Continuous Acoustic Emission Signal Research Articles

Acoustic Emission Response and Damage Process for Q235 Steel in an in Situ Tensile Test

Q235 steel is widely used in engineering and construction. Therefore, it is important to identify the damage mechanism and the acoustic emission (AE) response of the material to ensure the safety of structures. In this study, an AE monitor system and an in situ tensile test with an optical microscope were used to investigate the AE response and insight into the damage process of Q235 steel. The surface of the specimen was polished and etched before the test in order to improve the quality of micrographs. Two kinds of AE responses, namely a burst and a continuous signal, were recorded by the AE monitor system during the test. Based on the in situ test, it was observed that the damage of Q235 steel was induced by the crystal slip and the inclusion fracture. Since the crystal slip was an ongoing process, continuous AE signals were produced, while burst AE signals were possibly produced by the inclusion fracture which occurred suddenly with released higher energy. In addition, a great number of AE signals with high amplitude were observed during the yielding stage and then the number and amplitude decreased.

The wear detection of mill-grinding tool based on acoustic emission sensor

The monitoring of tool wear plays an important role in improving the processing efficiency and reducing the production cost of enterprises. This paper is focused on the detection of electroplated diamond mill-grinding tools by using the acoustic emission sensor. The wear stages of mill-grinding tools are divided into three parts, namely initial wear stage, normal wear stage, and severe wear stage. The characteristic parameter method and the waveform analysis method are applied to analyze the acoustic emission signals. The wear characteristics of the tool and workpiece in different wear stages are observed and analyzed. The results indicate that the acoustic emission waveform is relatively stable in the initial wear stage, and the continuous acoustic emission signal is dominated. Moreover, the diamond abrasive grains are mainly worn and slightly broken in the normal wear stage, and there are some pits on the machined workpiece surface after the initial wear stage. In the severe wear stage, most of the abrasive grains are broken or broken in a large area, and there are burst acoustic emission signals in the waveform.

СРАВНЕНИЕ ОСНОВНЫХ ЧАСТОТНО-ВРЕМЕННЫХ ПРЕОБРАЗОВАНИЙ СПЕКТРАЛЬНОГО АНАЛИЗА СИГНАЛОВ АКУСТИЧЕСКОЙ ЭМИССИИ

Due to the intensive development of spectroscopic techniques for detecting acoustic emission signals, the problem of providing the best time-frequency resolution through the application of specific time-frequency transformation algorithms comes to the fore. The Short-Time Fourier Transform, the Wavelet Transform, the Smoothed Pseudo Wigner Distribution, the Choi-Williams Distribution, and the Hilbert-Huang Transform are currently the main time-frequency transformations used or integrated into the acoustic emission method. However, today in the literature, there is not enough information that allows evaluating time-frequency transformations regarding the effectiveness of their application to specify the features of discrete and continuous acoustic emission signals. On this basis, the authors carried out an experimental comparison of synthetic and actual model signals to determine the efficiency of specified time-frequency transformations. The synthetic model signals were a chirp signal, ideal sinusoids, and a Dirac delta function. The actual signals were a discrete acoustic emission signal from the Hsu Nelson source decomposed into dispersion modes in the acoustic channel and a continuous acoustic emission signal from the air outflow through a calibrated hole. The analysis shows that only the Fourier transform and the Wavelet transform can define all control features of model signals at the frequency components’ energy gap of about 25 dB. Wigner Distribution, Choi-Williams Distribution, and Hilbert-Huang Transform demonstrated higher time-frequency resolution did not identify frequency components of low energy. Therefore, the authors recommend using them to identify spectral changes in the resonance and discrete signals but in the narrow energy range. The Fourier transform and the Wavelet transform demonstrated the best result to analyze continuous acoustic emission. However, to use the latter, the procedure of selection of the optimal basis function is necessary. The study determined that the Hilbert-Huang transform allows identifying the frequency fluctuations, but it is necessary to develop ways to increase sensitivity and extract basic information from the spectrograms to enhance the validity of its results.

Continuous Leak Location in Gas-Filled Steel Pipe Based on Optimum Wavelet Analysis of Leak AE Signal

Abstract This study is investigates how to reconstruct the continuous leak acoustic emission (AE) signal to precisely locate the leak source on a gas-filled steel pipe based on cross-correlation location theory. Thus, in this article, the determination method for the optimum mother wavelet was proposed. The frequency distribution of the continuous leak AE event was firstly extracted through wavelet transform (WT) with optimum mother wavelet. In the considered frequency range, the WT coefficients were further used to reconstruct the raw continuous leak AE signal. The correlation-based location experiments were finally carried out to evaluate the performance of the proposed method. The results show that the location errors of the leak source were decreased by some 10 % compared with those achieved by only using the raw AE signals. Thus, improvement verifies the validity of choice of the optimum mother wavelet. Meanwhile, it is also founded that, whether the reconstructed signals or raw signals were used, the localization accuracy of the leak source using the speed of the T wave is greater than that using the speed of the L wave and theoretical acoustic speed under the same symmetry of the AE sensors with reference to the leak source. Therefore, defining the acoustic speed with the speed of the T wave was recommended to use on-site for detection of the continuous leak source on a gas-filled steel pipe.

Acoustic Emission Detection and Analysis Method for Health Status of Lithium Ion Batteries.

The health detection of lithium ion batteries plays an important role in improving the safety and reliability of lithium ion batteries. When lithium ion batteries are in operation, the generation of bubbles, the expansion of electrodes, and the formation of electrode cracks will produce stress waves, which can be collected and analyzed by acoustic emission technology. By building an acoustic emission measurement platform of lithium ion batteries and setting up a cycle experiment of lithium ion batteries, the stress wave signals of lithium ion batteries were analyzed, and two kinds of stress wave signals which could characterize the health of lithium ion batteries were obtained: a continuous acoustic emission signal and a pulse type acoustic emission signal. The experimental results showed that during the discharge process, the amplitude of the continuous acoustic emission signal decreased with the increase of the cycle times of batteries, which could be used to characterize performance degradation; there were more pulse type acoustic emission signals in the first cycle of batteries, less in the small number of cycles, and slowly increased in the large number of cycles, which was in line with the bathtub curve and could be used for aging monitoring. The research on the health of lithium ion batteries by acoustic emission technology provides a new idea and method for detecting the health lithium ion batteries.

Condition monitoring of hydraulic cylinder seals using acoustic emissions

In recent years, there has been a growing concern for fluid spill from hydraulic cylinders in the offshore oil and gas industry. To diagnose the leakage from hydraulic cylinders, there have been attempts made in literature using fluid and pressure-based condition monitoring techniques. However, there have been limited attempts to monitor leakage from hydraulic cylinders using acoustic emissions. Therefore, in this paper, an attempt has been made to understand the fluid leakage in the hydraulic cylinder based on acoustic emissions. An experimental study was performed using a test rig (with a water-glycol as hydraulic fluid) which closely replicates the operation of a hydraulic cylinder. As piston rod seal failure is the foremost cause for leakage, experiments were performed using unworn, semi-worn, and worn piston rod seals. For each seal condition, experiments were performed for five strokes at pressure conditions of 10, 20, 30, and 40 bar. In this study, the continuous acoustic emission signal was observed for each hydraulic cylinder stroke. Acoustic emission data was analysed using different techniques such as time domain, frequency domain, and time-frequency technique. By using acoustic emission features such as root mean square (RMS), peak, skewness, median frequency, and mean frequency, it was possible to identify and separate non-leakage and leakage conditions in the test rig. By using AE bandpower and power spectral density features, it is also possible to identify the leakage due to semi-worn seal and worn seal in the test rig. This study lays a strong basis to develop a real-time monitoring technique based on acoustic emissions to monitor the health of piston rod seals used in the hydraulic cylinder in the offshore industry.

Experimental investigation of the fault diagnosis of typical faults in reciprocating compressor valves

The failure of suction/discharge valves is the most common cause of unscheduled compressor shutdowns; therefore, the in-time fault diagnosis of valves is crucial to the reliable operation of reciprocating compressors. Major valve faults include leakage, valve flutter, delayed closing, and improper lift. To determine the features for diagnosing these typical valve faults, this paper presents an experimental study of the fault diagnosis of reciprocating compressor valves with acoustic emission technology and simulated valve motion. The measured AE signals and simulated valve motions of normal and failed valves are studied. The results of the fault diagnosis indicate that an earlier occurrence of the suction process can diagnose suction valve leakage and that an earlier occurrence of the discharge process can be used for detecting discharge valve leakage. The leakage also causes an increase in the amplitude of the continuous acoustic emission signal. Valve faults resulting from improper valve lift can be diagnosed by the amplitude of the burst acoustic emission signal. The number of burst acoustic emission signals and the shape of the simulated valve motion can be used to monitor the valve flutter conditions. The location where the valve closes can diagnose a valve-delayed closing fault.

OS1814 エンジンガスケットにおけるリークの検知と挙動評価へのAE法の適用

Applicability of acoustic emission (AE) method to the diagnosis of leaks in an engine gasket was studied. Wide band type AE sensors were attached on the gasket around the cylinder, and nitrogen gas was gradually injected to the cylinder in order to occur leaks. It was demonstrated that continuous AE signals due to leaks are detected at the internal pressure exceeding a certain level. Since the average signal level (ASL) was increased according to the internal pressure, it was found that the amounts of leaks could be evaluated by the ASL. It was also found that the AE counts are preferable to detect the occurrence of leaks because of the feature that the leaks lead to significantly change of the counts. The result of wavelet transform of the leak AE signal indicates that both of the peak frequency and its intensity vary with time so that the time-frequency analysis is useful to evaluate the leak behavior.

Identify on Acoustic Emission Signals of Tank Bottom Corrosion Using Hidden Markov Model

Aiming at the acoustic emission signals of oil storage tank bottom injured, hidden Markov algorithm is proposed to identify the tank bottom corrosion signal. Typical corrosion acoustic emission signal is divided into transient acoustic signal, continuous acoustic emission signal and mixed acoustic emission.Baum-Welch algorithm is used to train these typical corrosion acoustic emission signals model, then establish HMM model library. The forward-backward algorithm is used to compute each acoustic emission model’s output probability. The simulation experiments shows that the hidden Markov algorithm can correctly identified the acoustic emission signals.

Continuous acoustic emission during intermittent plastic flow in α-brass

The Portevin–Le Chatelier effect which is usually associated with discontinuities in plastic flow has been studied in α-brass using an acoustic emission (AE) technique. It is found that intermittent plastic flow in the high-strain-rate regime corresponding to A-type serrations on the stress–strain diagram is accompanied by a continuous AE signal reflecting a smooth dynamics of emitting sources. No individual transient AE signals, which could be associated with rapid dislocation avalanches, have been resolved at different timescales.

AE signals as laser cutting quality indicators

The paper presents results of an analysis of acoustic emission (AE) signals obtained during laser cutting of a steel plate, which is frequently used in the production of automotive body components. The acoustic emission in the plate, which is produced by various physical phenomena occurring during the laser cutting process, was measured with contact PZT sensors. During the laser cutting process, continuous AE signals, which are produced by the action of a cutting gas, were captured. After laser cutting, AE bursts produced by various phenomena during material cooling were captured. The results obtained confirm that both the continuous signals occurring during the cutting process and the AE bursts after laser cutting provide a good insight into the quality of a laser cut performed. Results of a factorial analysis of an influence of laser cutting conditions on the measured continuous AE signals are described. Response surfaces of amplitude average and intensity average and their mutual correlation are established. An influence of cutting parameters on the size of a dross formed at the lower laser-cut edge, which is an important indicator of cut quality, is analysed and a relation with the measured AE signals is established.

Acoustic Emission in Elementary Processes of Friction and Wear: In-Situ Observation of Friction Surface and AE Signals

The relationship between acoustic emission (AE) signals and tribological phenomena in the elementary processes of friction and wear is examined. The elementary processes of friction and wear were observed by a frictional surface microscope, which installed a friction system into the view of an optical microscope. In this paper, the elementary processes are focused on the plastic deformation of the friction surface and the formation of wear particles between friction surfaces. In-situ experiments with the frictional surface microscope were performed by a pin-on-block type sliding test for iron and zinc. The friction surface and its side face were also observed by an atomic force microscope. The results show that two types of AE signals—a continuous AE signal of low amplitude and a burst-type AE signal of high amplitude—are detected in the elementary processes of friction and wear. The continuous AE signal of low amplitude is detected by the generation of slip lines and wear elements. The burst-type AE signal of high amplitude is detected by the formation of a transfer particle.

Acoustic Emission in Elementary Processes of Friction and Wear (In-Situ Observation of Friction Surface and AE Signals)

The relationship between acoustic emission (AE) signals and tribological phenomena in the elementary processes of friction and wear is examined. The elementary processes of friction and wear were observed by a frictional surface microscope which installed a friction system into the view of an optical microscope. In this paper, the elementary processes are focused on the plastic deformation of the friction surface and the formation of wear particles between friction surfaces. In-situ experiments with the frictional surface microscope were performed by a pin-on-block type sliding test for iron and zinc. The friction surface and its side face were also observed by an atomic force microscope. The results show that two kinds of AE signals, a continuous AE signal of low amplitude and a burst AE signal of high amplitude, are detected in the elementary processes of friction and wear. The continuous AE signal of low amplitude is detected by the generation of slip-lines and wear elements. The burst AE signal of high amplitude is detected by the formation of a transfer particle.

Acoustic Emission Technique to Study the Effect of Strain Rate on the Deformation Behaviour of TRIP Aided Steels with Different Matrix Microstructures

Acoustic Emission (AE) signals generated during the tensile deformation of three TRIP-aided steels with different matrix microstructures were monitored. The tensile tests were carried out at four different strain rates varying from 3.33×10−5 to 3.33×10−2 s−1 and additional intermittent tests were carried out at the lowest and highest strain rates to estimate the extent of deformation induced austenite to martensite transformation using X-ray diffraction analysis. Good correlation was obtained between AE and X-ray diffraction analyses. Both techniques showed that increasing strain rate restricted progressive transformation of retained austenite to martensite. The energy of continuous AE signals generated during tensile test conducted at high strain rate (3.33×10−2 s−1) was found to be directly proportional to the fraction of martensite formed. The proportionality constant (k) was influenced by the matrix microstructure; steels with polygonal ferrite matrix manifesting a higher k value than steels with annealed martensite matrix or bainitic ferrite matrix.

Acoustic emission monitoring of bainitic and martensitic transformation in medium carbon steel during continuous cooling

This paper concerns acoustic emission (AE) measurements during continuous cooling of steel C45 using a Gleeble 1500 thermomechanical simulator. After austenising at a certain temperature, the studied specimen was cooled down and the root mean square (RMS) value of the continuous AE signal was measured. During cooling two distinct peaks in the RMS data were observed at temperatures of 200-300°C and 500-600°C, which have been attributed to martensite and bainite formation respectively. The observed bainite peak strongly indicated that the mechanism of bainite growth is displacive. The AE monitoring of bainite and martensite formation was supported by dilatation measurements, which were performed simultaneously. The effect of the austenite grain size on the evolution of the bainitic and martensitic transformation was studied by varying the austenising temperature Ta. It was found that upon lowering Ta, i.e. with decreasing austenite grain size, the bainite peak increases while the martensite peak decreases.

A study of acoustic emission energy generated during bainite and martensite formation

Acoustic emission (AE) signals generated during bainite and martensite formation in steel C45 have been measured, and the AE energy has been correlated with the strain energy accompanying both displacive transformations. The gas tungsten arc welding process was used to vary the volume transformation rates of bainite and martensite formation. The root mean square (rms) voltage Urms of the continuous AE signals was measured during travelling arc welding and after spot welding. Depending on the cooling rate and the mean austenite grain size, martensite or bainite is formed in the weld. After spot welding with moderate arc currents, only martensite was formed during cooling, which was reflected by a peak in the Urms data: the martensite peak. An analysis of the results shows that the AE energy produced during the transformation (∫Ū2m dt) is proportional to the volume Vm of martensite in the spot weld, with proportionality factor km. During travelling arc welding, bainite and martensite formation occur simultaneously and both displacive transformations contribute to the measured AE power at each moment. The AE power due to bainite formation (Ū2b) was calculated using the obtained proportionality factor km and was found to be proportional to the volume rate of bainite formation dVb/dt with proportionality factor kb.

Wavelet analysis of acoustic emission signals in boring

Using acoustic emission (AE) signals to monitor tool wear states is one of the most effective methods used in metal cutting processes. As AE signals contain information on cutting processes, the problem of how to extract the features related to tool wear states from these signals needs to be solved. In this paper, a wavelet packet transform (WPT) method is used to decompose continuous AE signals during cutting; then the features related to tool wear states are extracted from decomposed AE signals. Experimental results verified the feasibility of using the WPT method to extract features related to tool wear states in boring.

Location of continuous AE sources by sensory neural networks

A brief classification of location problems which appear in acoustic emission (AE) analysis is given. Empirical treatment of corresponding inverse problems is explained and applied to location of sources which generate continuous AE signals. A continuous AE phenomenon is treated as a stochastic process which is represented by the source coordinates and the correlation function of the emitted sound. The empirical model of AE phenomenon is formed based on a set of samples. The model includes a network of AE sensors and a neural network (NN). During formation of the model, the AE signals are generated by sources at typical positions on a specimen. Recorded ultrasonic signals are transmitted to the NN together with the source coordinates. The first layer of NN determines the cross-correlation functions of signals and forms from them and source coordinates the data vectors. In the second layer, a set of prototype vectors is formed from the data vectors by a self-organized learning. After learning, the network is capable to locate the source based on detected sound. For this purpose, the sensors provide AE signals, while the NN determines the corresponding correlation function and associates to it the source coordinates. The association is performed by a non-parametric regression which is implemented in the third layer of NN.

Monitoring of turning process with acoustic emission signals.

Automatic monitoring of the cutting condition is a key technology to increase the reliability of modern machining process. In this study, practical methods which use acoustic emission (AE) signals are proposed to monitor the wear and breakage of the cutting tool and the condition of chip formation in turning process. The signal processing method developed in this study is efficient to measure the amplitude of continuous type AE signal which is closely related to the wear of cutting tool. The tool breakage is detected by monitoring the stepwise increase of AE signal. Formation of the tangled chip is successfully detected by paying attention to the frequency component of the AE signal.

Acoustic emission initiated by penetrating ions

An acoustic emission (AE) activity during implantation of H + ions in crystalline LiNbO 3 is studied using the method of instant spectrum analysis. The observations support the idea that the continuous AE signals in the sound — and infrasound — frequency range are caused by the local and random macro-strain and macro-deformations formed by penetrating ions. A generalised integrated characteristic-relative integrated intensity of AE is proposed to obtain information about the physical state of the implanted subsurface layer.