal characteristics. Experimental investigations are carried out by testing eight concrete cylinders with and without steel fibers. The experimental result shows that the proposed method of using smart aggregates and evaluating damage index based on wavelet analysis is effective in monitoring the health of the concrete cylinders. Compression tests on steel fiber reinforced concrete cylinders indicate that the addition of fibers improves the ductility and post peak behavior under compression. Damage index variation shows that the steel fibers resist the crack propagation and results in improved performance.

Numerical study on the mechanism of active interfacial debonding detection for rectangular CFSTs based on wavelet packet analysis with piezoceramics

In recent years, Piezoelectric Lead Zirconate Titanate (PZT) based active interfacial debonding defect detection approach for concrete-filled steel tubular (CFST) columns has been proposed and validated experimentally. In order to investigate the mechanism of the PZT based interfacial debonding detection approach, a multi-physics coupling finite element model (FEM) composed of surface-mounted PZT actuator, embedded PZT sensor and a rectangular CFST column is constructed to numerically simulate the stress wave propagation induced by the surface-mounted PZT actuator under different excitation signals with different frequency and amplitude. The measurements of the embedded PZT sensor in concrete core of the CFST columns with different interfacial debonding defect lengths and depths are determined numerically with transient dynamic analysis. The linearity between the PZT response and the input amplitude, the effect of different frequency and measurement distance are discussed and the stress wave fields of CFST members without and with interface debonding defects are compared. Then, the response of the embedded PZT in concrete core is analyzed with wavelet packet analysis. The root mean square deviation (RMSD) of wavelet packet energy spectrum of the PZT measurement is employed as an evaluation index for the interfacial debonding detection. The results showed that the defined index under continuous sinusoidal and sweep frequency signals changes with the interfacial defects length and depth and is capable of effectively identifying the interfacial debonding defect between the concrete core and the steel tubular. Moreover, the index under sweep frequency signal is more sensitive to the interfacial debonding. The simulation results indicate that the interfacial debonding defect leads to the changes in the propagation path, travel time and the magnitude of stress waves. The simulation results meet the findings from the previous experimental study by the authors and help understand the mechanism of interfacial debonding defect detection for CFSTs using PZT technology.

Predicting Preterm Delivery Based on Wavelet Packet Analysis of a Single Electrohysterography Channel

Fraiwan, L. (2016). Predicting preterm delivery based on wavelet packet analysis of a single electrohysterography channel. Journal of Medical Imaging and Health Informatics, 6(6), 1419-1425.

Automatic Speech Segmentation Using Hybrid Wavelet Features and HMM

In this research, a novel feature set is used to automatically segment speech signal. Automatic segmentation is veryuseful especially for large database. A hybrid features model is created from wavelet packet analysis and mel-scale is used to train Hidden Markov Model (HMM) for phone boundary detection. HMM is implemented using the Hidden Markov Model Toolkit (HTK).The database (Ked-TIMIT) is used for result verifications and Mel Frequency Cepstral Coefficients (MFCC) is used as reference for evaluating the results of the proposed Hybrid model. The results are categorized for vowels, consonants and short phones. Phone duration and start location are used as metrics to evaluate the system success rate. Success rate of 74% is achieved for consonant detection, 72% for vowel detection and 58% for short phone detection. Using the simple metric that relies only on boundary locations but ignoring duration, the achieved results are 92.5% for consonant detection, 90% for vowel detection and 77.5% for short phoneme detection. In addition to boundary detection the proposed hybrid model is utilized to compare newly developed features called Mel scale Best Tree Encoding (Mel-BTE ) to the mostly used popular features MFCC along with all experiments using the same database. The relative results for Mel-BTE with respect to MFCC are 94.77% for consonant detection, 87.5% for vowel detection and 93.33% for short phoneme detection.

PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons.

Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% ± 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% ± 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.