Search Results (41)

In the context of autonomous environmental monitoring, this study investigates a surface acoustic wave (SAW) sensor designed for selective carbon dioxide (CO₂) detection. The sensor is based on a LiTaO₃ piezoelectric substrate with copper interdigital transducers and a polyetherimide (PEI) layer, chosen for its high electromechanical coupling and strong CO₂ affinity. Finite element simulations were conducted to analyze the resonance frequency response under varying gas concentrations, film thicknesses, pressures, and temperatures. Results demonstrate a linear and sensitive frequency shift, with detection capability starting from 10 ppm. The sensor’s autonomy is ensured by a piezoelectric energy harvester composed of a cantilever beam structure with an attached seismic mass, where mechanical vibrations induce stress in a piezoelectric layer (PZT-5H or PVDF), generating electrical energy via the direct piezoelectric effect. Analytical and numerical analyses were performed to evaluate the influence of excitation frequency, material properties, and optimal load on power output. This integrated configuration offers a compact and energy-independent solution for real-time CO₂ monitoring in low-power or inaccessible environments. Full article

Passive wireless surface acoustic wave (SAW) sensors are widely adopted for monitoring the safety status of industrial equipment due to their compact size and maintenance-free operation. Replacing traditional discrete-component interrogators with software-defined radio (SDR) architectures offers lower cost and greater flexibility. However, conventional frequency estimation methods often rely on iterative algorithms with high computational complexity, limiting their real-time applicability. This paper presents an SAW sensing system based on an SDR platform and a non-iterative spectrum-fitting method for SAW frequency measurement. The feasibility of the proposed method is theoretically analyzed, and its performance under different window functions and length of fast Fourier transform (FFT) configurations is evaluated through simulations and experimental measurements. The results demonstrate a favorable trade-off between time efficiency and SAW frequency measurement accuracy. Compared to traditional approaches, the proposed method reduces complexity while maintaining ± 3$\mathrm{k}$$\mathrm{Hz}$ peak-to-peak accuracy with only 4096-point FFT length according to experimental results. Full article

This study reveals the damage mechanisms and fracture evolution characteristics of deeply buried granite with prefabricated joints (inclinations of 0°, 30°, 45°, 60°, and 90°) using uniaxial compression tests monitored by Acoustic Emission (AE) technology. Three-dimensional X-CT technology was used to analyze post-damage fracture evolution in specimens with varying joint inclinations. The results show that the stress–strain curve of deeply buried jointed granite under uniaxial compression includes three stages: initial compaction, crack extension, and failure. AE characteristics align with these stages, showing clear stress responses and timing features. In the initial compaction stage, micro-crack closure dominates, with smaller joint inclinations showing stronger closure effects. In the crack extension stage, joint inclination determines the crack propagation mode. In the failure stage, joint inclination significantly affects the spatial distribution of the rupture network by altering stress concentration areas and crack types. The proportion of shear micro-cracks increases with joint inclination, and peak strength rises with increasing joint angle, potentially accelerating micro-crack evolution. These findings provide valuable insights for designing excavation and instability monitoring in deeply buried multi-jointed granite underground projects. Full article

This study investigates the mechanism behind the cavitation-induced noise in an automotive heater core and proposes a structural solution to eliminate it. Abnormal noise during cold-start conditions in a compact passenger vehicle was traced to cavitation in the heater core of the heating, ventilation, and air conditioning (HVAC) system. Controlled bench tests, in-vehicle measurements, and computational fluid dynamics (CFD) simulations were conducted to analyze flow behavior and identify the precise location and conditions for cavitation onset. Results showed that high flow rates and low coolant pressure generated vapor bubbles near the junction of the upper tank and outlet pipe, producing distinctive impulsive noise and vibration signals. Flow visualization using a transparent pipe and accelerometer data confirmed cavitation collapse at this location. CFD analysis indicated that the original geometry created a high-velocity, low-pressure region conducive to cavitation. A redesigned outlet with a tapered transition and larger diameter significantly improved flow conditions, raising the cavitation index and eliminating cavitation events. Experimental validation confirmed the effectiveness of the modified design. These findings contribute to improving the acoustic performance and reliability of automotive HVAC systems and offer broader insights into cavitation mitigation in fluid systems. Full article

Measuring parameters like heart temperature, heart rate, and cardiac magnetic field aids in analyzing cardiac health and disease. A multi-parameter sensor tailored to the heart can significantly enhance convenience in medical diagnosis and treatment. This work introduces a multi-parameter sensor based on Surface Acoustic Wave Sensors (SAWSs) and magnetostrictive materials, designed to selectively measure various cardiac parameters. SAWSs are characterized by their compact dimensions, which facilitate integration into various medical devices. The wireless and passive characteristics of the sensors enable flexibility in the detection process. This sensor can detect various common physical quantities like weak magnetic fields by the control variable method, ensuring a high degree of accuracy. The working mode of SAWSs is investigated in this study, and the relationship curve concerning various influencing factors is established. Full article

Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) for smart cities. Despite the clear necessity for sound-based diagnostic systems, the scarcity of specialized publicly available datasets presents a major challenge. This study addresses this gap by contributing in multiple dimensions. Firstly, it emphasizes the significance of sound-based diagnostics for real-time detection of faults through analyzing sounds directly generated by vehicles, such as engine or brake noises, and the classification of external emergency sounds, like sirens, relevant to vehicle safety. Secondly, this paper introduces a novel dataset encompassing vehicle fault sounds, emergency sirens, and environmental noises specifically curated to address the absence of such specialized datasets. A comprehensive framework is proposed, combining audio preprocessing, feature extraction (via Mel Spectrograms, MFCCs, and Chromatograms), and classification using 11 models. Evaluations using both compact (52 features) and expanded (126 features) representations show that several classes (e.g., Engine Misfire, Fuel Pump Cartridge Fault, Radiator Fan Failure) achieve near-perfect accuracy, though acoustically similar classes like Universal Joint Failure, Knocking, and Pre-ignition Problem remain challenging. Logistic Regression yielded the highest accuracy of 86.5% for the vehicle fault dataset (DB1) using compact features, while neural networks performed best for datasets DB2 and DB3, achieving 88.4% and 85.5%, respectively. In the second scenario, a Bayesian-Optimized Weighted Soft Voting with Feature Selection (BOWSVFS) approach is proposed, significantly enhancing accuracy to 91.04% for DB1, 88.85% for DB2, and 86.85% for DB3. These results highlight the effectiveness of the proposed methods in addressing key ITS limitations and enhancing accessibility for individuals with disabilities through auditory-based vehicle diagnostics and emergency recognition systems. Full article

Structural health monitoring (SHM) in fiber-reinforced polymer (FRP) composites is essential to ensure safety and reliability during service, particularly in critical industries such as aerospace and wind energy. Traditional methods of analyzing Acoustic Emission (AE) signals in the time domain often fail to accurately detect subtle or early-stage damage, limiting their effectiveness. The present study introduces a novel approach that integrates frequency-domain analysis using the fast Fourier transform (FFT) with deep learning techniques for more accurate and proactive damage detection. AE signals are first transformed into the frequency domain, where significant frequency components are extracted and used as inputs to an autoencoder network. The autoencoder model reduces the dimensionality of the data while preserving essential features, enabling unsupervised clustering to identify distinct damage states. Temporal damage evolution is modeled using Markov chain analysis to provide insights into how damage progresses over time. The proposed method achieves a reconstruction error of 0.0017 and a high R-squared value of 0.95, indicating the autoencoder’s effectiveness in learning compact representations while minimizing information loss. Clustering results, with a silhouette score of 0.37, demonstrate well-separated clusters that correspond to different damage stages. Markov chain analysis captures the transitions between damage states, providing a predictive framework for assessing damage progression. These findings highlight the potential of the proposed approach for early damage detection and predictive maintenance, which significantly improves the effectiveness of AE-based SHM systems in reducing downtime and extending component lifespan. Full article

The sandstone roof rock in the Huaibei mining area contains abundant water at depths of 2–3 m. Water–rock interactions in the rock-surrounding roadway can cause significant deformation, seriously threatening the safety of mine operations. Investigating the deformation and failure mechanisms of water-rich sandstone is therefore of critical importance. In this study, X-ray diffraction and scanning electron microscopy were used to analyze the composition and microstructure of water-rich sandstone. Based on the stress state during the roadway excavation, a true triaxial loading scheme with four different stress paths was designed to study the effects of different moisture contents and loading methods on the mechanical properties of the sandstone. The results show that the deviatoric stress decreased for all stress paths. Acoustic emission (AE) characteristics during the deformation and failure processes were also studied, which indicated that the AE b-value decreased, increased, and then decreased again corresponding to the primary compaction, elastic deformation, and plastic deformation evolutionary processes in the internal microstructure of the rock. The variation in the b-value reflected the development and expansion of internal fractures. These findings provide useful insights for controlling the stability of the surrounding rock in water-rich roadways in coal mines. Full article

Nowadays, much of the attention paid to building construction is focused on sustainability and environmental protection. The materials applied in construction should be safe and free of toxins, but they should also follow the idea of circular construction. Quests for materials with an appropriate structure and composition, unifying features of a construction, insulation (thermally and acoustically), and environmentally friendly material turned our attention in this paper toward expanded perlite (EP). This study aimed to analyze the results of the experimental determination of the basic physical and mechanical parameters of expanded perlite and pure perlite concrete blocks (PPC), i.e., containing 100% EP instead of sand, while in contrast, most existing studies focus only on the partial replacement of sand with EP. This research aims to confirm that PPC containing 100% EP is the product that meets the requirements for load-bearing walls in single-family buildings in European countries such as Poland. The study aimed to determine the procedure for preparing the samples of PPC, i.e., the mixing procedure, the displacement speed during compaction, and the maximum loading force during compaction. It was determined that the appropriate speed of compaction to form the samples is 15 mm per minute, i.e., the same as during the compressive strength tests. The maximum compaction force of 10,000 N during the preparation of samples at a speed of displacement of 15 mm per minute guarantees a compressive strength greater than 3 MPa for dry density class 650, and the method of forming the samples in a single layer, i.e., solid samples. Full article

This paper proposes a novel method to estimate rainfall intensity by analyzing the sound of raindrops. An innovative device for collecting acoustic data was designed, capable of blocking ambient noise in rainy environments. The device was deployed in real rainfall conditions during both the monsoon season and non-monsoon season to record raindrop sounds. The collected raindrop sounds were divided into 1 s, 10 s, and 1 min intervals, and the performance of rainfall intensity estimation for each segment length was compared. First, the rainfall occurrence was determined based on four extracted frequency domain features (average of dB, frequency-weighted average of dB, standard deviation of dB, and highest frequency), followed by a quantitative estimation of the rainfall intensity for the periods in which rainfall occurred. The results indicated that the best estimation performance was achieved when using 10 s segments, corresponding to the following metrics: accuracy: 0.909, false alarm ratio: 0.099, critical success index: 0.753, precision: 0.901, recall: 0.821, and F1 score: 0.859 for rainfall occurrence classification; and root mean square error: 1.675 mm/h, $R^2$: 0.798, and mean absolute error: 0.493 mm/h for quantitative rainfall intensity estimation. The proposed small and lightweight device is convenient to install and manage and is remarkably cost-effective compared with traditional rainfall observation equipment. Additionally, this compact rainfall acoustic collection device can facilitate the collection of detailed rainfall information over vast areas. Full article

The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting human voices in soundscape monitoring projects is useful both for analyzing human disturbance and for privacy filtering. Despite significant strides in deep learning in recent years, the deployment of large neural networks on compact devices poses challenges due to memory and latency constraints. Our approach focuses on leveraging knowledge distillation techniques to design efficient, lightweight student models for speech detection in bioacoustics. In particular, we employed the MobileNetV3-Small-Pi model to create compact yet effective student architectures to compare against the larger EcoVAD teacher model, a well-regarded voice detection architecture in eco-acoustic monitoring. The comparative analysis included examining various configurations of the MobileNetV3-Small-Pi-derived student models to identify optimal performance. Additionally, a thorough evaluation of different distillation techniques was conducted to ascertain the most effective method for model selection. Our findings revealed that the distilled models exhibited comparable performance to the EcoVAD teacher model, indicating a promising approach to overcoming computational barriers for real-time ecological monitoring. Full article

To investigate the dynamic response of fractured rock under cyclic loading and unloading, a WHY-300/10 microcomputer-controlled electro-hydraulic servo universal testing machine was used to conduct uniaxial cyclic loading and unloading tests. Simultaneously, acoustic emission (AE) and a CCD high-speed camera were employed to monitor the fracturing characteristics of sandstone. The mechanical properties, energy evolution, AE characteristics, and deformation of 45° sandstone were analyzed. The results indicate that as the load cycle level increases, both the elastic modulus and deformation modulus exhibit a “parabolic” increase, with a rapid rise initially and a slower rate of increase later. The damping ratio generally shows a decreasing trend but tends to rise near the peak load. The total energy, elastic energy, dissipated energy, damping energy, and damage energy all follow exponential function increases with the load level. The b-value fluctuates significantly during the stable crack propagation phase, unstable crack propagation phase, and peak phase. When the FR (Felicity ratio > 1), the rock is relatively stable; when the FR (Felicity ratio < 1), the rock gradually extends towards an unstable state. The Felicity ratio can be used as a predictive tool for the precursors of rock failure. Shear fractures dominate during the compaction and peak phases, while tensile fractures dominate during the crack propagation phase, ultimately leading to a failure characterized by tensile fracture. High-speed camera observations revealed that deformation first occurs at the tips of the prefabricated cracks and gradually spreads and deflects toward the ends of the sandstone. This study provides theoretical support for exploring the mechanical behavior and mechanisms of fractured rock under cyclic loading and unloading, and it has significant practical implications. Full article

Deep-learning models play a significant role in modern software solutions, with the capabilities of handling complex tasks, improving accuracy, automating processes, and adapting to diverse domains, eventually contributing to advancements in various industries. This study provides a comparative study on deep-learning techniques that can also be deployed on resource-constrained edge devices. As a novel contribution, we analyze the performance of seven Convolutional Neural Network models in the context of data augmentation, feature extraction, and model compression using acoustic data. The results show that the best performers can achieve an optimal trade-off between model accuracy and size when compressed with weight and filter pruning followed by 8-bit quantization. In adherence to the study workflow utilizing the forest sound dataset, MobileNet-v3-small and ACDNet achieved accuracies of 87.95% and 85.64%, respectively, while maintaining compact sizes of 243 KB and 484 KB, respectively. Henceforth, this study concludes that CNNs can be optimized and compressed to be deployed in resource-constrained edge devices for classifying forest environment sounds. Full article

A novel approach for the fracture characterization of soft adhesive materials using spiral cracking patterns is presented in this study. This research particularly focuses on hydrocarbon polymeric materials, such as asphalt binders. Ten different asphalt materials with distinct fracture characteristics were investigated. An innovative integrated experimental–computational framework coupling acoustic emissions (AE) approach in conjunction with a machine learning-based Digital Image Analysis (DIA) method was employed to precisely determine the crack geometry and characterize the material fracture behavior. Cylindrical-shaped samples (25 mm in diameter and 20 mm in height) bonded to a rigid substrate were employed as the testing specimens. A cooling rate of −1 °C/min was applied to produce the spiral cracks. Various image processing techniques and machine learning algorithms such as Convolutional Neural Networks (CNNs) and regression were utilized in the DIA to automatically analyze the spiral patterns. A new parameter, “Spiral Cracking Energy (E_Spiral)”, was introduced to assess the fracture performance of soft adhesives. The compact tension (CT) test was conducted at −20 °C with a loading rate of 0.2 mm/min to determine the material’s fracture energy (G_f). The embrittlement temperature (T_EMB) of the material was measured by performing an AE test. This study explored the relationship between the spiral tightness parameter (“b”), E_Spiral, G_f, and T_EMB of the material. The findings of this study showed a strong positive correlation between the E_Spiral and fracture energies of the asphalt materials. Furthermore, the results indicated that both the spiral tightness parameter (“b”) and the embrittlement temperature (T_EMB) were negatively correlated with the E_Spiral and G_f parameters. Full article

The aim of this paper is to analyze the deformation-failure degree and microstructure variations in coal under the cryogenic-freezing effect of liquid CO₂. In this paper, X-ray CT scanning technology is adopted to measure the microscopic-morphological parameters of coal. Drawing support from the image processing and three-dimensional (3D) visualization functions of Avizo software, 3D spatial structure variation rules, as well as the deformation and permeability parameters, are quantitatively calculated. Under the effect of LCO₂ cryogenic freezing, the macroscopic mechanical properties and deformation-failure degree of coal are thoroughly analyzed. The results show that fracture-scale parameters of treated coal are significantly increased, resulting in spatial structure parameters including the coal plug total volume (V_t), fracture network volume (V₀), and proportion of fracture network (μ₀) to increase by 17.11%, 56.57%, and 55.59%, respectively. A comparison analysis indicates that the coverage area of a single value function from the percolation theoretical model for treated coal plugs becomes larger, and its percolation curves are more intensive; the quantitative coal permeability coefficients are increased to more than 40% on average, which further proves that the permeability of coal by using LCO₂ cryogenic freezing is significantly improved. Under the same uniaxial stress loading rate, the peak stress threshold value required by treated coal in the compaction and elastoplastic deformation stage is decreased. The corresponding output acoustic emission energy is apparently increased, owing to the increased brittleness of coal, and deformation failure of coal occurs more easily. Simultaneously, the fracture network and matrix surface of treated coal are more complex, and the corresponding fractal characteristic is obvious. It could be thus concluded that the coal plugs have deformation-failure changes under cryogenic freezing by using LCO₂, increasing the proportion of coal microstructure and enhancing coal permeability. Therefore, the capability of gas migration through the coal microstructure becomes easier, which is favorable for coalbed methane recovery. Full article