AE Signals Research Articles

A pharmacovigilance analysis of post-marketing safety of tezepelumab

BackgroundTezepelumab has shown promising efficacy for adult patients with severe asthma since its approval. However, the post-marketing safety evaluation of tezepelumab is currently lacking. ObjectiveThe present study aims to investigate the post-marketing safety of tezepelumab based on the FDA Adverse Event Reporting System (FAERS) database. MethodsAdverse events (AEs) reports from January 2022 to December 2023 were extracted from the FAERS database. Disproportionality analysis by reporting odds ratio (ROR) and empirical Bayesian geometric mean (EBGM) was performed to detect potential AEs related to tezepelumab. Clinical characteristics and time to onset of AEs were also assessed. ResultsA total of 1,699 tezepelumab-related AE reports were identified during the study period. Thirty tezepelumab-related AE signals were detected by simultaneously applying the two algorithms. At the system organ class (SOC) level, the most common SOC related to tezepelumab was respiratory, thoracic and mediastinal disorders. At the preferred term (PT) level, common AEs including arthralgia and back pain were detected which were also documented in the label of tezepelumab and clinical trials. New unexpected AEs such as chest pain and myalgia were also identified. The median time to onset of tezepelumab-related AEs was 7.5 days, and the majority of AEs occurred within the first 1 month after tezepelumab initiation. ConclusionThe present study presents a comprehensive evaluation of the post-marketing safety of tezepelumab in the real-world setting. Our findings will provide valuable evidence for future clinical studies and management of safety issues of tezepelumab.

Study on the influence of cement slurry viscosity on acoustic emission response and deformation characteristics of slurry-coal cemented body

In the process of grouting sealing, the cementation failure of slurry and coal interface exists in the gas extraction borehole, which leads to unstable and gas leakage in the sealing section and affects the efficient gas extraction in the borehole. In order to study the influence of slurry viscosity on the failure characteristics of slurry-coal cemented body, this paper explores the law of influence of slurry viscosity on the acoustic emission response characteristics and deformation characteristics of slurry-coal cemented body under uniaxial compression through laboratory tests, data analysis and quantitative research of image processing. The results show that: (1) Changing the slurry viscosity can achieve the strength control of the cemented body, which can be increased by up to 42.65 %. (2) In the process of uniaxial compression, the cementation structure of slurry-coal cemented body breaks down successively, and its proportion and strength lead to the distribution difference of AE signals and the fluctuation of b-value. Combined with the phase response characteristics of the two, the change of failure mode can be analyzed to provide a reliable evaluation of crack propagation in the cemented body. (3) The b-value of the sample of slurry-coal cemented body with high-viscosity slurry does not fluctuate significantly, and is relatively stable at a low level near 1.0. The results are helpful to promote the research and development of sealing materials and sealing technology.

Safety study on adverse events of zanubrutinib based on WHO-VigiAccess and FAERS databases.

The study endeavors to elucidate AE signals associated with zanubrutinib data from the WHO-VigiAccess and FAERS databases. The aim is to furnish robust scientific evidence to inform clinical practice and regulatory decisions. We meticulously extracted all AE breports tied to zanubrutinib from the both databases, encompassing the period from the drug's market introduction until 30 April 2024. Retrospective quantitative analysis employing ROR, PRR, and BCPNN methodologies were utilized to analysis. The investigation unveiled 1,304 reports from WHO-VigiAccess and 1,141 reports from FAERS related to zanubrutinib. Among the top 30 reported PTs in both databases, those not recorded in the drug label included pyrexia, dizziness, and death. In the FAERS database, signals for zanubrutinib were detected across 19 SOCs. Systems not covered in the drug label included reproductive system and breast disorders, metabolism and nutrition disorders, ear and labyrinth disorders, among others. Unmentioned signals included intestinal perforation, onychoclasis, night sweats, etc. This study confirms common AEs of zanubrutinib and identifies new ones, highlighting the need for careful monitoring and personalized treatment. It also emphasizes the importance of ongoing drug safety surveillance and patient management to maximize therapeutic benefits and minimize risks.

Exploring SGLT-2 inhibitors and sarcopenia in FAERS: a post-marketing surveillance study

ABSTRACT Background The sodium-dependent glucose transporters 2 inhibitors (SGLT-2i) is associated with body weight loss but the composition of the losing weight remains unclear. Research design and methods Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multi- item gamma Poisson shrinker (MGPS) algorithms, were employed to quantify the signals of SGLT-2i-associated musculoskeletal and connective tissue disorders AEs. Results The search retrieved a total of 3,206 cases of musculoskeletal and connective tissue disorder-related AEs during the reporting period. This included 1,061 cases for Canagliflozin, 1,052 cases for Dapagliflozin, 1,074 cases for Empagliflozin, and 19 cases for Ertugliflozin. Fifteen preferred terms (PTs) with significant disproportionality were retained. No musculoskeletal and connective tissue system-related AE signals were reported for Ertugliflozin. We identified a risk of muscle necrosis with Canagliflozin use, a risk of sarcopenia with Dapagliflozin use, and a chance of muscle atrophy with Dapagliflozin and Empagliflozin prescriptions. Most cases occurred within the first month after SGLT-2i initiation, and AEs can persist beyond 360 days of use. Conclusions Our study identified potential new musculoskeletal and connective tissue disorder-related AE signals associated with SGLT-2 inhibitors.

A real-world pharmacovigilance study using disproportionality analysis of United States Food and Drug Administration Adverse Event Reporting System events for vinca alkaloids: comparing vinorelbine and Vincristine

ABSTRACT Background Vinca alkaloids are widely used in cancer treatment for their ability to target microtubule dynamics. While their efficacy in treating certain cancers is well-established, the full spectrum of their adverse event profiles remains an area of ongoing research. Methods We analyzed AEs related to vinorelbine and vincristine using a retrospective case/non-case approach with data from the FDA Adverse Event Reporting System (FAERS). We applied various algorithms to detect AE signals: the reporting odds ratio (ROR) and proportional reporting ratio (PRR) measured disproportionality and association strength; the Bayesian confidence propagation neural network (BCPNN) calculated the Information Component (IC) for associations against background rates; and the multi-item gamma Poisson shrinker (MGPS) yielded empirical Bayes geometric mean (EBGM) values, accounting for reporting variability. Results Both medications significantly involve the blood and lymphatic systems, with vinorelbine reporting 401 cases in this System Organ Class (SOC), exhibiting a ROR of 17.4, PRR of 12.4, IC of 3.63, and EBGM of 12.38. An intersection analysis of Preferred Terms (PTs) has uncovered previously unreported AEs shared by both drugs, including posterior reversible encephalopathy syndrome and inappropriate antidiuretic hormone secretion. Conclusions This analysis highlights the need for ongoing research of the risks associated with vinorelbine and vincristine.

Influence of loading conditions on acquired AE signals in biocomposites

Composite materials are increasingly used in lightweight transportation systems and civil engineering due to increasing weight constraints and installation costs. In the case of transportation systems such as in the aerospace and automobile industries, the use of composite materials reduces the weight, which is reflected in increasing the transportable load and reducing fuel consumption, and therefore the mechanical performance of the material is a very important and desired attribute. Biocomposites reinforced with natural fibers, such as flax, are gaining attention in engineering applications due to their sustainable and mechanical properties. This paper's research was designed to investigate the AE signals at different loading conditions of V-notched biocomposite specimens. Applying different loading cases, ranging from pure tension to pure shear, provided an understanding of the material's response under diverse stress states. The multi-axial strength and failure characteristics of biocomposites are evaluated. Digital image correlation technique provided measurement of strain fields complemented with AE data. AE signals have proven effective in capturing the onset and progression of different failure sources in composite material.

Experimental and numerical investigation of the sensor effect on the acoustic emission during Pencil-lead Break tests on PMMA plates

Pencil leads are broken on PMMA plates at various source-sensor distances to evaluate the influence of the propagation distance and the sensor type on AE signals. The resulting waves are detected using a broadband or a resonant sensor. The ability of five transducers, PKBBI sensor, MICRO80 sensor, MICRO200HF sensor, Nano30 sensor, and WD sensor, to recreate the characteristic forms of plate waves is evaluated. Because of the response spanning between 300 to 400 kHz frequency range, Nano30 and MICRO80 sensors have a bigger magnitude in the S0 mode than in the A0 mode, especially for small sensor-source distances. Their different responses demonstrate why similar test specimens and test settings can provide diverse findings due to the sensor effect, which is significant for the use of AE data in the classification of AE sources. Then, based on the normalization method, we suggest a procedure to acquire equivalent values for the selected descriptors using Laplacian Score and Principle Component Analysis. In order to gain a thorough understanding of the sensor effect through 3D finite element simulation, a comparative analysis is conducted between the perfect contact sensor and the resonant sensor with sensor effect. The sensor effect arises from the convolution of the Fourier Transform of the signal with the sensitivity curve in the frequency domain. By comparing the waveforms in the time and frequency domain, it is useful to determine how the different sensors differ from the AE signals.

Acoustic emission monitoring of a laser powder bed fusion process

Additive manufacturing (AM) metal parts offers opportunities for various industrial applications. From individual production of spare parts for unique mechanical components to prototyping of complex structures, the possibilities of production using the additive manufacturing process are manifold. One common AM technique is the Laser Powder Bed Fusion (PBF-LB/M) process, where a laser is used to selectively melt metal powder and create the parts layer wise as designed in a model. During manufacturing certain defects like pores, cracks and lack of fusion may be created in the built parts. As AM parts often have complex geometries, a postprocess non-destructive testing is difficult or even not possible. Thus, different optical monitoring techniques are applied to detect flaws during the build process with the aim to detect and repair defects right away during the manufacturing process. However, optical monitoring system require a clear view of the melt pool and quite expensive equipment. Acoustic monitoring by AE sensors attached to the built plate would be a cheaper alternative which also can be used without visual contact to the building chamber. This paper shows a first approach of an AE based process monitoring. The build plate of a PBF-LB/M machine therefore was adapted to hold four AE sensors that are then used to monitor the process. The build process itself creates AE signals caused by the melting and cooling and the mechanical application of powder. The formation of pores and cracks is expected to create additional acoustic emissions that will be distinct from the AE pattern from the printing process. This AE signals can be linked to the different layers of the printed part or in the long run to the laser position. Results from the first tests in a customized LPBF machine will be shown as well as the implementation of AE into the PBF-LB/M machine.

Comparative safety analysis of lacosamide and perampanel in epilepsy management: insights from FAERS database.

Epilepsy is a chronic neurological condition requiring effective management with minimal adverse effects. Lacosamide (LCM) and Perampanel (PER), two promising treatments, have distinct profiles that merit comparative analysis to guide clinical decision-making. This study utilizes a pharmacovigilance analysis of adverse events reported in the FDA Adverse Event Reporting System database from Q1 2009 to Q3 2023. Employing disproportionality and Bayesian analyses, we assessed and compared the AE signals associated with LCM and PER to elucidate their safety profiles in epilepsy treatment. The analysis included 12,576 AE reports for LCM and 2,703 for PER, highlighting a higher incidence of psychiatric disorders, including aggression with LCM, and a notable association of PER with psychiatric disorders such as psychotic disorders and dizziness. LCM showed a relatively safe profile during pregnancy, whereas PER's data suggested caution due to reported cases of suicidal ideation and attempts. This comprehensive evaluation underscores the importance of understanding the distinct AE profiles of LCM and PER in clinical practice, providing valuable insights for personalized epilepsy management. Future research with rigorous prospective designs is recommended to validate these findings and explore the mechanisms underlying the reported adverse events.

Fracture propagation characteristics of layered shale oil reservoirs with dense laminas under cyclic pressure shock fracturing

Forming a fracture network through fracturing stimulation is significant to efficiently developing shale oil resources. However, the complex lithological characteristics and dense laminas of continental shale oil strongly shield fracture propagation. The concept of "cyclic fluid injection induces rock fatigue" was introduced into shale oil fracturing technology, and the cyclic pressure shock fracturing method was designed. The horizontal well fracturing simulation experiments used prepared shale rock samples from the Permian Lucaogou Formation shale oil reservoir outcrop in Jimusar Sag, Junggar Basin. Two pressurization states were obtained through constant injection and rapid release of accumulated high pressure, corresponding to conventional and pressure shock conditions. The characteristics of fracture propagation under different fracturing methods were analyzed by combining acoustic emission monitoring and injection pressure curve response. Research has found that the dense laminas with a certain original width near the wellbore significantly inhibit the vertical propagation of hydraulic fractures (HFs), and conventional constant-rate fracturing methods make it difficult to stimulate the reservoir effectively. Fatigue fracturing can increase the complexity of near-wellbore fractures, but the HFs still tend to be arrested by the laminas. The bottom hole pressure (BHP) is artificially increased to a value far exceeding the rock breakdown pressure near the wellbore by applying the cyclic pressure shock fracturing method. It can avoid the communication between micro-cracks and horizontal laminas during the BHP constant rate increase process and overcome the inhibition of weak layers on vertical propagation. Besides, the fracture height and number of activated laminas positively correlate with the number of cycles. When the shock pressure is about 30 MPa, the fracture height of the three cycles increases by 50% compared to a single shock. In addition, the shock pressure has a more significant effect on the fracture height, and the fracture height increases significantly with the increase of shock pressure. The shock pressure increased by about 57%, and the fracture height increased by 60%. High-pressure shock has a certain effect on the naturally weak surface of the far well, which may cause the slip of the weak surface to produce AE signals. This provides a new approach for improving fracture complexity and control volume in layered shale oil reservoirs with dense laminas.

Damage identification of plain-woven composites at T > Tg using AE: Damage clustering and initiation detection

In this work, the damage modes and their initiation detections of plain-woven composites under tensile and compressive loads at T > Tg are investigated by conducting a comprehensive analysis of AE signals, including the feature selection, algorithm comparison and clustering validity. According to a newly proposed Pearson correlation coefficient cumulative criterion, the PF and PA are the most representative features of AE signals. A systematic comparison of K-Means, GMM and Hierarchical model, reveals that the Hierarchical model is the most suitable clustering algorithm for plain-woven composites at T > Tg. The cluster validity indexes combined with microscopic observations identify matrix cracking, interface debonding and fiber breakage as the three dominant damage modes, with PF ranges of [0–200 kHz], [200–350 kHz] and [350–600 kHz], respectively, and independent of different loading conditions. Three damage initiation criteria, sentry function, b-value and cumulative AE counts, are compared and the sentry function is determined to be optimal. The damage initiations of matrix cracking, interface debonding and fiber breakage within plain-woven composites at T > Tg can be effectively detected by AE.

Post-marketing drug safety surveillance of enfortumab vedotin: an observational pharmacovigilance study based on a real-world database.

Enfortumab vedotin (EV) is an antibody-drug conjugate (ADC) that has been approved by the FDA for patients with locally advanced or metastatic urothelial carcinoma (UC). This study presents a comprehensive pharmacovigilance analysis of the post-marketing safety profile of EV in the real-world based on the US Food and Drug Administration Adverse Event Reporting System (FAERS). Adverse event (AE) reports regarding EV between January 2020 and December 2023 were obtained from the FAERS database. The standardized MedDRA query (SMQ) narrow search AEs on the preferred term (PT) level were used. Disproportionality analysis was performed to identify the AE signals for EV with the reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN). A total of 2,216 reports regarding EV were included in the present study. SMQ analysis results indicated that a stronger strength signal was found in severe cutaneous adverse reactions, retroperitoneal fibrosis, and peripheral neuropathy. A total of 116 significant disproportionality PTs referring to 14 system organ classes (SOCs) were retained by disproportionality analysis, with 49 PTs not listed on the EV drug label. Frequently reported EV-related AEs included rash, peripheral neuropathy, decreased appetite, alopecia, and pruritus. The time to onset of the majority of EV-related AEs was within 30 days (66.05%), with only 0.73% events occurring after 1 year. The disproportionality analysis highlights that dermatologic toxicity and peripheral neuropathy were the major AEs induced by EV. The potential AEs not listed on the drug label were mainly related to gastrointestinal, hepatic, and pulmonary events. Further research is needed to confirm and explore the EV-related AEs in clinical practice.

Defect monitoring method for Al-CFRTP UFSW based on BWO–VMD–HHT and ResNet

Underwater friction stir welding (UFSW) achieves reliable joining between dissimilar materials and meets the welding demand for function and properties in lightweight structures of modern engineering. A defect monitoring method based on Variational Mode Decomposition optimized by Beluga Whale Optimization and Hilbert–Huang Transform (BWO–VMD–HHT) is proposed to solve the unclear feature of AE signal in UFSW due to the aqueous medium. UFSW experiments on Al alloy and carbon fiber reinforced thermoplastic (CFRTP) are carried out with AE signals measured. The time–frequency domain features of AE signals are extracted by BWO–VMD–HHT. The experimental results show that the main frequency of the AE signal is 22.5 kHz, and surface crack defects, shallow hole defects, and deep hole defects are accompanied by the transfer phenomena of different frequency components. Then, the feature vectors are built by frequency components in the BWO–VMD–HHT spectrum and reduced by principal component analysis, including 22.5 kHz, 24 kHz, 20.6 kHz, 18.4 kHz, 17.3 kHz, and 15.6 kHz. The feature vectors are divided into the train and test sets, and the welding defect prediction model (ResNet18-attention) is built by ResNet18 and trained by feature vectors. In the test set, the ResNet18-attention is compared with the BP, SVM, and RBF. Test results show that the precision of models has improved by at least 10%, which are trained by BWO–VMD–HHT features vector. Also, ResNet18-attention has achieved an average precision of 0.906 and recognizes the category of weld defect accurately, and this method can be applied to the defect monitoring of UFSW.

In-situ analysis of the effect of residual fcc phase and special grain boundaries on the deformation dynamics in pure cobalt

Polycrystalline hcp metals – an important class of engineering materials – typically exhibit complex plasticity because of a limited number of slip systems. Among these metals, deformation is even more complicated in cobalt as it commonly contains residual fcc phase due to the incomplete martensitic fcc→hcp transformation upon cooling. In this work, we employ a combination of in-situ (acoustic emission, AE) and ex-situ (scanning electron microscopy, SEM) techniques in order to examine deformation dynamics in pure polycrystalline cobalt varying in grain size and the content of residual fcc phase prepared using systematic thermal treatment and cycling. We reveal that the presence of the fcc phase and special ∼71∘ grain boundaries between different hcp martensite variants brings about higher deformability and strength. The fcc phase provides additional slip systems and also accommodates deformation via the stress-induced fcc→hcp transformation during loading. On the other hand, special boundaries enhance structural integrity and suppress the formation of critical defects. Both these non-trivial effects can dominate over the influence of grain size, being a traditional microstructural variable. The ex-situ SEM experiments further reveal that the stress-induced fcc→hcp transformation is sluggish and only partial even at high strains, and it does not give rise to detectable AE signals, unlike in other materials exhibiting martensitic transformation. In turn, these insights into cobalt plasticity provide new avenues for the microstructure and performance optimization towards the desired applications through the modern concept of grain boundary engineering.

Pharmacovigilance study of GLP-1 receptor agonists for metabolic and nutritional adverse events.

Aims: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are employed extensively in the management of type 2 diabetes and obesity. However, there is a paucity of real-world data on their safety and tolerability for metabolic and nutritional adverse events in large sample populations. This study aimed to analyse the metabolic and nutritional safety signatures of different GLP-1 RAs by exploring the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Methods: AEs data were extracted from the FDA Adverse Event Reporting System database for each GLP-1 RA from the time of its launch until the second quarter of 2023. The reported odds ratio (ROR), proportional reporting ratio (PRR), Empirical Bayesian Geometric Mean and Bayesian Confidence Propagation Neural Network were employed to identify AE signals. Results: A system organ class of metabolism and nutrition disorders was employed to filter AE reports, resulting in the identification of 10,450 reports for exenatide, 2,860 reports for liraglutide, 240 reports for albiglutide, 4,847 reports for dulaglutide, 2,905 reports for semaglutide, 1,089 reports for tirzepatide, and 13 reports for lixisenatide. Semaglutide (ROR, 3.34; 95%CI, 3.22), liraglutide (ROR, 2.78; 95%CI, 2.69), and exenatide (ROR, 2.15; 95%CI, 2.11) were associated with metabolism and nutrition disorders. The number of AE signals detected were as follows: albiglutide (n = 1), lixisenatide (n = 2), tirzepatide (n = 11), exenatide (n = 12), liraglutide (n = 16), semaglutide (n = 20), dulaglutide (n = 22). Dehydration was the most frequent AE contributing to serious outcomes for liraglutide (n = 318, 23.93%), dulaglutide (n = 434, 20.90%), semaglutide (n = 370, 25.10%) and tirzepatide (n = 70, 32.86%). The time to onset (TTO) of AE was statistically different between exenatide and the other GLP-1 RAs (p < 0.001), and the Weibull parameters for dehydration for liraglutide, dulaglutide, and semaglutide analyses all showed an early failure-type profile. Conclusion: Our study suggests that exenatide, liraglutide, and semaglutide are more susceptible to metabolic and nutritional AEs than other GLP-1 RAs. Liraglutide, dulaglutide, semaglutide, and tirzepaptide's potential to induce dehydration, necessitates special attention. Despite certain deficiencies, GLP-1 RAs have considerable potential for the treatment of eating disorders.

A real-world data analysis of tirzepatide in the FDA adverse event reporting system (FAERS) database.

Tirzepatide, a glucose-dependent insulinotropic polypeptide (GIP) receptor and glucagon-like peptide-1 (GLP-1) receptor agonist, is indicated for chronic weight management in adults with obesity or overweight as an adjunct to a reduced-calorie diet and increased physical activity. However, the safety profile of Tirzepatide-associated adverse events requires comprehensive evaluation. The AE reports from the first quarter of 2022 to the third quarter of 2023 were selected by exploring the FDA Adverse Event Reporting System (FAERS) database. The new and unexpected potenial AE signals were detected using the disproportionality analysis, including reporting odds ratio(ROR), the proportional reporting ratio (PRR) the Bayesian confidence propagation neural network (BCPNN) and the empirical Bayes geometric mean(EBGM). Then the MedDRA was used to systematically classify the results. A total of 1,904,481 case reports were obtained from 2022Q2 to 2023Q3. Forty-sixth tirzepatide-induced ADRs at the preferred terms (PTs) level are associated with 8 system organ class In addition, this study uncovered multiple anticipated ADRs, such as gastrooesophageal reflux disease, dyspepsia, and vomiting, in line with the drug labels. Moreover, unexpected and significant ADRs at PTs level, such as incorrect dose administered, injection site haemorrhage, and increased appetite, were discovered and linked to Injury, poisoning, and procedural complications, General disorders and administration site conditions, and Metabolism and nutrition disorders at the System Organ Class level. This study offered new perspectives on the monitoring, surveillance, and management of adverse drug reactions related to tirzepatide. The outcomes of severe adverse events and their respective detection signals, along with unexpected significant adverse event signals, are important to consider in efforts to enhance clinical medication safety when using tirzepatide.

To Monitor Fracture Behaviors in Four-Point Cyclic Bending Tests of Low-Activated High Alumina Concrete and Standard Concrete RC Beams Using Acoustic Emission Technique

In this study, acoustic emission monitory (AE) was used to observe and evaluate the mechanical behavior of two kinds of RC beam members, made of low-activation concrete and normal concrete, by general four-point bending test and cyclic four-point bending test. During the loading, acoustic emissions due to cracking, shearing, rubbing, de-bonding or interlocking could be monitored and recorded using a real-time high sampling rate DAQ system and proper sensors; besides, beam displacement, cracking situation and loading were also measured simultaneously. The experiment goals included: (1) to observe the damage mechanisms of the beams under bending test and their corresponding AE performance; and (2) to observe the material fracture and corresponding AE in the beams under the cyclic bending test of 0.5Hz (periodic dynamic condition). After experiments, the fracture history of the beam under general loading test could be revealed through the comparison for the chronicled AE signal density and load/strain data. The damage on beam under the cyclic test could be analysed through the chronological comparison for the cumulative sum of AE signal counts obtained for different test periods and stages. Both the irreversibility on fracture mechanism (affected by the Kaiser effect) and the “remainder toughness” could be discussed through the quantitative analysis on AE characteristics for two different beams. They might relate to the durability and toughness of material for beam.

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

AbstractAcoustic emission is a nondestructive testing (NDT) technique, widely used to monitor the condition of structures for safety reasons especially in real time. The method utilizes the electrical signals generated by the elastic waves in a material under load to detect and locate damage in structures. However, identifying the sources of AE signals in concrete or composite materials can be challenging due to the anisotropic properties of materials and interpreting a large amount of AE data, leading to data misinterpretation and inaccurate detection of damage. Hence, the need for filtering out noise-induced signals from recorded data and emphasizing the actual AE source is crucial for monitoring and source localization of damage in real time. This study proposed a one-dimensional convolutional neural network (1D-CNN) deep learning approach to filter around 22,000 AE data in a reinforced concrete (RC) beam. The model utilizes significant AE parameters identified through neighborhood component analysis (NCA) to classify true AE signals from noise-induced signals. By using the optimized network parameters, a high classification accuracy of 97% and 96.29% was achieved during the training and testing phases, respectively. To check the reliability of the proposed AE filtering model in the real world, it was evaluated and verified using source location AE activities collected during a four-point bending test on a shear-deficient beam. The outcomes suggest that the proposed AE filtration model has the potential to accurately classify AE signals with an accuracy of 92.8% and proved that the filtration model provides accurate and valuable insight into source location determination.

Monitoring warning criterion of acoustic emission active waveguide system based on loess deformation and failure.

The construction of acoustic emission criterion system is crucial for monitoring and providing early warning of geological hazards. In the current soil acoustic emission monitoring methods, the signal generated by soil deformation and failure is weak and experiences high attenuation, resulting in a low level of the monitored signal. One approach to enhance the quality of monitoring data is by utilizing the active waveguide model. However, the current research on the active waveguide model system is not extensive. To address these issues, a set of active waveguide system was designed to improve the data quality of acoustic emission monitoring and early warning. The deformation and failure process of loess monitored by acoustic emission was divided into stages, and the precursor information of acoustic emission for geological disasters in loess areas was comprehensively deconstructed. The data quality advantage of the active waveguide model was verified through comparative experiments of with the passive waveguide model. This study investigates the AE signal characteristics of the active waveguide model. It explores various aspects such as the AE waveform parameter characteristics, the discrimination method for failure mode based on RA-AF value, the AE r-value characteristics, the AE b-value characteristics, and the frequency-amplitude characteristics. The study reveals the evolution law of AE signals in the active waveguide model, including early warning signs and failure morphological characteristics. Furthermore, it constructs a warning criterion for the active waveguide system. The development of this criterion system is of great importance in guiding the monitoring and early warning of geological disasters in loess areas.

Determine the location of acoustic emission source on wood surface by using a wavelet-energy attenuation method

ABSTRACT To determine the location of wood damage and fracture, an acoustic emission source localization method based on wavelet-energy attenuation law was proposed. High-speed acquisition equipment and LabVIEW software were used to design a three-channel AE signal acquisition system. The wavelet transform is used to decompose the original signals into five frequency levels, and the second, third, and fourth level were selected for reconstruction of the acoustic emission signal. Finally, the reconstructed signals was used to determine the location of the acoustic emission source in accordance with the energy attenuation law. The results indicated that when the distance between the three sensors were 100 and 200 mm, the average location error obtained using the original signals in accordance with the energy attenuation law was 12.6 and 18.4 mm, respectively. However, the average location error obtained using the reconstructed signals in accordance with the energy attenuation law was 2.2 and 6.9 mm, respectively. These results reveal that the proposed method can achieve high-precision localization of acoustic emission source. This research enrich and expand the content of AE technology and wooden building research, provide new ideas and methods for non-destructive testing of similar structures, and promote research progress in related fields..