Search Results (269)

As the core device for protecting the safety of the pressure-bearing system, the spring full-open safety valve is prone to various forms of valve seat sealing surface damage after long-term opening and closing impact, corrosion, and medium erosion, which may lead to internal leakage. In view of the problems that the high-frequency acoustic emission signal of the internal leakage of the safety valve has, namely, a large number of energy-overlapping areas in the frequency domain, the overall signal presents broadband characteristics, large noise content, and no obvious time–frequency characteristics. A composite denoising method, IWTD, improved wavelet threshold function with dual adjustable factors, and the improved VMD algorithm is proposed. In view of the problem that the optimal values of the dual adjustment factors a and b of the function are difficult to determine manually, an improved dung beetle optimization algorithm is proposed, with the maximum Pearson coefficient as the optimization target; the optimization is performed within the value range of the dual adjustable factors a and b, so as to obtain the optimal value. In view of the problem that the key parameters K and α in VMD decomposition are difficult to determine manually, the maximum Pearson coefficient is taken as the optimization target, and the improved dung beetle algorithm is used to optimize within the value range of K and α, so as to obtain the IVMD algorithm. Based on the IVMD algorithm, the characteristic decomposition of the internal leakage acoustic emission signal occurs after the denoising of the IWTD function is performed to further improve the denoising effect. The results show that the Pearson coefficients of all types of internal leakage acoustic emission signals after IWTD-IVMD composite noise reduction are greater than 0.9, which is much higher than traditional noise reduction methods such as soft and hard threshold functions. Therefore, the IWTD-IVMD composite noise reduction method can extract more main features out of the measured spring full-open safety valve internal leakage acoustic emission signals, and has a good noise reduction effect. Feature recognition after noise reduction can provide a good evaluation for the safe operation of the safety valve. Full article

Acoustic emission (AE) technology has been extensively applied in the damage assessment of steel strands; however, it remains inadequate in identifying and quantifying the number of strand fractures, which limits the accuracy and reliability of prestressed structure monitoring. In this study, a test platform based on practical engineering was built. The AE monitoring method using a waveguide rod was applied to identify signals from different numbers of strand fractures, and their acoustic characteristics were analyzed using Fourier transform and multi-bandwidth wavelet transform. The propagation attenuation behavior of the AE signals in the waveguide rod was then analyzed, and the optimal parameters for field monitoring as well as the maximum number of plates suitable for series beam plates were determined. The results show that AE signals decrease exponentially with an increasing propagation distance, and attenuation models for various AE parameters were established. As the number of strand fractures increases, the amplitude of the dominant frequency increases significantly, and the energy distribution shifts towards higher-frequency bands. This finding introduces a novel approach for quantifying fractures in steel strands, enhancing the effectiveness of AE technology in monitoring and laying a foundation for the development of related technologies. Full article

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

This study presents a novel methodology for the real-time characterisation and quantitative assessment of damage in fibre-reinforced polymers (FRPs) using acoustic emission (AE) techniques. While FRPs offer superior mechanical properties for structural applications, their anisotropic nature introduces complex damage mechanisms that are challenging to detect with conventional inspection methods. Our approach advances beyond traditional peak frequency analysis by implementing a multi-variant frequency assessment that can detect and evaluate simultaneously occurring damage modes. By applying the fast Fourier transform and examining multiple frequency peaks within AE signals, we successfully identified five distinct damage mechanisms in carbon fibre composites: matrix cracking (100–200 kHz), delamination (205–265 kHz), debonding (270–320 kHz), fibre fracture (330–385 kHz), and fibre pullout (395–490 kHz). A comparative analysis with wavelet transform methods demonstrated that our approach provides earlier detection of critical damage events, with delamination identified approximately 28 s sooner than with conventional techniques. The proposed methodology enables a more accurate quantitative assessment of structural health, facilitating timely maintenance interventions for large-scale FRP structures, such as wind turbine blades, thereby enhancing reliability while reducing operational downtime and maintenance costs. 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

Modified phosphogypsum (MPG) is a new type of solid waste, which could show unique mechanical properties in complex stress conditions. In this study, the effects of different loading rates (0.05, 0.1, 0.5, and 1 MPa/s) on the mechanical properties and acoustic emission (AE) characteristics of modified phosphogypsum were systematically studied through uniaxial compression tests combined with AE technology. The results showed that (1) the peak strength and elastic modulus of MPG increased as a power function of the loading rate, while the peak strain gradually decreased. (2) The cumulative event count of AE decreased as a power function with an increasing loading rate. Compared to the lowest loading rate, the cumulative event count was reduced by nearly two orders of magnitude. (3) An increase in the loading rate resulted in greater large-scale macroscopic failure in MPG specimens, along with an increased proportion of low-frequency AE signals and tensile cracks. (4) The b-value of AE decreased with an increasing loading rate, suggesting that microcrack-dominated small-scale damage prevailed at low loading rates, whereas large-scale damage became more pronounced at high loading rates. The abrupt drop in the b-value served as a precursor signal for macroscopic failure. This study presents an innovative methodology combining variable loading rates with AE technology to investigate the mechanical response of MPG, and the findings reveal the influence of the loading rate on the mechanical properties and AE characteristics of MPG, providing a theoretical basis for its engineering application under different loading environments. Full article

High-frequency injection (HFI) is widely adopted for the sensorless control of permanent magnet synchronous motors (PMSMs) at low speeds. However, conventional HFI strategies relying on fixed-frequency carrier modulation and square-wave injection concentrate current harmonic energy within narrow spectral bands, thereby inducing pronounced high-frequency motor vibrations and noise. To mitigate this issue, this paper proposes a noise suppression strategy based on synchronized periodic frequency modulation (PFM) of both the carrier and high-frequency square-wave signals. By innovatively synchronizing the periodic modulation of the triangular carrier in space vector pulse width modulation (SVPWM) with the injected high-frequency square wave, harmonic energy dispersion and noise reduction are achieved, substantially lowering peak acoustic emissions. First, the harmonic characteristics of the voltage-source inverter output under symmetric triangular carrier SVPWM are analyzed within a sawtooth-wave PFM framework. Concurrently, a harmonic current model is developed for the high-frequency square-wave injection method, enabling the precise derivation of harmonic components. A frequency-synchronized modulation strategy between the carrier and injection signals is proposed, with a rigorous analysis of its harmonic suppression mechanism. The rotor position is then estimated via high-frequency signal extraction and a normalized phase-locked loop (PLL). Comparative simulations and experiments confirm significant noise peak attenuation compared to conventional methods, while position estimation accuracy remains unaffected. This work provides both theoretical and practical advancements for noise-sensitive sensorless motor control applications. Full article

In this work, experimental results on the generation of acoustic contributions during the discharge process of different granular materials from both vertical and inclined silos are presented. The experiments show that the generation of acoustic emissions associated with the “silo music” phenomenon occurs not only in vertical silos but also in inclined ones. The acoustic signals produced during the silo discharge process are recorded and analysed in both time and frequency domains. The frequency analysis focuses on low frequencies near the lower auditory threshold of 20 Hz, demonstrating that the spectral components of the acoustic signals are related to the mass flow rate and the discharge velocity of the granular material. Full article

A theoretical model is presented for the accurate detection of a gas leak source through a pinhole in a cylindrical storage vessel using the acoustic emission (AE) technique. Pinholes of various diameters ranging from 0.20 to 1.2 mm were installed as leak sources, and safe N₂ was used as a filler gas. AE signals were measured and analyzed in terms of AE parameters (such as frequency, amplitude and RMS) as a function of angle and axial distance. Among them, the amplitude characteristic was the most important parameter to determine the leakage dynamics of AE with a continuous waveform. The simulation of AE amplitude was performed using the theoretical model for AE. For practical applications, the theoretical formula was modified into two semi-empirical equations by introducing the normalization method to fit the angular and axial characteristics of the observed AE amplitude, respectively. The main finding of this study is that the semi-empirical equations provide an accurate solution for leak source localization in the cylindrical vessel. As a priori knowledge, the value of ${\kappa }_{\eta }$ in Green’s function, which determines the angular and axial dependence of the AE amplitude, was determined by applying external excitation to the cylinder surface. The proposed formulas provide a suitable approach for practical application in the localization of leak sources in cylindrical storage tanks. 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

To ensure high quality of the machined surface, various methods are used to assess the turning process. This process can be monitored using indirect techniques, such as measuring cutting forces and recording acoustic emission (AE) signals, which help determine the stability of machining conditions. The tests were carried out on AW-7020 alloy shafts turned using a tool with a replaceable CCGT09T302-DL insert. Cutting forces were measured using a Kistler dynamometer, while AE signals were recorded with a system from Physical Acoustics Corporation. Surface quality was evaluated based on roughness measurements, with the Ra parameter ranging from 1.67 to 5.03 μm. An increase in cutting forces, particularly the Fz component, resulted in higher surface roughness. The Fz force ranged from 41 to 251.8 N. Parameters of the AE signal made it possible to identify the most stable turning conditions. For this purpose, the standard deviation of the selected parameters—such as amplitude and RMS—was compared. Additionally, spectral analysis of the AE signal allowed observation of frequency-related changes. The test results indicated that the most stable cutting conditions—and, consequently, the best surface quality—were achieved for the sample machined with the following parameters: Vc = 300 m/min, ap = 0.5 mm, f = 0.078 mm/rev. 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

This paper offers an experimental approach that integrates acoustic emission (AE) monitoring with machine learning (ML) to identify damage mechanisms and predict the mechanical properties of 3D-printed biocomposites. Specimens were fabricated using a bio-filament composed of a PLA matrix reinforced with 10% wt. of Lygeum spartum fibers and were subjected to tensile and flexural tests. The processed dataset, comprising six normalized features (cumulative rise, duration, count, frequency, energy, and amplitude) was used to train four ML models: Random Forest Regression (RFR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Decision Trees (DT) implemented in Python using libraries such as scikit-learn, pandas, and numpy. The prediction models were developed using an 80/20 train–test split and further validated by 5-fold cross-validation, with performance evaluated by R-squared (R²) and Mean Squared Error (MSE) metrics. Our results demonstrate robust prediction capabilities, with the RFR model achieving the highest accuracy (R² > 0.98 and MSE as low as 0.013 for tensile stress prediction). Additionally, unsupervised clustering using K-means was applied to group AE signals into distinct clusters corresponding to different damage modes. This comprehensive methodology not only enhances our understanding of damage evolution in composite materials but also establishes a data-driven framework for non-destructive evaluation and structural health monitoring. Full article

Railways are crucial in economic development and improving people’s livelihoods. Therefore, defect detection and maintenance of rails are particularly important. In order to accurately separate and identify the rail defect from the mixed signals by acoustic emission (AE) techniques, this paper proposes a novel time–frequency separation neural network (TFSNN) architecture to solve the problems existing in the blind source separation (BSS), such as in non-stationary signals and low stability in the convergence. Combined with the smoothed pseudo Wigner–Ville distribution (SPWVD), this method can increase the spectrogram resolution, suppress the noise interference, and effectively improve the extraction performance of crack signals. In addition, 1D-CNN and GRU structures were introduced in the TFSNN structure to exploit the dominant features from AE signals. A dense regressor was also subsequently used to estimate the separation weights. Simulation and experiments showed that compared with traditional algorithms like independent component analysis, shallow neural networks, and time–frequency blind source separation, the proposed algorithm can provide better separation performance and higher stability in rail crack detection. Full article

To study the influence of the main roof period pressure on the instability mechanism of rock pillars with burst liability, the composite loading mode of “pre-cycling loading + continuous loading with a constant rate” was used to conduct compression experiments on rock samples. Meanwhile, the mechanical behaviour response characteristics of rock samples were discussed. Experiment results are shown as follows: (1) mechanical properties of rock samples were strengthened by closing primary pores under pre-cycling loading. The surface roughness and secondary crack number decreased gradually with the pre-cycling threshold; (2) the Kaiser effect of AE (Acoustic Emission) signals was significant in the second and third pre-cycling loading and unloading stages. The Kaiser effect disappeared in the continuous loading stage; (3) AF-RA (Average Frequency-Risetime Amplitude) signals were distributed in a dense-sparse-dense form. Low AF and high RA shear type cracks were more common. Shear failure was the dominant failure mode in rock samples. Full article