Wavelet Scalogram Research Articles

Erratum to “Structural Pounding Detection by Using Wavelet Scalogram”

Erratum to “Structural Pounding Detection by Using Wavelet Scalogram”

Localization of acoustic-emission sources in objects with small geometric dimensions

The problems of the localization of sources of acoustic-emission (AE) signals for objects with small geometric dimensions are considered. The dependence of the localization error for a flaw inside a piezoelectric antenna on the error in determining the difference in the times of signal arrival (DTA) at acoustic transducers was obtained. A method for determining the DTA using wavelet scalograms of AE signals was studied. Experimental studies showed the advantages of this method over the method for determining the rms deviation of AE-signal amplitudes within a “time window.”

Structural Pounding Detection by Using Wavelet Scalogram

Structural pounding can cause considerable damage and even lead to collapse of structures. Most research focuses on modeling, parameter investigation, and mitigation approaches. With the development of structural health monitoring, the on-line detection of pounding becomes possible. The detection of pounding can provide useful information of potential damage of structures. This paper proposed using wavelet scalograms of dynamic response to detect pounding and examined the feasibility of this method. Numerical investigations were performed on a pounding system that consisted of a damped single-degree-of-freedom (SDOF) structure and a rigid barrier. Hertz contact model was used to simulate pounding behavior. The responses and pounding forces of the system under harmonic and earthquake excitations were numerically solved. The wavelet scalograms of acceleration responses were used to identify poundings. It was found that the scalograms can indicate the occurrence of pounding and occurrence time very well. The severity of the poundings was also approximately estimated. Experimental studies were carried out, in which shake table tests were conducted on a bridge model that underwent pounding between its different components during ground motion excitation. The wavelet scalograms of the bridge responses indicated pounding occurrence quite well. Hence the conclusions from the numerical studies were verified experimentally.

Testing Stationarity with Wavelet-Based Surrogates

AbstractTraditional methods of using surrogate data to test for linearity of time-series data can be extended into the time-frequency domain to test for stationarity. Surrogates are time series that are created directly from the original dataset through manipulation and seek to replicate important properties of the original dataset. By comparing the original signal to the surrogates, additional structure in the original dataset not explained by the replicated properties may be revealed. The surrogates used for the purpose of testing stationarity are stationarized versions of an original signal that have the same Fourier amplitudes, but have randomized phases. Wavelet analysis is used in this method to transform the signals into the time-frequency domain and wavelet scalograms are used to quantitatively compare the original signal to the stationarized surrogate signals. Methods introduced in previous research compare the local and global spectral features of the surrogate signals with the local and global ...

Wavelet Frequency Estimation Parameter Of Energy Distribution For Electrooculograph Signal Analysis

Pengajian ini mengkaji electrooculograph (EOG) isyarat pola gerakan mata. Perilaku dari isyarat gerakan mata dijelaskan menggunakan kaedah wavelet dan digabungkan dengan ciriciri pengedaran tenaga. Ciri–ciri yang berasal dari isyarat EOG daripada empat jenis pergerakan mata dan dicatat menggunakan Sistem Akuisisi Data EEG, EEG Neurofax–9200. Elektrodelektrod tersebut diletakkan di dahi dan di bawah mata. Data diperolehi daripada 15 subjek di dalam bilik yang senyap, di mana data yang tercatat terdiri daripada empat gerakan mata yang berbeza, iaitu pergerakan ke atas, ke bawah, ke kiri dan ke kanan. Algoritma Wavelet scalogram digunakan untuk menganalisa isyarat yang direkodkan kerana ia mampu untuk menunjukkan amaun tenaga isyarat EOG pergerakan mata dengan perubahan masa dan frekuensi. Hasil kajian menunjukkan bahawa amaun tenaga isyarat EOG menunjukkan pola yang berbeza dalam gerakan–gerakan berikut: tahap 6 (8–16 Hz) untuk gerakan mata ke kiri; tahap 7 (4–8 Hz) untuk gerakan ke atas; tahap 8 (2 – 4 Hz) untuk gerakan ke kanan dan peringkat 9 (1–2 Hz) untuk gerakan ke bawah. Kata kunci: Electro–oculogram; gerakan mata; tenaga isyarat; transformasi wavelet; scalogram The study investigates the electrooculograph (EOG) signals of eye movement patterns. The behaviours of the eye movement signal is described using wavelet method and combined with the energy distribution features. The features are derived from EOG signals of four type eye movement and recorded using the EEG Data Acquisition System Neurofax EEG–9200. The electrodes were attached to the subjects on the forehead and below the eye. The data is acquired from 15 subjects in a quiet room, in which the recorded data is composed by four different eye movements that are upward, downward, towards to left and towards to right. Wavelet scalogram algorithm is used as the tool because of its capable to distribute the EOG signals energy of eye movement with the change of time and frequency. From the results, it indicated that the energy distribution of EOG signals exhibit different patterns in their corresponding movements as follow: level 6 (8–16 Hz) for left eye movement; level 7 (4–8 Hz) for upward; level 8 (2–4 Hz) for right and level 9 (1–2 Hz) for downward. Key words: Electro–oculogram; eye movement; signal potentials; wavelet transform; scalogram

A New Method for Electromagnetic Time Reversal in a Complex Environment

A new time reversal method is proposed to infer the location of an unknown extending target in a complex environment. In our method, the wavelet analysis and scalogram of energy density are first introduced into the electromagnetic time reversal process to improve the accuracy and to eliminate noises. The computation results show that the new technique can refocus the unknown target better than using traditional time reversal.

Spatial and Temporal Features of Regional Variations in Mean Sea Level around Taiwan

Satellite altimeter and in-situ tide gauge records are probably the most common means to obtain observational data for the study of changes in mean sea level. In this study, we employed these data to discuss the spatial and temporal features of regional variations in mean sea level around Taiwan. The results showed that most of the regional mean sea surface heights (SSH) around Taiwan are higher than the global mean sea surface heights. Most of the sea level trends are greater than the global mean sea level trend as well. We obtained diverse distribution results from the altimeter sea level records in neighboring areas by distributions fit, and the altimeter sea level records showed obvious inhomogene- ity. In addition, periodic fluctuations in the records regarding mean sea level were revealed in our study, based on Fourier spectra and wavelet scalograms.

Self potential signals preceding variations of fumarole activity at Merapi volcano, Central Java

This paper analyzes simultaneous self-potential and gas temperature variations recorded at Merapi volcano in spring 2011, the dry season shortly after the volcanic crisis 2001. Temporal variations of fumarole gas temperature show characteristic quasi periodic signals at scales 1–8h and amplitudes up to ten degrees. We propose a simple graphical technique combining a wavelet scalogram and a cross-correlation analysis to demonstrate that the variations of gas temperature are systematically preceded by self-potential variations at the same scales. The influence of meteorological variations on these correlated signals can be ruled out. Rather, we suggest them to be related to the magma degassing in the upper conduits of the volcano. We discuss a semi-qualitative model to explain this correlation and the observed phase shift of about two hours.

Identification of Electrooculography Signals Frequency Energy Distribution Using Wavelet Algorithm

Problem statement: The time frequency analysis of non-stationary signals has been the considerable research effort in recent years. Wavelet transform is one of the favored tool for the analyzing the biomedical signals. Approach: We describe the identification of Electro-Oculograph (EOG) signals of eye movement potentials by using wavelet algorithm which gives a lot of information than FFT. The capability of wavelet transform was to distribute the signal energy with the change of time in different frequency bands. This will showed the characteristic of the signals since energy was an important physical variable in signal analysis. The EOG signals were captured using electrodes placed on the forehead around the eyes to record the eye movements. The wavelet features used to determine the characteristic of eye movement waveform. This technique adopted because it was a non-invasive, inexpensive and accurate. The new technology enhancement has allowed the EOG signals captured using the Neuronal EEG-9200. The recorded data was composed of an eye movement toward four directions, i.e., downward, upward, leftward and rightward. The proposed analysis for each eyes signal is analyzed by using Wavelet Transform (WT) with energy algorithm and by comparing the energy distribution with the change of time and frequency of each signal. Results: A wavelet Scalogram was plotted to display the different percentages of energy for each wavelet coefficient towards different movement. Conclusion: From the result, it is proved that the different EOG signals exhibit differences in signals energy with their corresponding scale such as leftward with scale 6 (8- 16Hz), rightward with scale 8 (2-4Hz), downward with scale 9 (1-2Hz) and upward with level 7 (4-8Hz). Statistically, the results in this study indicate that there are 93% (averages) significance differences in the extracted features of wavelet Scalogram analysis.

Passive acoustic diagnostics of an atmospheric pressure linear field jet including analysis in the time-frequency domain

This study presents the results of passive acoustic diagnostics of an atmospheric pressure linear field jet source operating inflowing helium. Variations of the electrical, optical and acoustic parameters of the source are monitored with respect to changing gas flow rate [0.5–7 liters per minute (lpm)] and applied voltage amplitude (3.5–7 kV). It was found that the jet length and coupled power were maximized when the jet flow was laminar. Flow mode transitions (buoyant, laminar and turbulent) associated with the jet Richardson number could easily be distinguished acoustically through their unique wavelet scalograms. Each scalogram can also be explained through qualitative correlation with the discharge electrical parameters. As the jet became turbulent, low frequency oscillation (c. 180 Hz) bursts were present in the time-frequency trace which were compared to an empirical relation for the Richardson number in the case of flow induced oscillations. It was found that the frequency value correlated well with the frequency of 200 Hz determined from literature. Anomalous sparking events were detected which manifested as almost constant magnitude, broadband acoustic transients in the time-frequency domain. Comparisons between the average acoustic output power and the average dissipated power from the discharge at two different flow rates (4 lpm and 2 lpm) reveal an approximately equal linear trend for a fixed microphone-discharge placement while in the laminar flow regime. Due to the increased turbulence induced noise, however, no such linear correlation could be drawn. Finally, optical emission spectra from the discharge at a point 8 mm downstream of the plume exit were taken and correlations drawn for each flow regime.

Experimental research into time–frequency characteristics of cavitation noise using wavelet scalogram

The cavitation has become the main cause of the damage to the hydraulic machine. Cavitation detection is very important to guarantee the safe running of the hydraulic machine. The sound, especially the audible sound, based methods are becoming attractive due to their simplicity and logicality in the application. However, the cavitation noise is easy to be contaminated by the background noise. In order to understand the characteristics of the cavitation noise deeply, using the wavelet scalogram analysis, this paper presents an experimental study to investigate the time–frequency characteristics of the cavitation noise of various cavitation states and the relation between the cavitation noise and the cavitation process. In addition, the method of parameters optimization for the wavelet is used to improve the transform performance of the wavelet scalogram. The results show that: the cavitation noise is composed of the components with wide band frequency and obvious impulse feature; but the cavitation noise of different cavitation stages has different time–frequency characteristics and compositions; in addition, the cavitation noise can be distinguished from the background noise because they have totally different frequency characteristic. This study validates that the cavitation noise can be used to detect the cavitation state and monitor the cavitation process.

Feature Extraction Accuracy Improvement of Acoustic Signals Based on Reassigned Wavelet Scalogram

Based on the wavelet scalogram obtained by Morlet wavelet transform and hard threshold de-noising filtering for typical acoustic emission signals, region segmented location method is introduced to get the number and accurate values of the characteristic frequencies, therefore the error induced by misjudgment and misreading can be avoided effectively. Then considering the weakness of large characteristic frequency error in Morlet wavelet scalogram, the feature extraction accuracy has been improved by combing region segmented location method and reassigned wavelet scalogram. Simulation results show that the proposed method has the merits of well rapidity, high reliability and briefness, hence can realize high precision feature extraction and has great practical value.

TIME-FREQUENCY CHARACTERIZATION OF FETAL PHONOCARDIOGRAPHIC SIGNALS USING WAVELET SCALOGRAM

Fetal phonocardiography is a simple and noninvasive diagnostic technique for surveillance of fetal well-being. The fetal phonocardiographic (fPCG) signals carry valuable information about the anatomical and physiological states of the fetal heart. This article is concerned with a study of continuous wavelet transform (CWT)-based scalogram in analyzing the fPCG signals. The scalogram has both spatial and frequency resolution powers, whereas traditional spectral estimation methods only have the frequency resolution ability. The fPCG signals are acquired by a specially developed data recording system. Segmentation of these signals into fundamental components of fetal heart sound (S1 & S2) is carried out through envelope detection and thresholding techniques. CWT-based scalogram is used for time-frequency characterization of the segmented fPCG signals. It has been shown that the wavelet scalogram provides enough features of the fPCG signals that will help to obtain qualitative and quantitative measurements of the time-frequency characteristics of the fPCG signals and consequently, assist in diagnosis. The proposed method for time-frequency analysis (TFA) and the associated pre-processing have been shown to be suitable for the characterization of fPCG signals, yielding relatively good and robust results in the experimental evaluation.

APPLYING THE WAVELET TRANSFORM TO STUDY THE FEATURES OF FREAK WAVES

The issues of freak waves are more and more popular since the late 1980s. This study tries to use the wavelet scalogram of freak wave records to investigate the energy characteristics during the occurrence of the freak waves. Through the analysis of the wave energy and phase, it is found that as freak waves occur, the component waves will lead to constructive superposition due to similar phases. The wavelet scalogram provides the other idea to explain the feature of freak waves.

Feature Extraction of Acoustic Signals Based on Complex Morlet Wavelet

This article studies feature extraction of acoustic signals based on complex Morlet wavelet. Since, parameter optimization is the important and difficult point of complex Morlet wavelet application. In this article, a new 2-parameter process optimization algorithm is proposed, i.e., ascertaining a parameter, getting the optimized value of the other parameter, and then repeating the process until the system performance index is satisfied. Based on the optimal wavelet scalogram obtained from applying wavelet transform to typical acoustic emission (AE) signal, the region segmented location method is designed to judge exactly the number of AE signal feature frequencies whose exact values are then calculated. By this way, the error induced by misjudgment and misreading can be avoided effectively. Finally, the optimal complex Morlet wavelet obtained from 2-parameter process optimization algorithm is compared with the extraction results of the traditional Morlet wavelet on acoustic signal feature. Simulation results show that such methods can improve precision of acoustic signal feature extraction and have good engineering value.

WAVELETS WITH RIDGES: A HIGH-RESOLUTION REPRESENTATION OF CATACLYSMIC VARIABLE TIME SERIES

Quasi-periodic oscillations and dwarf nova oscillations occur in dwarf novae and nova-like variables during outburst and occasionally during quiescence, and have analogues in high-mass X-ray binaries and black-hole candidates. The frequent low coherence of quasi-period oscillations and dwarf nova oscillations can make detection with standard time-series tools such as periodograms problematic. This paper develops tools to analyse quasi-periodic brightness oscillations. We review the use of time-frequency representations in the astronomical literature, and show that representations such as the Choi-Williams Distribution and Zhao-Atlas-Marks Representation, which are best suited to high signal-to-noise data, cannot be assumed a priori to be the best techniques for our data, which have a much higher noise level and lower coherence. This leads us to a detailed analysis of the time-frequency resolution and statistical properties of six time-frequency representations. We conclude that the wavelet scalogram, with the addition of wavelet ridges and maxima points, is the most effective time-frequency representation for analysing quasi-periodicities in low signal-to-noise data, as it has high time-frequency resolution, and is a minimum variance estimator. We use the wavelet ridges method to re-analyse archival data from VW Hyi, and find 62 new QPOs and 7 new long-period DNOs. Relative to previous analyses, our method substantially improves the detection rate for QPOs.

Feature Analysis of Mechanical Fault Signals Based on the Wavelet Transform Technique

Simulated four different fault signals in the lab, the authors then used wavelet scalogram and amplitude spectrum to make analysis on the above four fault signals and abstract each spectrum characteristics. Wavelet scalogram was able to extract the characteristic’s frequency, show the impact components caused by rub-impact, show the beat phenomenon caused by oil whip and show the irreducible high frequency components as well as the complex low-frequency components. Amplitude spectrum was able to show the energy size distribution at various frequency bands and able to analyze and calculate the relationship between various frequency components. Thus they express the relationship between various frequency banks from a quantitative manner. Therefore, combining the wavelet scalogram and amplitude spectrum when making analysis, as they complement and verify each other, it will enhance the reliability when extract and analyze the characteristics of fault signal.

Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

Observation of time-frequency characteristics of the acoustic emission from defects in rolling element bearings

Parameter analysis is a traditional and widely used method for the analysis of acoustic emission (AE) signals due to its simplicity and easy realisation. However, this approach has limitations and is not suitable for detailed analysis of the signals, and waveform-based methods are becoming attractive for the analysis of AE signals. This paper uses waveform-based analysis methods to investigate the time-frequency characteristics of AE signals arising from defects in rolling element bearings. This includes the composition of the signals, the generation mechanism of the defects and the influence of the operational condition of the bearing on these characteristics. The wavelet scalogram of the continuous wavelet transform is used to perform the qualitative analysis and the discrete wavelet transform is used for the quantitative analysis. The results demonstrate that the operational condition of the bearing will influence the time domain, the frequency domain and the time-frequency domain characteristics of the AE signals in various ways. The results also prove that the wavelet transform, and especially the wavelet scalogram, has great potential for the waveform-based analysis of AE signals from defects.

Distinguishing Cancer and Normal Breast Tissue Autofluorescence Using Continuous Wavelet Transform

We study the spectral features of the polarized fluorescence spectra of normal and cancerous human breast tissues through continuous wavelet transform, which clearly identifies distinguishing features between the tissue types. After pinpointing these robust features in the wavelet scalogram, we systematically study the autocorrelation property of the wavelet coefficients of the fluorescence spectra, which is found to differentiate normal and malignant tissues with high sensitivity. The intensity difference of parallel and perpendicularly polarized fluorescence spectra is subjected to investigation, since the same is relatively free of the diffusive background.