Influence of Graphite Powder on the Mechanical and Acoustic Emission Characteristics of Concrete

In this paper, uniaxial compressive strength (UCS) test and three-point bending (TPB) test, together with an acoustic emission (AE) system, were performed to investigate the mechanical properties and AE characteristic changes of concrete with different graphite powder (GP) content. The results show that: (1) Poor adhesion and low interlocking of graphite with cement stone increase the initial defects of concrete, reducing its elastic modulus and the cyclo-hoop effect, and thus weakening the compressive strength. (2) For concrete with a low graphite content, the second sharp rise in ringing counts or energy released during the compressive process can be regarded as a failure alarm. However, as GP content increases, the second sharp rise fades away, while the first sharp rise becomes more visible. At high GP content, the first sharp rise is better for predicting failure. (3) The initial defects caused by GP significantly lower the initial fracture toughness, but its bridging effect greatly increases the critical crack mouth opening displacement and thus significantly enhances the unstable fracture toughness of concrete, by up to 9.9% at 9% GP content. (4) In contrast to compressive process, the sharp increase in AE signals preceding failure during the fracture process cannot be used to predict failure because it occurs too close to the ultimate load. However, as GP can significantly increase the AE signals and damage value in the stable period, such failure precursor information can provide a safety warning for damage development.

Estimation of Acoustic Source Positioning Error Determined by One-Dimensional Linear Location Technique

The one-dimensional (1D) linear location technique has been considered as one of the methods for determining the position of acoustic emission (AE) sources in metallic objects. However, this approach does not take into account the heterogeneity of materials and that leads to poor accuracy of AE sources localization. To estimate the positioning error of the linear location technique which is typically used to determine the AE source location a new approach based on the combination of experimental and simulation methods is proposed. This approach for error estimation contains a finite element model construction of the AE signals localization. The model consists of transmitting and receiving transducers mounted on the test object, the frequency response of which selected close to the characteristics of acoustic emission transducers applied in the preliminary experiments. The application of the approach in current research showed that a reduced positioning error on a flat steel plate reaches 15%. The proposed technique can be used to optimize the number of preliminary tests required to calculate the reduced error of the 1D linear location technique applied for the AE sources localization during the inspection of the structure.

Identification of Fatigue Damage Stages in Polymer Composite Materials by using Acoustic Emission: Approach and Perspectives

The work is devoted to the study of the mechanisms of damage accumulation in a polymer composite material (PCM) during fatigue loading. Mechanical testing of a fiberglass sample was carried out by cyclic tension accompanied by registration of acoustic emission (AE). For the recorded AE signals, the Fourier spectra were calculated and used for clustering with Kohonen self-organizing map. Relations between clusters and types of damage in the PCM structure were established. The analysis of the peak frequencies of the Daubechies D14-wavelet components of AE signals was carried out. Obtained results has allows one to describe the processes of destruction in the PCM sample. It has been established that, on the base of local formation of microdamages in the matrix and the fracture of the fibers detected during recording of the AE data, it is possible to predict the destruction of the polymer composite material, while the beginning of a material destruction can be registered if the damage identified as an adhesion failure is observed. Perspectives of application of adaptive fiber-optic AE sensors for structural monitoring of PCMs on the base of preliminary experimental results are considered and discussed.

Propagation Characteristics of Acoustic Emission Signals in Stiffened Cylindrical Shells Based on the Multipath Propagation Model

A stiffener attached to a cylindrical shell strongly interferes with the propagation of the acoustic emission (AE) signal from the fault source and reduces the fault detection accuracy. The interaction of AE signals with the stiffener on the cylindrical shell is thoroughly investigated in this paper. Based on the proposed model of the AE signal propagating inside the cylindrical shell with a stiffener, the installation constraints for the sensor are derived, resulting in the separation of the direct signal, the stiffener scattering signal, and other signals in the time domain. On this basis, combinations of the excitation frequency and the stiffener height are simulated, and the reflection and transmission of the AE signal in each case are quantitatively characterized by the scattering coefficients. The results indicate that there is a “T-shaped” transformation of the signal at the stiffener, which evolves into a variety of other modes. Moreover, the reflection and transmission coefficients of the incident AE signal are displayed as a function of the excitation frequency and the height of the stiffener. In addition, the accuracy of the scattering coefficients obtained from the numerical simulations is verified by experiments, and a good consistency between simulation results and experiment results is presented. This work illustrates the propagation characteristics of AE signals in a cylindrical shell with a stiffener, which can be used as guidance for optimizing the spatial arrangement of sensors in AE monitoring.

A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov–Smirnov Test

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these features ineffective. The present paper proposes a pipeline leak detection technique based on acoustic emission event (AEE) features and a Kolmogorov–Smirnov (KS) test. The AEE features, namely, peak amplitude, energy, rise-time, decay time, and counts, are inherent properties of AE signals and therefore more suitable for recognizing leak attributes. Surprisingly, the AEE features have received negligible attention. According to the proposed technique, the AEE features are first extracted from the AE signals. For this purpose, a sliding window was used with an adaptive threshold so that the properties of both burst- and continuous-type emissions can be retained. The AEE features form distribution that change its shape when the pipeline condition changes from normal to leakage. The AEE feature distributions for leak and healthy conditions were discriminated using the two-sample KS test, and a pipeline leak indicator (PLI) was obtained. The experimental results demonstrate that the developed PLI accurately distinguishes the leak and no-leak conditions without any prior leak information and it performs better than the traditional features such as mean, variance, RMS, and kurtosis.

Identification of Fracturing Behavior In Thermally Cracked Granite Using The Frequency Characteristics of Acoustic Emission

The frequency characteristics of acoustic emission (AE) during triaxial compression of thermally cracked and unheated (“fresh”) granite samples were investigated with the aim of understanding the influence of pre-existing cracks on precursor information regarding macroscopic failure. The peak frequency during the damage process was the same for thermally cracked and fresh granites. Analysis of AE signals showed that signals with low peak frequency appeared before failure of the sample, implying the initiation of microfractures with progressive growth of cracks. The peak amplitude of the frequency spectrum recorded in the thermally cracked samples was much lower than that in the fresh samples. This result suggests two reasons for the difference in peak amplitude: reduction in shear modulus and the attenuation filtering phenomenon caused by thermal cracks. In particular, the maximum value of peak amplitude in the low-frequency band for the thermally cracked samples was smaller than that for fresh samples. This characteristic can be related to the stress drop and crack size. Assuming that pre-existing thermal cracks grow during the pre-failure stage, the events with low peak frequency and low peak amplitude in the heat-treated samples are interpreted as exhibiting a low stress drop because of the small rupturing area for individual events. Therefore, although AE signals with low frequency can be considered as precursors to rock failure, cracking behavior suggested by events with low frequency depends on the initial damage condition of the rock sample.

Towards a Diagnostic Tool for Diagnosing Joint Pathologies: Supervised Learning of Acoustic Emission Signals

Acoustic emission (AE) testing detects the onset and progression of mechanical flaws. AE as a diagnostic tool is gaining traction for providing a tribological assessment of human joints and orthopaedic implants. There is potential for using AE as a tool for diagnosing joint pathologies such as osteoarthritis and implant failure, but the signal analysis must differentiate between wear mechanisms—a challenging problem! In this study, we use supervised learning to classify AE signals from adhesive and abrasive wear under controlled joint conditions. Uncorrelated AE features were derived using principal component analysis and classified using three methods, logistic regression, k-nearest neighbours (KNN), and back propagation (BP) neural network. The BP network performed best, with a classification accuracy of 98%, representing an exciting development for the clustering and supervised classification of AE signals as a bio-tribological diagnostic tool.

Quality control of adjustment of clearances of ball bearing supports, dynamic devices rotor-type, according to parameters of acoustic emission signals

The article is devoted to increasing the bearing life of dynamic rotary-type machines by controlling the uniformity of the distribution of the value of the preliminary axial load acting on the rolling bearings of the rotation axis of the power unit. The possibility of monitoring the axial load using acoustic emission (AE) signals is considered. The results of experimental studies of the kinematics of the ball movement relative to other bearing parts, depending on the tightening torque of bolted joints, estimated by the parameters of AE signals, are presented.

Regularities and features of acoustic emission under plasma electrolytic oxidation of wrought Al-Mg alloy

The paper highlights the characteristic features of acoustic emission (AE) in the process of plasma electrolytic oxidation (PEO) of aluminum alloy, and also reveals the correlation of 4 main stages of PEO process with AE signals. The fundamental possibility to establish and detail the features of oxidation stages and to compare different PEO modes by AE signals was demonstrated. The results obtained substantiate the high potential of AE method as an instrument of in situ research, production monitoring and control, and evaluation of quality of PEO process on aluminum alloys.

Acoustic emission control of tensile and compression defects for plastic carbon fiber samples reinforced with side girders

Tensile and compression tests of composite samples made of carbon fiber reinforced with side girders have been carried out. It was found that the area of active location of acoustic emission (AE) signals corresponds to the destruction of the composite, bolted and glued joints. Destruction of the carbon fiber-reinforced plastic material did not always occur in the area of the fasteners, but the sources of AE signals with the maximum total amplitude were registered in the destruction zones.