Clustering reveals cavitation-related acoustic emission signals from dehydrating branches.

In order to explore the influence of wood’s anisotropic characteristics on Acoustic Emission (AE) signals’ propagation, the law of AE signals’ propagation velocity along different directions was studied. First, The center of the specimen’s surface was took as the AE source,then 24 directions were chose one by one every 15º around the center,and 2 AE sensors were arranged in each direction to collect the original AE signals. Second, the wavelet analysis was used to denoise the original AE signals, then the AE signals were reconstructedby Empirical Mode Decomposition (EMD). Finally, time difference location method was utilized to calculate AE signals’ propagation velocity. The results demonstrate that AE signals’ propagation velocity has obvious feature of quadratic function. In the range of 90º, as the angle of propagation direction increases, the propagation velocity of the AE signals presents a downward trend.

The observed acoustic emission (AE) signals of PZT transducers during glass capillary breaking were compared with the theoretical AE signals based on the AE system approach. The AE system composed of (1) an AE source: a glass capillary breaking, (2) a propagating medium: a glass plate, (3) a detector: a PZT transducer and (4) a signal processing unit: a digital oscilloscope was nearly constant in order to minimize the diculty of the AE system approach, but the thickness of a PZT transducer with a consistent diameter was adjusted to change the resonant frequency. The theoretical AE signals were obtained by using the Mason equivalent circuit and a point loading on a glass plate with a ramped functional dependence for a force strength of 10 N and a rise time of 280 ns. The observed AE signals compared with the theoretical AE signals had pulse-type AE signals and continuous-type AE signals with low frequencies. The pulse-type AE signals may originate from the thickness mode of the PZT transducer and the continuous-type AE signals may originate from the radial mode of the PZT transducer.

Strain localization peculiarities and distribution of acoustic emission sources in rock samples tested by uniaxial compression and exposed to electric pulses

The finite geometry of a laboratory specimen influences a measured acoustic emission waveform because of reflections, transmission, and mode conversion at the interface and boundaries of the specimen, thus making it difficult to determine the location of an acoustic emission (AE) source. The objective of this investigation is to develop a model experiment to identifiy the exact source location on the surface using ``synthetic'' AE signals. The AE event is generated by a short local thermal expansion. This expansion is produced by the absorption of a short laser pulse which provides a noncontact and broad-band generation of elastic waves. The signals are detected by a noncontact, broad-band, and high-fidelity sensor: a laser interferometer. The triangulation with several detectors is replaced by a single probe laser interferometer located at different coordinates under reproducible conditions. The recorded signals are analyzed by wavelet transform in order to determine the arrival times of waves for several frequency levels. These arrival times are used to quantify the location of the AE source in the surface as well as the velocity of the most dominant feature, the Rayleigh wave, and the time lag between the instant of the AE and the recording of the signal. The accuracy of the method is demonstrated by comparing the identified source location with the exact one.

Effect of layer orientation on acoustic emission characteristics of anisotropic shale in Brazilian tests

In order to reveal the propagation characteristics of acoustic emission (AE) signals in the body of industrial machinery, the characteristic frequencies and wave speeds of AE signals propagated on the interior and exterior surfaces (IS and ES) of the body were extracted. Subsequently, an algorithm utilizing characteristic frequency and time difference of arrival (TDOA) is proposed for the identification and localization of AE sources. Initially, an AE source is induced on the IS and ES of the body using the pencil-lead break method in accordance to ASTM standards, and the AE signal is captured by two piezoelectric sensors at a sampling frequency of 3 MHz. Then, to avoid the limitations of wavelet decomposition using self-selected wavelet scale and the problem that a single indicator cannot properly evaluate the correlation between independent mode functions (IMFs) with the original signal, this paper will conduct 4-layer wavelet decomposition of the original signal according to the response frequency range of the sensor, and select the wavelet details within the stable range of the response frequency of the sensor for preliminary reconstruction,and then the empirical mode decomposition (EMD) method is used to decompose the de-noised AE signal into 7 IMFs, and the AE waveform is reconstructed by the combined information of correlation coefficient and variance accounted for. Secondly, the reconstruction method combined with EMD analysis and a single index is compared with the proposed method in this paper to verify the reliability of the proposed method. In addition, the frequency domain characteristics of AE signal propagation process on the IS and ES of the body are extracted. Finally, based on the TDOA principle, the propagation speed of AE signal on the IS and ES of the body is calculated. Based on the geometric relationship between the AE source and two sensors, an algorithm for the location of the AE source is proposed. The results show that the proposed signal reconstruction method can effectively extract the features of AE signals, and the average positioning accuracy of the localization algorithm based on characteristic frequency and TDOA reaches 0.86%.

The cutting performance of grit is controlled by its shape and geometric parameters. However, the geometry changes with the progression of attrition wear, thereby increasing grinding temperature, grinding force and decreasing grinding efficiency. It is important to monitor the cutting condition of grit to confirm its state of wear. Acoustic emission (AE) signals have been widely used in monitoring cutting tool conditions and have been proven effective in detecting tool wear and failure. To identify the correlation between attrition wear and AE features, comparison tests were carried out with a pair of sharp and blunt grits scratching on hardened AISI4340 workpiece and the AE signals during the scratching process were obtained. The signals were analyzed in the time, frequency, and time-frequency domain The numbers of AE counts in the time domain differed for the sharp and blunt grits. It is also found that the proportions of AE energy in the frequency band of 0–90 kHz and 90–250 kHz differed was attributed to differences in strength of the various AE sources. The AE energy was further analyzed in time-frequency domain using discredit wavelet transform (DWT). The AE energy at certain decomposition level was extracted. A validation test was carried out with a worn grit. It was suggested that features associated with AE energy are appropriate for detecting attrition wear of grit, but the number of AE counts fluctuated owing to the complex friction behavior of the worn surfaces. The results of AE energy distribution in the time-frequency domain reveal that the amplitude of the AE energy deteriorates more rapidly when the grit is worn.

In recent years, acoustic emission (AE) testing technology is the one of the most important non-destructive testing (NDT) methods. The characteristics can be described by AE signals, including the location, nature and severity. In order to obtain the basic data for monitoring the wind turbine blade composite structure, the experiment adopted Φ0.5 mm lead pencil as artificial acoustic emission source and measured AE parameters, attenuation and source location of resin matrix for wind turbine blade. This paper introduced linear location and two-dimensional positioning technology of time arrival location method about the burst AE signal. The result shows that the location of AE source basically reflects the location of stimulation AE source, the location of AE source for resin matrix can agree well with the simulated location of AE source, the more close to the middle area, the more accurate location.

Cemented tailings backfill (CTB) is the most cost-effective and environmentally friendly method to recycle tailings for filling mining. It is of great significance to study the fracture mechanism of CTB for safe mining. In this study, three cylindrical CTB samples with a cement-tailings ratio of 1:4 and a mass fraction of 72% were prepared. An acoustic emission (AE) test under uniaxial compression (UC) with WAW-300 microcomputer electro-hydraulic servo universal testing machine and DS2 series full information AE signal analyzer was carried out to discuss the AE characteristics of CTB, such as hits, energy, peak frequency, and AF-RA. Combined with particle flow and moment tensor theory, a meso AE model of CTB was constructed to reveal the fracture mechanism of CTB. The results show that (1) the AE law of CTB under UC has periodic characteristics, which can be divided into the rising stage, stable stage, booming stage, and active stage. (2) The peak frequency of the AE signal is mainly focused on three frequency bands. The ultra-high frequency AE signal may be the precursor information for CTB failure. (3) The low frequency band AE signals represent shear crack, while the medium and high frequency band AE signals represent tension crack. The shear crack initially decreases and then increases, and the tension crack is the opposite. (4) The fracture types of the AE source are divided into tension crack, mixed crack, and shear crack. The tension crack is dominant, while a larger magnitude AE source is frequently shear crack. The results can provide a basis for the stability monitoring and fracture prediction of CTB.

The finite geometry of a laboratory specimen influences a measured acoustic emission waveform because of reflections, transmission, and mode conversion at the interface and boundaries of the specimen, thus making it difficult to determine the location of an acoustic emission (AE) source. The objective of this investigation is to develop a model experiment to identifiy the exact source location on the surface using “synthetic” AE signals. The AE event is generated by a short local thermal expansion. This expansion is produced by the absorption of a short laser pulse which provides a noncontact and broad-band generation of elastic waves. The signals are detected by a noncontact, broad-band, and high-fidelity sensor: a laser interferometer. The triangulation with several detectors is replaced by a single probe laser interferometer located at different coordinates under reproducible conditions. The recorded signals are analyzed by wavelet transform in order to determine the arrival times of waves for several frequency levels. These arrival times are used to quantify the location of the AE source in the surface as well as the velocity of the most dominant feature, the Rayleigh wave, and the time lag between the instant of the AE and the recording of the signal. The accuracy of the method is demonstrated by comparing the identified source location with the exact one.

Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel deep learning algorithm called deep residual network (DRN), a structural health monitoring (SHM) strategy is proposed for AE source localization through classifying and recognizing the AE signals generated in different sub-regions of critical areas in structures. Hammer hits and pencil-leak break (PLB) tests were carried out on a steel-concrete composite slab specimen to register time-domain AE signals under multiple structural damage conditions. The obtained time-domain AE signals were then converted into time-frequency images as inputs for the proposed DRN architecture using the continuous wavelet transform (CWT). The DRNs were trained, validated and tested by AE signals generated from different source types at various damage states of the slab specimen. The proposed DRN architecture shows an effective potential for AE source localization. The results show that the DRN models pre-trained by the AE signals obtained in the undamaged specimen are able to accurately classify and identify the locations of different types of AE sources with 3–4.5 cm intervals even when multiple cracks with widths up to 4–6 mm are present in the wave travel paths. Moreover, the influence factors on the model performance are investigated, including structural damage conditions, sensor-to-source distances and AE sensor mounting positions; in accordance with the parametric analyses, recommendations are proposed for the engineering application of the proposed SHM strategy.

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

PurposeThe purpose of this paper is to classify and identify the acoustic emission (AE) signals of 304 stainless steel during stress corrosion process.Design/methodology/approachThe corrosion behavior of a specimen during slow strain rate testing (SSRT) in acidic NaCl solution was studied. The AE signals during the corrosion process were classified based on K‐means cluster algorithms; meanwhile, the characteristics of different AE sources were analyzed.FindingsThe results indicated that the AE characteristics of different AE sources, such as pitting, cracking, and bubble break‐up, differ significantly. The 304 stainless steel was prone to the occurrence of stress corrosion cracking under the SSRT condition in acidic NaCl solution.Originality/valueThe characteristics of different AE sources during corrosion process were gained for the first time, which could be of much help in analyzing and judging the corrosion situation.

Experimental study on acoustic emission (AE) characteristics and crack classification during rock fracture in several basic lab tests

Structural health monitoring (SHM)/nondestructive evaluation (NDE) is an emerging multi-disciplinary field that aims at detecting/characterizing structural damage and providing diagnosis/prognosis of structural health status in a real-time or on-demand manner. It can reduce maintenance costs, shorten the machine service downtime, and improve the safety and reliability of the engineering structures. Acoustic emission (AE) is one of the SHM/NDE methods by means of detecting elastic waves due to dynamic motions at AE sources, such as cracking, delamination, cleavage, and fretting in a material. The acoustic emission inspection technique relies on the AE sensors to collect the AE signals from the structure to monitor the structural health. Conventionally, these AE sensors need to be permanently attached to the structure through the bonding adhesive layer which may introduce contamination to the structure. In this work, the research is focused on investigating non-contact passive sensing of acoustic emission (AE) signals using an air-coupled transducer (ACT). The well-acknowledged pencil-lead-break method has been used to simulate the AE source. A resonant type ACT is used to passively sense the AE signals, which leaves the testing object intact and provides a non-intrusive sensing method. The non-contact AE test on a thin aluminum structure as well as a thick steel structure is first conducted. Next, the investigation is extended to composite structures. Both single-layer composite structure and bonded composite structure are investigated. The results successfully demonstrate the capability of non-contact passive sensing of the AE signals using the ACT method.