Acoustic Emission Source and Transmission Path Characterization Through Homomorphic Processing

Acoustic Emission (AE) has been adopted widely in structural health monitoring of buildings, walls, bridges, and has shown great potentials in incipient rotating machine fault detection [1, 2]. However, one inevitable problem that hinders AE of being practically applied in rotating machine fault detection is the transmission of AE signal inside the machine, as AE signal generally comprises high frequency components of typically 100kHz to 1MHz which will require transmission system to have extremely high data-rate for real-time monitoring. Traditional digital wireless system, for instance, Wi-Fi or Bluetooth, cannot satisfy the requirement. This paper addresses the issue of wireless AE transmission for rotating machine, with a modified analogue AE wireless transmission system. Piezoelectric wafer active sensor (PWAS), a lab-crafted miniature sensor was adopted to fulfill the need of small system.

Acoustic emission (AE) monitoring technique is a well-known approach in the field of NDE/SHM. AE monitoring from the defect formation and failure in the materials were well studied by the researchers. However, conventional AE monitoring techniques are predominantly based on statistical analysis. In this study we focus on understanding the AE waveforms from the fatigue crack growth using physics based approach. The growth of the fatigue crack causes the acoustic emission in the material that propagates in the structure. One of the main challenges of this approach is to develop the physics based understanding of the AE source itself. The acoustic emission happens not only from the crack growth but also from the interaction of the crack lips during fatigue loading of the materials. As the waveforms are generated from the AE event, they propagate and create local vibration modes along the crack faces. Fatigue experiments were performed to generate the fatigue cracks. Several test specimens were used in the fatigue experiments and corresponding AE waveforms were captured. The AE waveforms were analyzed and distinguished into different groups based on the similar nature on both time domain and frequency domain. The experimental results are explained based on the physical observation of the specimen.

Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in composite materials, because any AE signal contains useful information about the damage mechanisms. A major issue in the use of the AE technique is how to discriminate the AE signatures which are due to the different damage mechanisms. Conventional studies have focused on the analysis of different parameters of such signals, say the frequency. But in previous publications where the frequency is employed to differentiate between events, only one frequency is considered and this frequency was not enough to thoroughly describe the behavior of the composite material. So we introduced the second frequency. A Fast Fourier Transform (FFT) is then applied to the signals resulting from the two frequencies to discriminate different failure mechanisms. This was achieved by using self-organizing map and Fuzzy C-means to cluster the AE data. The result shows that the two approaches have been very successful.

Acoustic Emission (AE) is a widely used non-destructive evaluation technique mainly during periodic testing to insure structural integrity of process plant equipment. AE can also be used for continuous monitoring of damage (initiation and propagation) due to localized aqueous corrosion. For instance, AE is the only NDE technique that allows the detection and localization of propagating stress corrosion cracks (SCC). Moreover, SCC initiation is detected by AE, in the early stages compared to other techniques. The correlation between AE parameters and SCC damage is very useful for understanding and modelling the crack propagation process. For wear or abrasion corrosion, the AE signals measurements provide a valuable tool to determine the rate of mechanical abrasion related to the kinetic energy flow of particles. It gives complementary information to other techniques, such as continuous or transient electrochemical recording, more devoted to the monitoring of purely corrosive conditions. AE also appears to be a very promising tool for studying other localized corrosion modes such as pitting or crevice attack.

This paper demonstrates an inversion algorithm for characterizing the depth of acoustic emission (AE) sources in complex metallic plate-like structures. The characterization is motivated by gaining insight into early-stage fatigue crack propagation in such structures. The primary inputs to the algorithm are narrowband-filtered AE signals from a network of sensors and an estimate of the planar source location. The layout of the sensor network is specially designed to make use of locally isotropic regions in a complex structure. The algorithm is based on automatically extracting the depth-dependent amplitude ratio between the fundamental Lamb modes contained in the AE signals. A finite element model is designed to solve the forward problem of Lamb wave motion due to a given source, from which the relation between source depth and amplitude ratio is determined. The proposed algorithm was evaluated on a 1 m × 1 m section of a 2025-T6 aluminum civilian aircraft fuselage. Validation of the algorithm was achieved by generating controlled AE sources (Hsu-Nielsen pencil lead breaks) at various depths along the edges of the fuselage panel. Good agreement was found in the between the true and estimated source depths. The experiments demonstrate the ability to distinguish between sources originating near the mid-plane of a plate-like structure from those near the surface. Moreover, it is demonstrated that the fast computation of the inversion algorithm has great potential for real-time monitoring applications.

This paper presents a data-driven approach based on deep stacked autoencoders for the localization and characterization of acoustic emission sources in complex aerospace panels. The approach leverages the multimodal and dispersive reverberations of acoustic emissions. The approach is validated by Hsu-Nielsen pencil lead break tests on a fuselage section of a Boeing 777 instrumented with a single piezoelectric sensor.

Acoustic emission (AE) was used to monitor the tensile test of composites with adhesive specimens. The mechanical response behavior, damage and failure characteristics, and the corresponding AE characteristics of the composites have been investigated. The results show that the load of the join with defect in the adhesive layer is lower than the join with no defect. The higher AE relative energy and the AE amplitude were obtained in the adhesive specimen with defect in the adhesive layer whereas the variation of the AE relative energy is different from the adhesive specimen with on defect. The characteristics such as AE amplitude distribution, relative energy and cumulative hits are connected with the tensile damage and failure of the adhesively bonded single-lap joints of composite laminate. In the actual AE monitoring, these feature parameters should be considered entirely assess the damage and failure of the composites structures.

Acoustic emission (AE) from quartz during heating was measured with a high resolution decrepitometer on more than 350 samples from various conditions of formation and geological settings. The emitted acoustic signals can accumulate in very sharp peaks or extend over a wide temperature range. Different types and conditions of quartz formation can be distinguished from total counts and individual AE-patterns. Correlation of the determined AE peaks with microthermometric investigations, optical and scanning electron microscopical (SEM) studies of polished and etched thin sections showed that the sound, generated during heating, is caused by several distinguishable mechanisms. The main cause is the thermal expansion mismatch and thermal anisotropy of the quartz. In all cases the sound is emitted from opening and propagating microcracks. At temperatures below 380° C the main sources of AE are the rupture of grain boundaries, transgranular fracturing and decrepitation of large fluid inclusions. In the temperature range of 350–550° C, intragranular fracturing, sometimes enhanced by Brasil twinning, and reopening of healed fissures, often decorated with large numbers of small secondary fluid inclusions, cause distinct peaks of acoustic emission. At the α-β inversion temperature (≈ 573° C) massive emission of sound occurs if the quartz is twinned according to the Dauphine law. Measurement of AE can help to determine critical temperatures of material failure and reveal information on the residual strain in rocks. Different generations of quartz veins can be distinguished, even if covered by soil and the extent of alteration zones can be determined by the varying AE patterns.

Acoustic emission (AE) detection, as a non-electrical detection method, is very suitable for effective fault detection of power equipment with a strong electromagnetic field. However, the AE signal collected at industrial sites often contains a lot of interference noise, affecting the analysis and prediction of faults. In this study, a wavelet denoising method based on a new threshold function is proposed, to achieve a noise reduction in the low signal-to-noise ratio (SNR) signals. Simulation experiment results show that the proposed threshold function not only overcomes the shortcomings of the discontinuous hard threshold function, but also solves the constant deviation of the soft threshold function. What’s more, the proposed function achieves a good adaptability. When SNR = 10 dB: The SNR of the new threshold is 20.6622, and the RMSE is 0.0026. The SNR of the hard threshold is 20.2246 and the RMSE is 0.0027, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 2.16% and the root mean square error (RMSE) is reduced by 3.7%; the SNR of the soft threshold is 15.5656, and the RMSE is 0.0047, compared with the traditional soft threshold method, the new threshold has a 32.74% increase in SNR and a 40.43% reduction in RMSE. When SNR = -10 dB: The SNR of the new threshold is 4.2602, and the RMSE is 0.0172. The SNR of the hard threshold is 3.8558 and the RMSE is 0.0182, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 10.49% and the RMSE is reduced by 5.49%; the SNR of the soft threshold is 2.1625, and the RMSE is 0.0212, compared with the soft threshold method, SNR is improved by 97% and RMSE is reduced by 18.87%. Performance analyses have proved that the improved wavelet denoising method can obtain a good noise reduction effect. It is very helpful for AE signal analysis with the generally low SNR, which can improve the accuracy of failure identification in subsequent acts.

Acoustic emission (AE) technique is often employed by condition monitoring personnel to detect abnormalities in moving components of machines. Due to its low signal-to-noise ratio at high-frequency range, it often requires certain signal processing technique to extract valuable information from machine parts. This study intends to obtain the characteristic frequency segments of AE signals that correspond to the physical movement of valves in reciprocating compressor. It involves acquisition of AE signals at various simulated valve conditions and rotating speeds, decomposition of these signals through wavelet packet transform (WPT), and computation of crest factor (CF) of WPT coefficients at a specific crank angle. The characteristic frequency segments that indicate valve problems are often accompanied by an extra high or low CF value. By relating the CF value to valve events, the condition of valve can be predicted. It is hoped that this study can provide a methodology to obtain valve information effectively and efficiently while reducing unwanted and overloading information of AE signals.

Acoustic emission (AE) has previously been shown to be a useful technique in monitoring the state of wear of cutting tools. The piezo-electric transducers conventionally used for AE detection are contacting devices with a limited bandwidth. This paper describes the use of a robust fiber optic interferometer for the in-process measurement of AE during the face milling of annealed En24 steel to provide tool wear information via analysis of the rms AE signal. The interferometer displayed an improved diagnostic capability over a conventional piezoelectric sensor due to its advantages of being non-contacting, broadband with a flat frequency response to displacement, and providing absolute calibration.

Acoustic Emission (AE) monitoring is an effective tool for prioritising more conventional non-destructive inspections of above ground tanks, either process or storage vessels. Results obtained from AE monitoring may be assessed against an existing data bank permitting plant management to prioritise maintenance to tanks with high active corrosion rates or leaks. AE results may be used to extend inspection periods of tanks without leaks or active corrosion. This paper covers the theory of AE leak detection and corrosion monitoring including some background research into the difference between AE from corrosion and leaks. It looks at the equipment used and provides a summary of tank test results conducted by ATTAR and other overseas organisations as well as reviewing AE tank inspection economics.

This study focuses on localizing and characterizing acoustic emission (AE) sources in metallic panels with rivetconnected doublers. In particular, a deep learning-based framework is proposed that first performs zonal localization with only one sensor and then depending on the zone in which the source occurs, either finds the coordinates of the source or characterize it based on its source-to-rivet distance. The performance of the framework is assessed in typical scenarios in which the training and testing conditions of the deep networks are not identical, and Hsu-Nielsen sources were carried out for validation.

Acoustic emission (AE) is a widely used non-destructive technique for real time monitoring. Over the past decades it has been used as an effective technique for the detection, characterization and location of fatigue cracks. The basic principle is based on the elastic energy emitted from crack propagation incidents under stress. The emitted elastic wave propagates through the continuous media and it is detected by sensors attached on the surface of the material. Fatigue cracks are one of the important damage phenomena in metallic structures in aerospace applications. Fatigue life under cyclic loading consists of three stages: stage I (crack nucleation), stage II (crack propagation) and stage III (failure). In this study, the fatigue crack propagation of Ti6Al- 4V four-point bending test specimens is studied during stage II. Two test specimens are used for this study, one corner notched sample and one unnotched sample. In order to monitor the crack propagation, two AE piezoelectric sensors are placed on one side of the specimen and crack propagation gauges are placed on the damage location areas as verification. The rate and the location of the recorded events is in good correlation to the crack growth rate and the position of the crack showing the potential of using AE for fatigue monitoring of this kind of test. Other AE parameters are also studied with respect to their efficiency for characterizing crack propagation, though care should always be taken for reflections and dispersion which inevitably distort the acoustic signals. doi: 10.12783/SHM2015/186

Acoustic Emission (AE) has proven to be a useful tool in the monitoring of fiberglass pressure vessels. The ASME Piping and Pressure Vessel Code (subsection X) implements AE as an approved method for quality control and in service testing of fiberglass pressure vessels, making it an accepted technique for the evaluation of fiber composite structures. This article presents the result of AE monitoring of fiber reinforced plastic (FRP) pipes after impact damage has incurred. The results from these tests indicate that AE can be used as a tool to predict the level of damage incurred in the pipes after impact and during pressurization. The use of the Felicity Ratio as the benchmark for evaluation is presented as the key evaluating value.