Search Results (2,765)

Acoustic telemetry is a tool for tracking animals, but transmitted signals from tagged animals are not always detected. Detection efficiency declines with increasing background noise, which can have both abiotic and biotic sources. The abiotic noise present in reef environments (waves, bubbles, etc.) is primarily low-frequency, but snapping shrimp create high-frequency noise that can interfere with transmission detections. Prior work in shallow coastal reefs correlated winds with less high-frequency background noise, and hypothesized that it was due to a balance of biotic and/or abiotic factors: shrimp may be less active during high wind events, and sound attenuation at the surface increases with wave height. To test this hypothesis, passive acoustic recordings from a live-bottom reef are used to quantify snapping shrimp snap rate. Snap rate was strongly correlated with temperature, and warmer environments appeared to be challenging for acoustic telemetry. However, the majority of synoptic variability in noise is shown to be driven by abiotic attenuation. Wind speed has little to no effect on snapping shrimp behavior, but has a significant inverse correlation with high-frequency noise levels due to surface attenuation of high-frequency noise, and therefore a positive effect on detection efficiency, pointing to primarily abiotic forcing behind noise variability and resulting telemetry success. This research gives context to previously collected detection data and can be leveraged to help plan future acoustic arrays in shallow, complex, and/or noisy environments, potentially predicting changes in detection range. Full article

Photoacoustic imaging (PAI) is an emerging modality that merges optical and ultrasound imaging to provide high-resolution and functional insights into biological tissues. This technique leverages the photoacoustic effect, where tissue absorbs pulsed laser light, generating acoustic waves that are captured to reconstruct images. While lasers have traditionally been the light source for PAI, their high cost and complexity drive interest towards alternative sources like light-emitting diodes (LEDs). This study evaluates the feasibility of using an avalanche oscillator to drive high-power LEDs in a basic photoacoustic imaging system. An avalanche oscillator, utilizing semiconductor avalanche breakdown to produce high-voltage pulses, powers LEDs to generate short, high-intensity light pulses. The system incorporates an LED array, an ultrasonic transducer, and an amplifier for signal detection. Key findings include the successful generation of short light pulses with sufficient intensity to excite materials and the system’s capability to produce detectable photoacoustic signals in both air and water environments. While LEDs demonstrate cost-effectiveness and portability advantages, challenges such as lower power and broader spectral bandwidth compared to lasers are noted. The results affirm that LED-based photoacoustic systems, though currently less advanced than laser-based systems, present a promising direction for affordable and portable imaging technologies. Full article

As one of the key components in rotating machinery, rolling bearings have a crucial impact on the safety and efficiency of production. Acoustic signal is a commonly used method in the field of mechanical fault diagnosis, but an overlapping phenomenon occurs very easily, which affects the diagnostic accuracy. Therefore, effective blind source separation and noise reduction of the acoustic signals generated between different devices is the key to bearing fault diagnosis using acoustic signals. To this end, this paper proposes a blind source separation method based on an AFSA-FastICA (Artificial Fish Swarm Algorithm, AFSA). Firstly, the foraging and clustering characteristics of the AFSA algorithm are utilized to perform global optimization on the aliasing matrix W, and then inverse transformation is performed on the global optimal solution W, to obtain a preliminary estimate of the source signal. Secondly, the estimated source signal is subjected to CEEMD noise reduction, and after obtaining the modal components of each order, the number of interrelationships is used as a constraint on the modal components, and signal reconstruction is performed. Finally, the signal is subjected to frequency domain feature extraction and bearing fault diagnosis. The experimental results indicate that, the new method successfully captures three fault characteristic frequencies ($1f_i$, $2f_i$, and $3f_i$), with their energy distribution concentrated in the range of 78.9 Hz to 228.7 Hz, indicative of inner race faults. Similarly, when comparing the different results with each other, the denoised source signal spectrum successfully captures the frequencies $1f_o$, $2f_o$, and $3f_o$ and their sideband components, which are characteristic of outer race faults. The sideband components generated in the above spectra are preliminarily judged to be caused by impacts between the fault location and nearby components, resulting in modulated frequency bands where the modulation frequency corresponds to the rotational frequency and its harmonics. Experiments show that the method can effectively diagnose the bearing faults. Full article

This paper presents a new procedure for the real-time processing and analysis of data from thermoacoustic systems. The approach focuses on continuously acquiring and adjusting measurements of acoustic wave pressure, enabling the instantaneous estimation of acoustic power. This is crucial for real-time control and decision-making, especially in applications that require rapid power estimation, such as the control loop implementation in thermoacoustic engines, where conditions are constantly changing and dynamic adaptation is essential. Two methods for estimating the power delivered to the load are proposed: (method 1) instantaneous power evaluation, which calculates the power consumed by the resistance in the resistance–capacitance (RC) load, and (method 2) one-period average power calculation using the well-established two-microphones method. These methods are validated with both different synthetic signals and experimental measurements. The results reveal that the new method provides real-time accurate estimations of the power delivered to the acoustic load and, thus, has shown potential for control-based applications. Full article

The health status of rotating machinery equipment in nuclear power plants is of paramount importance for ensuring the overall normal operation of the power plant system. In particular, significant failures in large rotating machinery equipment, such as main pumps, pose critical safety hazards to the system. Therefore, this paper takes pump equipment as a representative of rotating machinery in nuclear power plants and proposes a fault diagnosis method based on a multi-scale convolutional self-attention network for three types of faults: outer ring fracture, inner ring fracture, and rolling element pitting corrosion. Within the multi-scale convolutional self-attention network, a multi-scale hybrid feature complementarity mechanism is introduced. This mechanism leverages an adaptive encoder to capture deep feature information from the acoustic signals of rolling bearings and constructs a hybrid-scale feature set based on deep features and original signal characteristics in the time–frequency domain. This approach enriches the fault information present in the feature set and establishes a nonlinear mapping relationship between fault features and rolling bearing faults. The results demonstrate that, without significantly increasing model complexity or the volume of feature data, this method achieves a substantial increase in fault diagnosis accuracy, exceeding 99.5% under both vibration signal and acoustic signal conditions. 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

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

This study addresses the challenge of predicting acoustic source strength in freshwater environments, focusing on frequencies between 100–400 kHz. Acoustic signal attenuation is inherently frequency-dependent and influenced by water properties as well as the total propagation path of the acoustic wave, complicating the accurate determination of source strength. To address this challenge, we developed a non-linear regression model for solving the inverse problem of attenuation correction in reflected signals from typical aquatic reflectors, addressing the current absence of robust correction tools in this frequency range. The novelty of our approach lies in designing a non-linear regression framework that incorporates key physical parameters—signal energy, propagation distance, and frequency—enabling accurate source strength prediction. Using an experimental setup comprising ultrasonic transducers and a signal generator under controlled conditions, we collected a comprehensive dataset of 366 samples. The results demonstrate that our proposed model achieves reliable source strength prediction by simplifying Thorpe’s equation for freshwater environments. This research represents a significant advancement in underwater acoustics, providing a practical and reliable tool for source strength estimation in freshwater systems. The developed methodology may have broad applications across sonar technology, environmental monitoring, and aquatic research domains. Full article

Bladed components are essential in engines and propulsion systems, but their thin structures, complex geometries, and significant material removal rates during machining make them challenging to manufacture. This study investigates the chatter phenomenon in blade whirling milling, a promising method for improving machining efficiency. Multi-sensor signals, including vibration and acoustic emission signals, are collected during roughing and finishing machining. Time-domain, frequency-domain, and time-frequency features are extracted, filtered, and fused using principal component analysis (PCA) to retain relevant information while ensuring computational efficiency. The features are then input into an MLGRU-based chatter recognition model, incorporating a self-attention mechanism (SAM) for enhanced performance. The experimental results show that the proposed model achieves an average recognition accuracy of 89.16%, with a response time within 0.4 s, reflecting its effectiveness and timeliness in chatter detection. The findings also validate that the blade edge regions are more prone to chatter, especially during rough machining, due to their lower rigidity and greater sensitivity to external excitations. 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

This paper presents a method of sound source separation in live audio signals, based on sound intensity analysis. Sound pressure signals recorded with an acoustic vector sensor are analyzed, and the spectral distribution of sound intensity in two dimensions is calculated. Spectral components of the analyzed signal are selected based on the calculated source direction, which leads to a spatial filtration of the sound. The experiments were performed with test signals convolved with impulse responses of a real sensor, recorded for a varying sound source position. The experiments evaluated the proposed method’s ability to separate sound sources, depending on their position, spectral content, and signal-to-noise ratio, especially when multiple sources are active at the same time. The obtained results are presented and discussed. The proposed algorithm provided signal-to-distortion ratio (SDR) values 10–12 dB, and Short-Time Objective Intelligibility Measure (STOI) values in the range 0.86–0.94, an increase by 0.15–0.30 compared with the unprocessed speech signal. The proposed method is intended for applications in automated speech recognition systems, speaker diarization, and separation in the concurrent speech scenarios, using a small acoustic sensor. Full article

(1) Background: With technological advancements, the integration of wireless sensing and artificial intelligence (AI) has significant potential for real-time monitoring and intervention. Wireless sensing devices have been applied to various medical areas for early diagnosis, monitoring, and treatment response. This review focuses on the latest advancements in wireless, AI-incorporated methods applied to clinical medicine. (2) Methods: We conducted a comprehensive search in PubMed, IEEEXplore, Embase, and Scopus for articles that describe AI-incorporated wireless sensing devices for clinical applications. We analyzed the strengths and limitations within their respective medical domains, highlighting the value of wireless sensing in precision medicine, and synthesized the literature to provide areas for future work. (3) Results: We identified 10,691 articles and selected 34 that met our inclusion criteria, focusing on real-world validation of wireless sensing. The findings indicate that these technologies demonstrate significant potential in improving diagnosis, treatment monitoring, and disease prevention. Notably, the use of acoustic signals, channel state information, and radar emerged as leading techniques, showing promising results in detecting physiological changes without invasive procedures. (4) Conclusions: This review highlights the role of wireless sensing in clinical care and suggests a growing trend towards integrating these technologies into routine healthcare, particularly patient monitoring and diagnostic support. Full article

Artificial intelligence (AI) models have demonstrated their capabilities across various fields by performing tasks that are currently handled by humans. However, the training of these models faces several limitations, such as the need for sufficient data. This study proposes the use of acoustic signals as training data as this method offers a simpler way to obtain a large dataset compared to traditional approaches. Acoustic signals contain valuable information about the process behavior. We investigated the ability of extracting useful features from acoustic data expecting to predict labels separately by a multilabel classifier rather than as a multiclass classifier. This study focuses on electrical discharge turning (EDT) as a hybrid process of electrical discharge machining (EDM) and turning, an intricate process with multiple influencing parameters. The sounds generated during EDT were recorded and used as training data. The sounds underwent preprocessing to examine the effects of the parameters used for feature extraction prior to feeding the data into the ANN model. The parameters investigated included sample rate, length of the FFT window, hop length, and the number of mel-frequency cepstral coefficients (MFCC). The study aimed to determine the optimal preprocessing parameters considering the highest precision, recall, and F1 scores. The results revealed that instead of using the default set values in the python packages, it is necessary to investigate the preprocessing parameters to find the optimal values for the maximum classification performance. The promising results of the multi-label classification model depicted that it is possible to detect various aspects of a process simultaneously receiving single data, which is very beneficial in monitoring. The results also indicated that the highest prediction scores could be achieved by setting the sample rate, length of the FFT window, hop length, and number of MFCC to 4500 Hz, 1024, 256, and 80, respectively. Full article

Hydrophones used in Passive Acoustic Monitoring generate vast amounts of data, with the storage requirements for raw signals dependent on the sampling frequency, which limits the range of frequencies that can be recorded. Since the installation of these observatories is costly, it is crucial to maximize the utility of high-sampling-rate recordings to expand the range of survey types. However, storing these large datasets for long-term trend analysis presents significant challenges. This paper proposes an approach that reduces the data storage requirements by up to 85% while preserving critical information about Power Spectral Density and Sound Pressure Level. The strategy involves generating these key metrics from spectrograms, enabling both short-term (micro) and long-term (macro) studies. A proposal for efficient data processing is presented, structured in three steps: the first focuses on generating key metrics to replace space-consuming raw signals, the second addresses the treatment of these metrics for long-term studies, and the third outlines the creation of event detectors from the processed metrics. A comprehensive overview of the essential features for analyzing acoustic signals is provided, along with considerations for the future design of marine observatories. The necessary calculations and processes are detailed, demonstrating the potential of these methods to address the current data storage and processing limitations in long-term acoustic monitoring. Full article

An investigation of a non-invasive method to detect defects and localize excitations in metallic structures is presented. It is shown how signals generated by very sensitive piezo sensor assemblies, secured to the metallic elements, can allow for space localization of excitations and defects in the analyzed structures. The origin of the piezo excitations are acoustic modes generated by light percussive excitations whose strength is of the order of tenths of a newton and that provide piezo signal amplitudes of a few hundred millivolts. Tests of the detection scheme of the excitations are performed on steel ropes, iron pipes, and bars with lengths in the range of 1–6 m with the sensor output signal shaped in the form of a clean pulse. It is shown that the signals generated by the piezo assemblies, when adequately shaped, can feed the input of an RF transmitter, which in turn transfers information to a remote receiver whose readout allows for remotely analyzing information collected on the metallic elements. Considering the voltage amplitude of the signals (of the order of 300 mV) generated by the piezo sensors as a result of very light percussive excitations, the low power required for transmitting data, and the low cost of the sensing and transmitting assembly, it is conceivable that our devices could detect excitations generated even tens of kilometers away and allow for setting up an array of sensors for controlling in real time the status of pipe networks. Full article