Search Results (353)

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is capable of capturing the transient stress waves induced by leakage, thereby furnishing an effective means for the real-time monitoring and quantitative assessment of internal leakage within the valve body. This paper conducts a systematic review of the theoretical foundations, signal-processing methodologies, and the latest research advancements related to the technology for detecting internal leakage in the valve body based on acoustic emission. Firstly, grounded in Lechlier’s acoustic analogy theory, the generation mechanism of acoustic emission signals arising from valve body leakage is elucidated. Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. Moreover, this paper recapitulates the current challenges encountered by this technology and delineates future research orientations, such as the fusion of multi-modal sensors, the deployment of lightweight deep learning models, and integration with the Internet of Things. This study provides a systematic reference for the engineering application and theoretical development of the acoustic emission-based technology for detecting internal leakage in valves. Full article

The wind turbine blade is subject to multi-source impacts, such as bird strikes, lightning strikes, and hail, throughout its extended service. Accurate localization of those impact sources is a key technical link in structural health monitoring of the wind turbine blade. In this paper, a single-sensor impact source localization method is proposed. Capitalizing on deep learning frameworks, this method innovatively transforms the impact source localization problem into a classification task, thereby eliminating the need for anisotropy compensation and correction required by conventional localization algorithms. Furthermore, it leverages the inherent coding effects of the blade’s material and geometric anisotropy on impact sources originating from different positions, enabling localization using only a single sensor. Experimental results show that the method has a high localization accuracy of 96.9% under single-sensor conditions, which significantly reduces the cost compared to the traditional multi-sensor array scheme. This study provides a cost-effective solution for real-time detection of wind turbine blade impact events. Full article

This study presents a comprehensive investigation of acoustic emission (AE) characteristics for condition monitoring of rolling bearings in passenger ropeway systems. Through controlled laboratory experiments and field validation across multiple operational ropeways, we establish an optimized AE-based diagnostic framework. Key findings demonstrate that resonant VS150-RIC sensors outperform broadband sensors in defect detection, showing greater energy response at characteristic frequencies for inner race defects. The RMS parameter emerges as a robust diagnostic indicator, with defective bearings exhibiting periodic peaks and higher mean RMS values. Field tests reveal progressive RMS escalation preceding visible damage, enabling predictive maintenance. Furthermore, we develop a novel Paligemma LLM model for automated wear detection using AE time-domain images. The research validates the AE technology’s superiority over conventional vibration methods for low-speed bearing monitoring, providing a scientifically grounded approach for safety-critical ropeway maintenance. Full article

Safe and efficient operation of port cranes is vital for maintaining the efficiency of global maritime logistics. However, traditional non-destructive testing methods face significant limitations in harsh port environments, such as periodic inspection intervals, restricted access to structural components, and a lack of real-time monitoring. This review explores the emerging paradigm of remote non-destructive testing through the integration of vibration and acoustic emission sensors with Internet of Things platforms. By enabling continuous, real-time monitoring, these sensor systems can detect early indicators of mechanical degradation, structural fatigue, and corrosion. This study synthesizes findings from over 100 peer-reviewed sources and identifies a significant gap in the application of these technologies to port cranes. Although vibration and acoustic emission sensors have been widely studied in various fields, their application to port cranes remains underexplored, presenting a novel and promising avenue for future research and practical applications. The unique operational demands and structural complexities of port cranes, coupled with their critical role in global trade logistics, make them ideal for leveraging these sensors in tandem with Internet of Things solutions. This integration not only overcomes the limitations of traditional non-destructive testing methods, but also offers substantial benefits, including enhanced safety, reduced inspection costs, and improved operational efficiency. This review concludes by proposing future research directions to enhance sensor performance, data analytics, and Internet of Things integration, paving the way for predictive maintenance strategies that increase operational uptime and improve safety in port crane operations. Full article

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

In this study, graded cemented carbide layers were fabricated using Laser Powder-Directed Energy Deposition (LP-DED) to investigate the effects of laser input energy and WC content on crack formation, compositional distribution, and hardness. Two-layer structures were formed, with the first layer containing either 30.5 wt.% or 42.9 wt.% WC and the second layer containing 63.7 wt.% WC. Crack formation was evaluated in situ using acoustic emission (AE) sensors, and elemental composition and Vickers hardness were measured across the cross-section of the deposited layers. The results showed that crack formation increased with higher laser power and higher WC content in the first layer. Elemental analysis revealed that higher laser input led to greater Co enrichment and reduced W content near the surface. Additionally, the formation of brittle structures was observed under high-energy conditions, contributing to increased hardness but decreased toughness. These findings indicate that both WC content and laser energy strongly influence the microstructural evolution and mechanical properties of graded cemented carbide layers. Optimizing the balance between WC content and laser parameters is essential for improving the crack resistance and performance of cemented carbide layers in additive manufacturing applications. Full article

In this study, acoustic emission (AE) analysis was carried out to evaluate and quantify the cracking behavior of large-scale multi-layered reinforced concrete beams under flexural tests. Four normal concrete beams were repaired by adding a layer of crumb rubberized engineered cementitious composites (CRECCs) or powder rubberized engineered cementitious composites (PRECCs), in either the tension or compression zone of the beam. Additional three unrepaired control beams, fully cast with either normal concrete, CRECCs, or PRECCs, were tested for comparison. Flexural tests were performed on all the tested beams in conjunction with AE monitoring until failure. AE raw data obtained from the flexural testing was filtered and then analyzed to detect and assess the cracking behavior of all the tested beams. A variety of AE parameters, including number of hits and cumulative signal strength, were utilized to study the crack propagation throughout the testing. Furthermore, b-value and intensity analyses were implemented and yielded additional parameters called b-value, historic index [H (t)], and severity (S_r)_. The analysis of the changes in the AE parameters allowed the identification of the first crack in all tested beams. Moreover, varying the rubber particle size (crumb rubber or powder rubber), repair layer location, or AE sensor location showed a significant impact on the number of hits and signal amplitude. Finally, by using the results of the study, it was possible to develop a damage quantification chart that can identify different damage stages (first crack and ultimate load) related to the intensity analysis parameters (H (t) and S_r). Full article

The integration of acoustic emission (AE) signals into adaptive control systems for CNC wood milling represents a promising advancement in intelligent manufacturing. This study investigated the feasibility of using AE signals for the real-time monitoring and control of CNC milling processes, focusing on medium-density fiberboard (MDF) as the workpiece material. AE signals were captured using dual-channel sensors during side milling on a five-axis CNC machine, and their characteristics were analyzed across varying spindle speeds and feed rates. The results showed that AE signals were sensitive to changes in machining parameters, with higher spindle speeds and feed rates producing increased signal amplitudes and distinct frequency peaks, indicating enhanced cutting efficiency. The statistical analysis confirmed a significant relationship between AE signal magnitude and cutting conditions. However, limitations related to material variability, sensor configuration, and the narrow range of process parameters restrict the broader applicability of the findings. Despite these constraints, the results support the use of AE signals for adaptive control in wood milling, offering potential benefits such as improved machining efficiency, extended tool life, and predictive maintenance capabilities. Future research should address signal variability, tool wear, and sensor integration to enhance the reliability of AE-based control systems in industrial applications. Full article

This work presents an on-field noise analysis during the class breaks in Greek school units (a high school and a senior high school) based on the design and deployment of low-cost IoT sensor nodes and IoT platforms. The course breaks form 20% of a regular school day, during which intense mobility and high noise levels usually evolve. Indoor noise levels, along with environmental conditions, have been measured through a wireless network that comprises IoT nodes that integrate humidity, temperature, and acoustic level sensors. PM₁₀ and PM_2.5 values have also been acquired through data sensors located nearby the school complex. School buildings that have been recently renovated for minimizing their energy footprint and CO₂ emissions have been selected in comparison with similar works in academia. The data are collected, shipped, and stored into a time-series database in cloud facilities where an IoT platform has been developed for processing and analysis purposes. The findings show that low-cost sensors can efficiently monitor noise levels after proper adjustments. Additionally, the statistical evaluation of the received sensor measurements has indicated that ubiquitous high noise levels during the course breaks potentially affect teachers’ leisure time, despite the thermal isolation of the facilities. Within this context, we prove that the proposed IoT Sensor Network could form a tool to essentially monitor school infrastructures and thus to prompt for improvements regarding the building facilities. Several guides to further mitigate noise and achieve high-quality levels in learning institutes are also described. Full article

Models for must fermentation kinetics are investigated and used in simulations for enabling control strategies based on temperature adjustment during the alcoholic fermentation process using the estimated CO₂ rate. An acoustic emission digital processing approach for estimating the CO₂ rate in the must fermentation process is proposed and investigated. The hardware architecture was designed considering easy integration with other sensor networks, remote monitoring of the fermentation-related parameters, and wireless communication between the fermentation vessel and the processing unit. It includes a virtual CO₂ rate sensor, having as input the audio signal collected by a microphone and as output the estimated CO₂ rate. Digital signal processing performed on an embedded board involves acquisition, filtering, analysis, and feature extraction. Time domain-based methods for analyzing the audio signal were implemented. An experimental setup with a small fermentation vessel (100 L) was used for CO₂ rate monitoring during the fermentation of grape must. The estimated CO₂ rate data were fitted with the CO₂ rate profiles generated by simulating the models under similar conditions. Simulations for controlling fermentation kinetics through a CO₂ rate-based temperature control were performed and analyzed. The simulations indicate the proposed approach as valid for a closed-loop system implementation capable of controlling kinetics behavior using estimated CO₂ rate and temperature control. Full article

This paper introduces an acoustic-based monitoring system for high-speed CNC drilling, aimed at optimizing processes and enabling real-time machine state detection. High-fidelity acoustic sensors capture sound signals during drilling operations, allowing the identification of critical events such as tool engagement, material breakthrough, and tool withdrawal. Advanced signal processing techniques, including spectrogram analysis and Fast Fourier Transform, extract dominant frequencies and acoustic patterns, while machine learning algorithms like DBSCAN clustering classify operational states such as cutting, breakthrough, and returning. Experimental studies on materials including acrylic, PTFE, and hardwood reveal distinct acoustic profiles influenced by material properties and drilling conditions. Smoother sound patterns and lower dominant frequencies characterize PTFE drilling, whereas hardwood produces higher frequencies and rougher patterns due to its density and resistance. These findings demonstrate the correlation between acoustic emissions and machining dynamics, enabling non-invasive real-time monitoring and predictive maintenance. As AI power increases, it is expected to extract in-situ process information and achieve higher resolution, enhancing precision in data interpretation and decision-making. A key contribution of this project is the creation of an open sound library for drilling processes, fostering collaboration and innovation in intelligent manufacturing. By integrating big data concepts and intelligent algorithms, the system supports continuous monitoring, anomaly detection, and process optimization. This AI-ready hardware enhances the accuracy and efficiency of drilling operations, improving quality, reducing tool wear, and minimizing downtime. The research establishes acoustic monitoring as a transformative approach to advancing CNC drilling processes and intelligent manufacturing systems. Full article

This study investigated the changes in the acoustic emission (AE) activity emitted in fiber-reinforced concrete (FRC) under flexure at two temperatures (25 °C and −20 °C). Seven concrete mixtures were developed with different water-binder ratios (w/b) (0.4 and 0.55), different fiber materials (steel fiber (SF) and synthetic polypropylene fiber (Syn-PF)), different fiber lengths (19 mm and 38 mm), and various Syn-PF contents (0%, 0.2%, and 1%). Prisms with dimensions of 100 × 100 × 400 mm from each mixture underwent a four-point monotonic flexure load while collecting the emitted acoustic waves via attached AE sensors. AE parameter-based analyses, including b-value, improved b-value (Ib-value), intensity, and rise time/average signal amplitude (RA) analyses, were performed using the raw AE data to highlight the change in the AE activity associated with different stages of damage (micro- and macro-cracking). The results showed that the number of hits, average frequency, cumulative signal strength (CSS), and energy were higher for the waves released at −20 °C compared to those obtained at 25 °C. The onset of the first visible micro- and macro-cracks was noticed to be associated with a significant spike in CSS, historic index (H (t)), severity (S_r) curves, a noticeable dip in the b-value curve, and a compression in bellows/fluctuations of the Ib-value curve for both testing temperatures. In addition, time and load thresholds of micro- and macro-cracks increased when samples were cooled down and tested at −20 °C, especially in the mixtures with higher w/b, longer fibers, and lower fiber content. This improvement in mechanical performance and cracking threshold limits was associated with higher AE activity in terms of an overall increase in CSS, S_r, and H (t) values and an overall reduction in b-values. In addition, varying the concrete mixture design parameters, including the w/b ratio as well as fiber type, content, and length, showed a significant impact on the flexural behavior and the AE activity of the tested mixtures at both temperatures (25 °C and −20 °C). Intensity and RA analysis parameters allowed the development of two charts to characterize the detected AE events, whether associated with micro- and macro-cracks considering the temperature effect. Full article

The integrity of multi-bolted flanges is crucial for ensuring safety and operational efficiency in industrial systems across sectors such as oil and gas, chemical processing, and water treatment. Traditional non-destructive testing (NDT) methods often require operational downtime and may lack sensitivity for early-stage defect detection. This review examines acoustic emission testing (AET), a real-time monitoring technique for detecting acoustic waves generated by material defects. An analysis of 145 studies demonstrated AET’s effectiveness in detecting early-stage defects across various materials and industrial applications. Recent advances in sensor technology and signal processing have significantly enhanced AET’s capabilities. However, challenges remain regarding environmental noise interference and the need for specialized expertise. The review identifies knowledge gaps and proposes future research directions, including planned laboratory experiments to characterize defect signals in multi-bolted flange systems under different operational conditions. The findings position AET as a transformative solution for industrial inspection and maintenance, offering enhanced safety and reliability for critical infrastructures. Full article

The present article investigates the effect of superplasticizer and graphene nanoplatelet addition on the flexural and electrical behaviour of nanocomposites for applications related to the restoration/conservation of Cultural Heritage Monuments in laboratory scale. Graphene nanoplatelets’ addition is used to transform the matrix into a piezo-resistive self-sensor by efficiently dispersing electrically conductive graphene nanoplatelets (GnPs) in the material matrix to create electrically conductive paths. Nevertheless, the appropriate dispersion is difficult to be achieved as the GnPs tend to agglomerate due to Van der Waals forces. To this end, the effect of the addition of carboxyl-based superplasticizer (SP) is proposed in the present investigation to efficiently disperse the GnPs in the water mix of the binders. Five (5) different ratios of SP per GnPs addition were examined. The GnPs concentration was chosen to be within the range of 0.05 to 1.50 wt.% of the binder. The same ultrasonic energy was applied in all of the suspensions to further aid the dispersion process. The incorporation of graphene nanoplatelets at low concentrations (0.15 wt.%) significantly increases flexural strength when added in equal quantity to superplasticizer (SP1 series). The SP addition at higher concentrations does not enhance the mechanical properties through effective dispersion of the GnPs. Additionally, a correlation was established between the electrical resistivity (ρ) values of the produced nanocomposites and the modulus of elasticity as a function of the GnPs concentration. The functional correlation between these parameters was also confirmed by linear regression analysis, resulting from the experimental data fitting. Finally, the acoustic emission (AE) can effectively capture damage evolution in such lime-based composites, while the emitted cumulative energy rises as the GnPs concentration is increased. Full article

The performance of steam traps plays an important role in the normal operation of steam systems. It also contributes to the improvement of thermal efficiency of steam-using equipment and the rational use of energy. As an important component of the steam system, it is crucial to monitor the state of the steam trap and establish a correlation between the acoustic emission signal and the internal leakage state. However, in actual test environments, the acoustic emission sensor often collects various background noises alongside the valve internal leakage acoustic emission signal. Therefore, to minimize the impact of environmental noise on valve internal leakage identification, it is necessary to preprocess the original acoustic emission signals through noise reduction before identification. To address the above problems, a denoising method based on a sparrow search algorithm, variational modal decomposition, and improved wavelet thresholding is proposed. The sparrow search algorithm, using minimum envelope entropy as the fitness function, optimizes the decomposition level K and the penalty factor α of the variational modal decomposition algorithm. This removes modes with higher entropy in the modal envelopes. Subsequently, wavelet threshold denoising is applied to the remaining modes, and the denoised signal is reconstructed. Validation analysis demonstrates that the combination of SSA-VMD and the improved wavelet threshold function effectively filters out noise from the signal. Compared to traditional thresholding methods, this approach increases the signal-to-noise ratio and reduces the root-mean-square error, significantly enhancing the noise reduction effect on the steam trap’s background noise signal. Full article