Acoustic Wavelet Research Articles

Using Wavelet Analysis to Distinguish Cavitation Acoustic Emissions From Blunt Impact Noise.

Cavitation has been shown to have implications for head injury, but currently there is no solution for detecting the formation of cavitation through the skull during blunt impact. The goal of this communication is to confirm the wideband acoustic wavelet signature of cavitation collapse, and determine that this signature can be differentiated from the noise of a blunt impact. A controlled, laser induced cavitation study was conducted in an isolated water tank to confirm the wide band acoustic signature of cavitation collapse in the absence of a blunt impact. A clear acrylic surrogate head was impacted to induce blunt impact cavitation. The bubble formation was imaged using a high speed camera, and the collapse was synched up with the wavelet transform of the acoustic emission. Wideband acoustic response is seen in wavelet transform of positive laser induced cavitation tests, but absent in laser induced negative controls. Clear acrylic surrogate tests showed the wideband acoustic wavelet signature of collapse can be differentiated from acoustic noise generated by a blunt impact. Broadband acoustic signal can be used as a biomarker to detect the incidence of cavitation through the skull as it consists of frequencies that are low enough to potentially pass through the skull but high enough to differentiate from blunt impact noise. This lays the foundation for a vital tool to conduct CSF cavitation research in-vivo.

Nonstationary seismic inversion: joint estimation for acoustic impedance, attenuation factor and source wavelet

Seismic signal can be expressed by nonstationary convolution model (NCM) which integrates acoustic impedance (AI), attenuation factor (AF) and source wavelet (SW) into a single formula. Although it provides attractive potential to invert AI, AF and SW, simultaneously, effective joint inversion algorithm has not been developed because of the extreme instability of this nonlinear inverse problem. In this paper, we propose an alternating optimization scheme to achieve this nonlinear joint inversion. Our algorithm repeatedly alternates among three subproblems corresponding to AI, AF and SW recovery until changes in inverted models become smaller than the user-defined tolerances. Also, when we optimize one parameter, other two parameters are fixed. NCM is an explicit linear formula for AI; therefore, AI recovery is accomplished by linear inversion which is regularized by low-frequency model and isotropy total variation domain sparse constraints. However, NCM is a complicated nonlinear formula for AF. To facilitate the AF inversion, we propose a centroid frequency-based attenuation tomography method whose forward operator and observations are acquired from the time-varying wavelet amplitude spectra which is estimated according to Gabor domain factorization of NCM. SW is decoupled from NCM based on Toeplitz structure constraint, and we obtain an orthogonal wavelet transform domain sparse regularized SW inverse subproblem. Split Bregman technique is adopted to optimize AI and SW inverse subproblems. Numerical test and field data application confirm that the proposed nonstationary seismic inversion algorithm can stably generate accurate estimates of AI, AF and SW, simultaneously.

The basics of a system for evaluation of fiber-cement materials based on acoustic emission and time-frequency analysis.

Fiber-cement building products are increasingly used in construction. They are used as building and finishing material for facades, internal walls and roofs. Numerous advantages such as relatively low weight, low absorbability and relatively high strength allow to use these materials in bulky constructions and in buildings which are commonly considered tall. Safety reasons, however, point to the need to control the condition of materials used to erect such structures. It is also in line with the more and more widely implemented concept of monitoring the state of the structure and its components over their entire period of use (SHM). The article presents the results of experimental tests on flexural strength of cement-fiber boards in an air-dry state, which have been soaked in water for 24 hours and subjected to high temperature. The paper also presents a possibility to use a non-invasive method of acoustic emission and wavelet analysis for testing cement boards reinforced with cellulose fibers. Obtained results allow to track the change of mechanical parameters in boards subjected to environmental and exceptional factors. These results also confirm the applicability of the presented methods as instruments for observing the condition of the panels used.

Sensitivity Improvement of Acoustic Partial Discharge Detection Measurements through Wavelet Analysis

Condition monitoring of power equipment is a vital stepin extending the lifetime of existing equipment andreducing costs for utilities while minimizing the risk ofunscheduled outages. Partial discharge (PD) monitoringhas evolved as a reliable mean of determiningdeterioration in insulation systems. Acoustic emissiondetection techniques are usually utilized for PDdetection mainly in oil-filled transformers offering theadvantage of being immune to electrical noise and amethod to localize PDs. In this work it was attempted toimprove the sensitivity of acoustic measurementsthrough wavelet analysis and estimation of the thresholdvalue from actual measurements of the noise, whichproved to be more effective compared to otherestimation values. The analysis was performed onlaboratory measurements from a 36 kV condenserbushing known to exhibit PD activity acquired with alow cost PD acoustic sensor developed at KTH. As anext step the results have to be verified by onlinemeasurements, which can result in the addition of anCondition monitoring of power equipment is a vital step in extending the lifetime of existing equipment and reducing costs for utilities while minimizing the risk of unscheduled outages. Partial discharge (PD) monitoring has evolved as a reliable mean of determining deterioration in insulation systems. Acoustic emission detection techniques are usually utilized for PD detection mainly in oil-filled transformers offering the advantage of being immune to electrical noise and a method to localize PDs. In this work it was attempted to improve the sensitivity of acoustic measurements through wavelet analysis and estimation of the threshold value from actual measurements of the noise, which proved to be more effective compared to other estimation values. The analysis was performed on laboratory measurements from a 36 kV condenser bushing known to exhibit PD activity acquired with a low cost PD acoustic sensor developed at KTH. As a next step the results have to be verified by online measurements, which can result in the addition of an onboard signal-processing box for improved sensitivity.

Research on two-port network of wavelet transform processor using surface acoustic wavelet devices and its application

The goal of this research is to study two-port network of wavelet transform processor (WTP) using surface acoustic wave (SAW) devices and its application. The motive was prompted by the inconvenience of the long research and design cycle and the huge research funding involved with traditional method in this field, which were caused by the lack of the simulation and emulation method of WTP using SAW devices. For this reason, we introduce the two-port network analysis tool, which has been widely used in the design and analysis of SAW devices with uniform interdigital transducers (IDTs). Because the admittance parameters calculation formula of the two-port network can only be used for the SAW devices with uniform IDTs, this analysis tool cannot be directly applied into the design and analysis of the processor using SAW devices, whose input interdigital transducer (IDT) is apodized weighting. Therefore, in this paper, we propose the channel segmentation method, which can convert the WTP using SAW devices into parallel channels, and also provide with the calculation formula of the number of channels, the number of finger pairs and the static capacitance of an interdigital period in each parallel channel firstly. From the parameters given above, we can calculate the admittance parameters of the two port network for each channel, so that we can obtain the admittance parameter of the two-port network of the WTP using SAW devices on the basis of the simplification rule of parallel two-port network. Through this analysis tool, not only can we get the impulse response function of the WTP using SAW devices but we can also get the matching circuit of it. Large numbers of studies show that the parameters of the two-port network obtained by this paper are consistent with those measured by network analyzer E5061A, and the impulse response function obtained by the two-port network analysis tool is also consistent with that measured by network analyzer E5061A, which can meet the accuracy requirements of the analysis of the WTP using SAW devices. Therefore the two-port network analysis tool discussed in this paper has comparatively higher theoretical and practical value.

Reach on Micro-Motor Acoustics Fault Diagnosis Based on Loose Wavelet Neural Network

Nicro-motor voice signal contains abundant running status information as well as vibration signal, aiming at the problem that it is difficult to obtain vibration signal in the production line of micro-motor, this paper proposes a micro-motor acoustic fault diagnosis methods based on loose wavelet neural network. Wavelet packet decomposition and reconstruction algorithm is utilized to extract micro-motor voice signals in each frequency band energy as the characteristic parameters of fault characteristic parameter samples will input to improve the BP neural network for training, build up the fault type of classifier, the realization of fault intelligent diagnosis. Application results show that a reasonable design of neural network has strong ability of fault identification; use loose micro-motor acoustic wavelet neural network fault diagnosis is feasible.

Study on characteristic of gas-liquid two-phase flow noise based on acoustic emission and wave-let transform

Study on characteristic of gas-liquid two-phase flow noise based on acoustic emission and wave-let transform

Oil Pipeline Leak Detection System Based on Acoustic Wave Technology

Principle of the pipeline leak detection system is presented, and the leak detection method based on acoustic wave and wavelet analysis is studied in this paper. The dynamic pressure transmitter based on piezoelectric dynamic pressure transducer is designed. The characteristic of dynamic pressure transmitter when pipeline leak happened is analyzed. The dynamic pressure signal is suitable for pipeline leak detection for quick-change of pipeline internal pressure, while the static pressure is suitable for slow-change of pipeline internal pressure. The signal is analyzed by wavelet analysis method to detect the singularity, and the singularity is used to recognize and locate the leak. This paper indicated that the dynamic pressure signal could be adjust to this detection that the pressure changes in the pipeline. Field tests in 68.2 km pipeline segment show that the method detects pipeline leak rapidly and precisely.

Testing and evaluation of water hydraulic components by acoustic emission and wavelet analysis

Abstract In condition monitoring and fault diagnosis of machinery, vibration analysis is the major method and wavelet analysis is widely implemented. The acoustic emission (AE) method is another important and effective signal in fault diagnosis of mechanical systems. In this paper, the AE signal is applied to diagnose the piston condition of the water hydraulic motor in a water hydraulic system. The AE signal is obtained under different flow rates and torques. The wavelet-based signal processing technique is used to analyze the fluid-borne vibration and structure-borne vibration (acoustic signal). The spectrum based on wavelet analysis is effective in analyzing the acoustic signals under specific frequency ranges, compared with the conventional power spectrum density (PSD). The results show root mean square (RMS) of the wavelet-based acoustic signal analysis is very effective for the fault diagnosis of piston cracks under the different flow rates and torques.

Measuring effective interfacial shear strength in carbon fiber bundle polymeric composites

Adhesion in composite materials is often quantified using the single fiber fragmentation (SFF) test. While this method is believed to provide accurate values for the fiber–matrix interfacial shear strength (IFSS), these may not accurately reflect the macroscopic mechanical properties of specimens consisting of tows of thousands of tightly spaced fibers embedded in a resin matrix. In these types of specimens, adhesion may be mitigated by fiber twisting and misalignment, differences in the resin structure in the confined spaces between the fibers and, most importantly, by any incompleteness of the fiber wetting by the resin. The present work implements fiber band fragmentation (FBF) testing to obtain effective interfacial shear strengths, whose values reflect the importance of these factors. The fiber fragmentation in these specimens is tracked through the counting and sorting of acoustic emission (AE) events occurring during the tensile testing of the specimen and yields the average critical fiber fragment length. AE results, in conjunction with stress-strain data, show that fiber breakage events occur at acoustic wavelet amplitudes substantially greater than those generated by fiber/matrix debonding. Kelly–Tyson analysis is applied, using the measured critical fiber fragment length together with known values for the fiber diameter and tensile strength to yield the effective IFSS. FBF tests are performed on carbon fiber/poly(vinyl butyral) (PVB) dog-bone fiber-bundle systems, and effective IFSS values substantially lower than those typically reported for the single fiber fragmentation testing of similar systems are obtained, suggesting the importance of multi-fiber effects and incomplete fiber wetting.

Acoustic Wavelet and its Applications to Seismic Data Decomposition

AbstractAcoustic wavelet is one type of physical wavelets constructed based on the acoustic wave equation. Unless scattering and absorption occur, the propagation of such wavelets is straightforward; while for mathematical wavelets, even propagation in homogeneous media becomes considerably complicated.A s solutions of wave equation, acoustic wavelets are mostly suitable for the decomposition and analysis of complicated acoustic or seismic wave fields. Wu et al.[18].introduced acoustic wavelets into seismic data analysis and opened a new area for the application of physical wavelets to the study of seismic signals. In this paper, based on Kaiser's acoustic wavelet theory, physical constructions of acoustic wavelets are explained through introducing complex time function and imaginary time coordinate of point sources respectively. The acoustic wavelet transform (AWT) in space‐time domain is applied both to the synthetic seismograms of point sources and to the seismic data produced by the complicated SEG‐EAGE salt model. The obtained results further indicate the effectiveness of applications of acoustic wavelets to seismic data decomposition.

An adaptive method for tracking voicing irregularities.

A method has been developed for tracking irregularities in the acoustic waveform of a sustained phonation using the adaptive Wiener filter. Irregularities are determined by the technique of correlation cancellation. The algorithm is evaluated using sustained vowels produced by a formant synthesizer and by subjects with and without phonatary disorders. Results indicate that the method is capable of differentiating between normal and abnormal voices. Most significantly, however, it can also track sporadic or nonstationary irregularities in the shape of an individual acoustic wavelet. This method is expected to be a useful tool for the acoustics analysis of voice production.

Wavelet estimation for a multidimensional acoustic or elastic earth

A new and general wave theoretical wavelet estimation method is derived. Knowing the seismic wavelet is important both for processing seismic data and for modeling the seismic response. To obtain the wavelet, both statistical (e.g., Wiener‐Levinson) and deterministic (matching surface seismic to well‐log data) methods are generally used. In the marine case, a far‐field signature is often obtained with a deep‐towed hydrophone. The statistical methods do not allow obtaining the phase of the wavelet, whereas the deterministic method obviously requires data from a well. The deep‐towed hydrophone requires that the water be deep enough for the hydrophone to be in the far field and in addition that the reflections from the water bottom and structure do not corrupt the measured wavelet. None of the methods address the source array pattern, which is important for amplitude‐versus‐offset (AVO) studies. This paper presents a method of calculating the total wavelet, including the phase and source‐array pattern. When the source locations are specified, the method predicts the source spectrum. When the source is completely unknown (discrete and/or continuously distributed) the method predicts the wavefield due to this source. The method is in principle exact and yet no information about the properties of the earth is required. In addition, the theory allows either an acoustic wavelet (marine) or an elastic wavelet (land), so the wavelet is consistent with the earth model to be used in processing the data. To accomplish this, the method requires a new data collection procedure. It requires that the field and its normal derivative be measured on a surface. The procedure allows the multidimensional earth properties to be arbitrary and acts like a filter to eliminate the scattered energy from the wavelet calculation. The elastic wavelet estimation theory applied in this method may allow a true land wavelet to be obtained. Along with the derivation of the procedure, we present analytic and synthetic examples.