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Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures.

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The mechanical properties of fissured sandstone will deteriorate under water-rock interaction. It is crucial to extract the precursor information of fissured sandstone instability under water-rock interaction. The potential of each acoustic emission (AE) parameter as a precursor for instability in the failure process of fissured sandstone was investigated in this study. An experimental dataset comprising 586 acoustic emission experiments was established, and subsequent classification training and testing were conducted using three machine learning (ML) models: AdaBoost, MLP, and Random Forest (RF). The primary parameters for identifying the instability risk state of fissured sandstone include acoustic emission ringing count, energy (mV·ms), centroid frequency, peak frequency, Rise Angle (RA), Average Frequency (AF), b value, and the natural/saturated state of fissured sandstone: state. To enhance data utilization, a 10-fold cross-validation method was employed during the model training process. The machine learning models were developed and designed to identify the instability risk of fissured sandstone under the natural and saturated states. The results demonstrated that the established RF model was capable of identifying fissured sandstone instability risks with an accuracy of 97.87%. Feature importance analysis revealed that state and b value exerted the most significant influence on identification results. The Spearman correlation coefficient was utilized to assess the correlation between input features. This study can provide technical support to identify the risk of instability of fissured sandstones under both natural and saturated water conditions. Based on the models developed in this study, it is possible to implement an early warning method for instability in fissured sandstone that meets realistic working conditions. Compared with the traditional empirical and formulaic methods, the machine learning method can more quickly process huge amounts of AE data and accurately identify the damage state of fissured sandstone.

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This paper presents an acoustic emission (AE) detection method for refined oil storage tanks which is aimed towards specialized places such as oil storage tanks with high explosion-proof requirements, such as cave oil tanks and buried oil tanks. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. An independent channel AE detection system is designed, which can store the collected data in a piece of independent explosion-proof equipment, and can analyze and process the data in a safe area after the detection, solving the problems of a short signal acquisition distance and the weak safety protection applied to traditional AE instruments. A location analysis of the AE sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open-tank tests confirms that using AE technology effectively captures corrosion signals from oil storage tanks' bottoms. The feasibility of carrying out online inspection under the condition of oil storage in vertical steel oil tanks was verified through a comparison with open inspections, which provided a guide for determining the inspection target and opening order of large-scale oil tanks.

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I/II/III mixed mode fractures of intersecting joint fissures often occur in natural rock masses, and jointed rock masses are prone to rockbursts in deep underground engineering when subjected to long-term crustal stresses. However, most studies of the mechanical mechanisms of these intersected joints have been conducted by simplifying two-dimensional joint model tests. Furthermore, the fracture mechanisms of two-dimensional intersected joints under tension and compression are completely different from those of three-dimensional joints. This paper presents a novel prefabricated specimen with combinations of intersecting joints capable of detecting the failure behaviours of rock I/II/III mixed mode fractures under creep loading. Uniaxial compression and multistage creep tests are performed on prefabricated sandstone specimens with intersecting joints of 0°/0°, 0°/30°, 0°/60°, and 0°/90°. The experimental results show that with the increase in the number of prefabricated intersecting joints, the uniaxial compressive strength and elastic modulus values of the sandstone specimens gradually decrease. In addition, the sandstone specimens experience relatively few AE events and minor axial strain variations in the first creep stage and the second creep stage of the multistage creep test. The axial strain increases sharply due to the sharp increase in the number of AE events in the third creep stage. The 0°/60° sandstone specimen undergoes accelerated creep failure, resulting in mixed X-shaped tensile‒shear rupture. The RA value is high based on the quantification of the creeping cracks using the acoustic emission parameters of the rise angle (RA) and average frequency (AF). The AF values of the 0°/0°, 0°/30°, and 0°/90° sandstone specimens are high. The experimental results show that a larger joint intersection angle leads to greater mutual restraints and greater effects of prefabricated crack propagation in the rock specimens, thus increasing the final failure strength. Finally, based on the acoustic emission count, a characteristic variable D suitable for characterizing the creep damage evolution of a joint rock mass is established. The findings of this paper can facilitate an effective understanding of the creep effect of I/II/III mixed mode fracture and its micromechanism. The research results will have a certain reference value for the detection and risk mitigation of instantaneous and time-delayed rockbursts.

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In geotechnical engineering, a large number of pillars are often left in underground space to support the overlying strata and protect the surface environment. To enhance pillar stability and prevent instability, this study proposes an innovative technology for pillar reinforcement. Specifically, local confinement of the pillar is achieved through fiber-reinforced polymer (FRP) strips, resulting in the formation of a more stable composite structure. In order to validate the effectiveness of this structural approach, acoustic emission characteristics and surface strain field characteristics were monitored during failure processes, while mathematical models were employed to predict specimen instability. The test results revealed that increasing FRP strip confinement width led to heightened activity in acoustic emission events during failure processes, accompanied by a decrease in shear cracks but an increase in tensile cracks. Moreover, ductility was improved and deformation resistance capacity was enhanced within specimens. Notably, initial crack generation occurred within unconfined regions of specimens during failures; however, both length and width as well as overall numbers of cracks significantly decreased due to implementation of FRP strips. Consequently, specimen failure speed was slowed down accordingly. Finally, the instability of the partial FRP-confined cement mortar could be more accurately predicted based on the model of FRP-confined concrete. It was verified by the test results.

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To contribute to the development of sustainable composites, this work investigates the effects of moisture on the key AE characteristics related to the damage mechanisms of a bio-based balsa wood core sandwich in 4-point bending tests, including cumulative counts, amplitude, peak frequency, and duration. Novel triple dog-bone balsa wood core sandwich specimens with different MC (moisture content) were studied by comparing microscopic observations and a proposed two-step clustering approach in AE analysis. Three MC states, i.e., dry, 50% MC, and 120% MC, are discussed. GFRP (glass-fiber-reinforced polymer) laminate skin damages were found to be predominant in most GFRP-balsa sandwich specimens, but balsa wood core damages play a more important role as MC increases. The degradation of the bending stiffness of the sandwich was proven to be faster in the first linear stage of the moisture absorption curve, while the decrease in bending strength was more pronounced at the MC saturation level. Finally, for all of the dry and wet sandwich specimens, peak frequency and duration were proven to be more helpful in identifying damages associated with the lighter bio-based balsa wood core, such as balsa core damages and skin/core debonding.

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The clutch facing is a complex organic matrix composite in dry clutch systems. When the clutch engages, there is a sliding contact between the clutch facing and the mating surfaces, resulting in temperature increases of up to 300 °C. These thermal cycles activate several mechanisms that can have consequences on such material: cracking and, more generally, the ageing of the polymer. The thermomechanical properties of the material therefore evolve according to the number of thermal cycles. This study focused on investigating the local and average evolution of the coefficients of thermal expansion (CTE) of clutch facing as a function of thermal cycles. Several techniques were employed, including image stereocorrelation for determining the CTE, Dynamic Mechanical Analysis (DMA) tests for monitoring the ageing of the material and acoustic emission for highlighting the damage. The results showed that the average CTE decreased as a function of the temperature and the number of loading cycles, while locally, it increased in some areas and decreased in others. These differences appear to be the result of material heterogeneity (actual yarn tracing, etc.) and interaction between cracking and ageing mechanisms in the polymer matrix. Indeed, thermal cycling led to cracking and additional crosslinking, which is influenced by ageing conditions.

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Cemented gangue backfill technology is beneficial to the reuse of solid waste and sustainable economic development. However, mine water has a great impact on the strength and deformation of cemented gangue backfill (CGB). In this study, the CGB specimens under load were placed in simulated acid mine water (H2SO4 solution). The changes in deformation, resistivity, and ultrasonic pulse velocity (UPV) of CGB were monitored. On the 360th day, the stress-strain curve and acoustic emission (AE) energy of the specimen during loading were recorded. The degradation mechanism of CGB was discussed by scanning electron microscope (SEM) and X-ray diffraction (XRD). The results showed that the deformation of CGB increased with time. The effect of H2SO4 solution concentration on the deformation was different in the early and late stages. Applying an 80% stress-strength ratio (SSR) reduced the strength and increased the deformation. The UPV and resistivity had different characteristics at different corrosion ages, which could be used for long-term stability monitoring of CGB. The CGB showed the strongest AE energy characteristics near the peak stress. The AE energy decreased with the increase of pH value in the pore compaction stage, and the AE activity of the CGB under 80% SSR was much greater than that of the CGB under 40% SSR. The erosion of the H2SO4 solution on the CGB was inhibited by applying a small load. Excessive load aggravated the erosion deterioration of CGB due to initial plastic damage. The research results can provide a reference for the durability design of CGB.

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In order to investigate the characteristics of rock and rock-like materials during the fracture process, notched semi-circular bending (SCB) experiments of 3 rocks and 2 rock-like materials were conducted in this paper. The process of the crack mouth opening was measured with a clip gauge. Acoustic emission was used to analyze the damage and failure mode of the specimens. Meanwhile, the fracture process zone (FPZ) was analyzed with the digital image correlation (DIC). Finally, the differences in the fracture process between rocks and rock-like materials were observed with a polarized microscope, and the formation mechanism of FPZ was discussed. The results indicate that the sequence from brittleness to plasticity is gypsum, marble, granite, concrete and fine sandstone. The crack opening velocity of gypsum, marble, and granite reaches 0.02-0.025 mm/s, far exceeding that of sandstone and concrete at 0.003 mm/s and 0.005 mm/s. The stronger the brittleness of geomaterials, the less significant their acoustic emission effect. Only a few acoustic emissions occur during the fracture process of gypsum with 8 hits. Its fracture occurs instantaneously rather than through a process of damage to fracture and the failure mode is tensile failure. Sandstone has the strongest plasticity, with a large count of acoustic emissions before and after fracture, with a hit number of 5062, which is 630 times of pure gypsum. The fracture is a process of damage accumulation with 94% of sandstone, 89% of concrete, 80% of granite, and 60% of marble showing a tensile and shear failure mode except gypsum. In addition, the stronger the brittleness of geomaterials, the smaller their FPZ size. The FPZ of gypsum is only about 3 mm, which can be considered as lacking, while other materials are about 6-11 mm. The formation of FPZ depends on whether an interlocking structure can be formed inside the material, which is related to the base material and crystalline or aggregate particle size.

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The article presents in detail the construction of a low-cost, portable online PD monitoring system based on the acoustic emission (AE) technique. A highly sensitive piezoelectric transducer was used as the PD detector, whose frequency response characteristics were optimized to the frequency of AE waves generated by discharges in oil-paper insulation. The popular and inexpensive Teensy 3.2 development board featuring a 32-bit MK20DX256 microcontroller with the ARM Cortex-M4 core was used to count the AE pulses. The advantage of the system is its small dimensions and weight, easy and quick installation on the transformer tank, storage of measurement data on a memory card, battery power supply, and immediate readiness for operation without the need to configure. This system may contribute to promoting the idea of short-term (several days or weeks) PD monitoring, especially in developing countries where, with the dynamically growing demand for electricity, the need for inexpensive transformer diagnostics systems is also increasing. Another area of application is medium-power transformers (up to 100 MVA), where temporary PD monitoring using complex measurement systems requiring additional infrastructure (e.g., control cabinet, cable ducts for power supply, and data transmission) and qualified staff is economically unjustified.

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The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.

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Tool wear leads to dimensional inaccuracy and low surface quality in the workpiece, and unexpected sudden tool failure. Detection of tool wear is essential to enhance the quality of manufacturing components and extend tool life. The present work is aimed to investigate the various damage mechanisms involved in the cutting tool and workpiece during drilling of Al-5%B4C composite using acoustic emission technique (AET). The dry drilling experiments were carried out at different spindle speeds and feed rates with high strength steel (HSS) tool. AE time-domain parameters such as count, energy, amplitude and root mean square (RMS) voltage were extracted from the signals and correlated with cutting parameters and tool damage. Fast Fourier transform (FFT) was applied to visualize the frequency components in the AE signals during the drilling process. The wavelet packet transform (WPT) approach was performed to the AE signals to identify and discriminate the various damage mechanism involved in the drilling. The differentiated damage mechanism and their corresponding wavelet energy content were studied. The wavelet energy ratio for decomposed components at different speeds was discussed. The vision measuring microscope was employed to measure the tool wear. The AE features, i.e., AERMS and wavelet coefficient increases with increasing tool wear. A scanning electron microscope was also utilized to characterize the microstructural damage present in the cutting tool and workpiece.

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Acoustic Emission (AE) is revealed to be highly adapted to monitor materials and structures in materials research and for site monitoring. AE-features can be either analyzed by means of physical considerations (geophysics/seismology) or through their time/frequency waveform characteristics. However, the multitude of definitions related to the different parameters as well as the processing methods makes it necessary to develop a comparative analysis in the case of a heterogeneous material such as civil engineering concrete. This paper aimed to study the micro-cracking behavior of steel fiber-reinforced reinforced concrete T-beams subjected to mechanical tests. For this purpose, four-points bending tests, carried out at different displacement velocities, were performed in the presence of an acoustic emission sensors network. Besides, a comparison between the sensitivity to damage of three definitions corresponding to the b-value parameter was performed and completed by the evolution of the RA-value and average frequency (AF) as a function of loading time. This work also discussed the use of the support-vector machine (SVM) approach to define different damage zones in the load-displacement curve. This work shows the limits of this approach and proposes the use of an unsupervised learning approach to cluster AE data according to physical and time/frequency parameters. The paper ends with a conclusion on the advantages and limitations of the different methods and parameters used in connection with the micro/macro tensile and shear mechanisms involved in concrete cracking for the purpose of in situ monitoring of concrete structures.

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Any mechanical instability associated with total hip replacement (THR) excites elastic waves with different frequencies and propagates through the surrounding biological layers. Using the acoustic emission (AE) technique as a THR monitoring tool provides valuable information on structural degradations associated with these implants. However, several factors can compromise the reliability of the signals detected by AE sensors, such as attenuation of the detected signal due to the presence of biological layers in the human body between prosthesis (THR) and AE sensor. The main objective of this study is to develop a numerical model of THR that evaluates the impact of biological layer thicknesses on AE signal propagation. Adipose tissue thickness, which varies the most between patients, was modeled at two different thicknesses 40 mm and 70 mm, while the muscle and skin thicknesses were kept to a constant value. The proposed models were tested at different micromotions of 2 µm, 15-20 µm at modular junctions, and different frequencies of 10-60 kHz. Attenuation of signal is observed to be more with an increase in the selected boundary conditions along with an increase in distance the signals propagate through. Thereby, the numerical observations drawn on each interface helped to simulate the effect of tissue thicknesses and their impact on the attenuation of elastic wave propagation to the AE receiver sensor.

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The stability of deep rock engineering, especially during the excavation, is inextricably linked to the time-dependent mechanical properties of brittle rock. Therefore, the uniaxial creep test in a multilevel loading path is carried out, accompanying the real-time DIC (digital image correlation) and AE (acoustic emission) technologies. For the quartz sandstone, the lateral strain is more sensitive to increasing stress levels, and the lateral ductility is more significant during the creep process. The saturated quartz sandstone shows a certain bearing capacity before the volumetric dilation predominance. The softening effect of moisture causes a nearly invariable Poisson's ratio during the middle stress stages, as well as the more notable increasing trend of a steady creep rate with an increasing stress level, reflected by the larger slope and the intercept in the fitting relations. The main shear pattern and the combination of the shear and splitting failures are separately shown by the dry and saturated quartz sandstone. For the granite, both compression and extension exist in the creep deformation, and the failure may first occur in the prominent deformation area with a cracking noise. The AE hits present a similar time-dependent behavior to the strain of rock, and the attenuation trend happens in both the AE amplitude and energy before the rock enters the unsteady phase. The incomplete specimen of granite exhibits a lower strength and a larger deformation, owing to the more remarkable damage accumulation reflected by the spatial distribution of the AE event points.

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The acoustic emission (AE) method is a popular and well-developed method for passive structural health monitoring of metallic and composite structures. The current study focuses on the analysis of one of its processes, sound source or signal propagation. This paper discusses the principle of plate wave signal sensing using piezoelectric transducers, and derives an analytical expression for the response of piezoelectric transducers under the action of stress waves, to obtain an overall mathematical model of the acoustic emission signal from generation to reception. The acoustic emission caused by fatigue crack extension is simulated by a finite element method, and the actual acoustic emission signal is simulated by a pencil lead break experiment. The results predicted by the mathematical model are compared with the experimental results and the simulation results, respectively, and show good agreement. In addition, the presence of obvious S0 mode Lamb waves is observed in the simulation results and experimental results, which further verifies the correctness of the analytical model prediction.

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The knee is the biggest and complicated lower extremity joint that supports mobility and the entire weight of the human body and lies between the hip joint and ankle joint. Osteoarthritis (OA) is the most common joint disease in the knee among various musculoskeletal disorders globally, with an age-associated increase in incidence and prevalence. Health monitoring of the knee joints in daily life, and early OA diagnosis is challenging and draws attention to the various methods of diagnosis for this irreversible disease. In this review, electronic databases have been searched from inception for a detailed study about knee OA and its management. It focuses on various sensor technologies and different semi-invasive and non-invasive diagnosis methods with their limitations. In the last decade, various researchers have engrossed their attention to the potential of piezoelectric-based acoustic sensors to fabricate a wearable device for OA and its management. A sensor-based wearable device using vibroarthrography as a tool can be an appropriate solution for early-stage disease detection. We firmly believe that wearable technology for the detection of OA in daily life activities will play a significant role in managing this disease and help to reduce the chances of total knee replacements.

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The use of dental implants is growing rapidly for the last few decades and Ti-based dental implants are a commonly used prosthetic structure in dentistry. Recently, the combined effect of corrosion and wear, called tribocorrosion, is considered as a major driving process in the early failure of dental implants. However, no previous study has reported the prediction of tribocorrosion processes in advance. Therefore, this study is a novel investigation on how the acoustic emission (AE) technique can predict tribocorrosion processes in commercially-pure titanium (cpTi) and titanium-zirconium (TiZr) alloys. In this study, tribocorrosion tests were performed under potentiostatic conditions and AE detection system associated with it captures AE data. Current evolution and friction coefficient data obtained from the potentiostatic evaluations were compared with AE absolute energy showcased the same data interpretation of tribocorrosion characteristics. Other AE data such as duration, count, and amplitude, matched more closely with other potentiostatic corrosion evaluations and delivered more promising results in the detection of tribocorrosion. Hence, AE can be consider as a tool for predicting tribocorrosion in dental implants. Experimental results also reveal Ti5Zr as one of the most appropriate dental implant materials while exposing Ti10Zr's lower effectiveness to withstand in the simulated oral environment.

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Total hip replacements (THR) are becoming an common orthopedic surgucal procedure in the United States (332 K/year in 2017) to relieve pain and improve the mobility of those that are affected by osteoarthritis, ankylosing spondylitis, or injury. However, complications like tribocorrosion, or material degradation due to friction and corrosion, may result in THR failure. Unfortunately, few strategies to non-invasively diagnose early-stage complications are reported in literature, leading to implant complications being detected after irreversible damage. Therefore, the main objective of this study proposes the utilization of acoustic emission (AE) to continuously monitor implant materials, CoCrMo and Ti6Al4V, and identify degradations formed during cycles of sleeping, standing, and walking by correlating them to potential and friction coefficient behavior. AE activity detected from the study correlates with the friction coefficient and open-circuit potential observed during recreated in-vitro standing, walking, and sleeping cycles. It was found that the absolute energy level obtained from AE increased as the friction coefficient increased, potential decreased, and wear volume loss increased. Through the results, higher friction coefficient and AE activity were observed in Ti6Al4V alloys while there was also a significant drop in potential, indicating increased tribocorrosion activity. Therefore, AE can be utilized to predict material degradations as a non-invasive method based on the severity of abnormality of the absolute energy and hits emitted. The correlation between potential, friction coefficient, and AE activity was further confirmed through profilometry which showed more material degradation in Ti6Al4V than CoCrMo. Through these evaluations, it was demonstrated that AE could be utilized to identify the deformations and failure modes of implant materials caused by tribocorrosion.

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The technique of acoustic emission (AE) source localization is critical for studying material failure mechanism and predicting the position of potential hazards. Most existing positioning methods heavily depend on the premeasured wave velocity and are not suitable for complex engineering practices where the wave velocity changes dynamically. To reduce the influence of measurement error of wave velocity on location accuracy, this paper proposes a new algebraic solution for AE source localization without premeasuring wave velocity. In this method, the nonlinear TDOA equations are established and linearized by introducing two intermediate variables. Then, by minimizing the sum of squared residuals of the linear TDOA equations with respect to the AE source coordinate and two intermediate variables separately, the optimal algebraic solution of the AE source coordinate in the least squares sense is obtained. A pencil-lead breaks experiment is performed to validate the positioning effectiveness of the proposed method. The results show that the new method improves the positioning accuracy by more than 40% compared with two pre-existing methods, and the minimum positioning accuracy of the proposed method can reach 1.12 mm. Moreover, simulation tests are conducted to further verify the location performance of the proposed method under different TDOA errors and the number of sensors.