Acoustic Signals Research Articles

Application of Acoustic Emission Technology for Bearing Condition Monitoring on Passenger Ropeway

Rolling bearings are important stressed components of driving wheels and detour wheels on passenger ropeway, and their failure may lead to accidents and casualties. After the ropeway is installed and put into operation, the rolling bearings in the wheels are difficult to disassemble. Once disassembled, the bearing may be damaged. Due to the installation position and the complex operation environment, the health status of the rolling bearings is a blind spot for ropeway inspection and detection. Acoustic emission detection technology is used to monitor the rolling bearings of passenger ropeways. After a large number of field tests, the acoustic emission monitoring program and signal analysis method of rolling bearing on ropeway were proposed. The parameter characteristics of acoustic emission signals obtained in bearing operating were analyzed from two aspects: the ropeway operating cycle and the bearing operating cycle. Spectrum analysis of typical signals was performed, and the spectrum characteristics of typical acoustic emission signals of rolling bearing during the operation of the ropeway were obtained. On-site tests results show that using acoustic emission technology to perform condition monitoring and fault diagnosis of low-speed rolling bearings on passenger ropeways has good application prospects.

Analysis of the Repeatability of the Pencil Lead Break in Comparison to the Ball Impact and Electromagnetic Body-Noise Actuator

Acoustic emission testing is used to monitor the structural health by recording and analyzing sound signals originating from within a structure. Usually, sensors are attached at key positions on a structure under test to record and analyze these acoustic signals. For reliable measurements, the coupling of these sensors has to be assessed. For this purpose, artificial sound sources are commonly used which induce signals in the form of elastic waves into a structure allowing to approximately assess the frequency response function of the sensor coupling. To assess the coupling of sensors reliably by means of an artificial sound source, it has to exhibit high repeatability, i.e. small relative standard deviation (RSTD) with respect to some characteristic of interest. In this work, the repeatability of the pencil lead break, ball impact and an electromagnetic body-noise actuator is assessed across several repetitions. Through controlled and repeated excitation of the respective artificial sound sources on a concrete girder, the RSTD of the magnitude spectra of the artificial sound sources across frequency is assessed in the frequency range of about 0 kHz to 650 kHz. The ball impact and the actuator exhibited a RSTD of below 5 % of their magnitude spectra for frequencies below about 20 kHz, the maximum frequency for the actuator, whereas for the pencil lead break a RSTD of above 11 % was observed across the entire frequency range. An increase of the RSTD with increasing frequency was observed for the ball impact and the pencil lead break. The repeatability of the pencil lead break, for which a RSTD between 15 % to 40 % was observed for a broad frequency range of its amplitude spectrum, usually achieved a lower relative standard deviation than the ball impacts with 4 mm and 5 mm balls above a frequency of about 60 kHz. A significant increase of the relative standard deviation for the 4 mm and 5 mm ball was observed starting at about 20 kHz. Above about 60 kHz, the relative standard deviation of the 5 mm ball impact had risen to 30 % and more oscillating between about 30 - 50 %. At 200 kHz and more all signals were attenuated to background noise level by the concrete girder. It is concluded, that the pencil lead break is inferior with respect to repeatability below 40 kHz compared to the 4 mm and 5 mm ball impact and electromagnetic body-noise actuator, but performs equally well or better above 60 kHz. The repeatability of the investigated artificial sound sources depends on the investigated frequency range with no unique best. The most important finding is the increasing relative standard deviation of the ball impact with increasing frequency. As a result, the repeatability of the ball impact is questionable and should be investigated before using it in high precision experiments.

Comparative Analysis of Acoustic Emission Signals from On-Load Tap-Changers for Potential Detecting of Non-Simultaneous Operations

The research reported in this paper deals with the potential of detecting non-simultaneous operation in on-load tap-changer (OLTC) using an acoustic emission method. Tests conducted under laboratory conditions were carried out using an OLTC model. Three transducers with different characteristics were used: WD 17 AH, D9241A, and R15α, alongside oscillography as the reference method. The use of two new descriptors in the time domain was proposed. The feasibility of detecting the defect with different piezoelectric transducers was investigated. As a result of the analysis of the results, it was found that each piezoelectric transducer can identify nonsimultaneous operation of the switch. The most significant changes in descriptor values occurred in the time domain, and the most effective transducer turned out to be R15α.

The human auditory system uses amplitude modulation to distinguish music from speech.

Music and speech are complex and distinct auditory signals that are both foundational to the human experience. The mechanisms underpinning each domain are widely investigated. However, what perceptual mechanism transforms a sound into music or speech and how basic acoustic information is required to distinguish between them remain open questions. Here, we hypothesized that a sound's amplitude modulation (AM), an essential temporal acoustic feature driving the auditory system across processing levels, is critical for distinguishing music and speech. Specifically, in contrast to paradigms using naturalistic acoustic signals (that can be challenging to interpret), we used a noise-probing approach to untangle the auditory mechanism: If AM rate and regularity are critical for perceptually distinguishing music and speech, judging artificially noise-synthesized ambiguous audio signals should align with their AM parameters. Across 4 experiments (N = 335), signals with a higher peak AM frequency tend to be judged as speech, lower as music. Interestingly, this principle is consistently used by all listeners for speech judgments, but only by musically sophisticated listeners for music. In addition, signals with more regular AM are judged as music over speech, and this feature is more critical for music judgment, regardless of musical sophistication. The data suggest that the auditory system can rely on a low-level acoustic property as basic as AM to distinguish music from speech, a simple principle that provokes both neurophysiological and evolutionary experiments and speculations.

Acoustic emission noise reduction: A case of a uniaxial compression test of gypsum-like rock

In acoustic emission tests, the effective signal generated by crack initiation and propagation inside a sample is easily affected by the testing machine, earth vibration and other external noise, which increases the difficulty of data processing and analysis. To solve this problem, a gypsum sample under uniaxial compression is taken as an example to analyze and process the acoustic emission signal during the experiment. The test results show that these noises generally have the characteristics of acoustic sources far from the probe and signal types different from the effective signal. Therefore, a series of methods concerning the noise reduction of acoustic emission signals, including acoustic emission signal preprocessing, acoustic source distance denoising, and acoustic source type denoising, is proposed. Through three analyses and treatments of acoustic emission parameters, noise reduction was achieved with good results. According to the principles of these methods, some suggestions have been put forward for the position of probes in acoustic emission tests; that is, the connection of two probes should not be perpendicular to a weak plane (bedding, joints, etc.). These results could provide a theoretical reference for the postprocessing of acoustic emission signals in mechanical tests.

Laser-sound reproduction by pulse amplitude modulation audio streams

Recently, the possibility to reproduce complex continuous acoustic signals via pulsed laser-plasma sound sources was demonstrated. This was achieved by optoacoustic transduction of dense laser pulse trains, modulated via single- or multi-bit Sigma–Delta, in the air or on solid targets. In this work, we extend the laser-sound concept to amplitude modulation techniques. Particularly, we demonstrate the possibility of transcoding audio streams directly into acoustic pulse streams by analog pulsed amplitude modulation. For this purpose, an electro-optic modulator is used to achieve pulse-to-pulse amplitude modulation of the laser radiation, similarly to the multi-level Sigma–Delta method. The modulator is directly driven by the analog input stream through an audio interface. The performance of the system is evaluated at a proof-of-principle level for the reproduction of test audio signals such as single tones, double tones and sine sweeps, within a limited frequency range of the audible spectrum. The results are supported by computational simulations of the reproduced acoustic signals using a linear convolution model that takes as input the audio signal and the laser-generated acoustic pulse profile. The study shows that amplitude modulation allows for significant relaxation of the laser repetition rate requirements compared to the Sigma–Delta-based implementation, albeit at the potential cost of increased distortion of the reproduced sound signal. The nature of the distortions is analyzed and a preliminary experimental and computational investigation for their suppression is presented.

Ship-Radiated Noise Separation in Underwater Acoustic Environments Using a Deep Time-Domain Network

Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditionally, underwater acoustic signal separation has employed blind source separation methods based on independent component analysis. Recently, the separation of underwater acoustic signals has been approached as a deep learning problem. This involves learning the features of ship-radiated noise from training data. This paper introduces a deep time-domain network for ship-radiated noise separation by leveraging the power of parallel dilated convolution and group convolution. The separation layer employs parallel dilated convolution operations with varying expansion factors to better extract low-frequency features from the signal envelope while preserving detailed information. Additionally, we use group convolution to reduce the expansion of network size caused by parallel convolution operations, enabling the network to maintain a smaller size and computational complexity while achieving good separation performance. The proposed approach is demonstrated to be superior to the other common networks in the DeepShip dataset through comprehensive comparisons.

Detection and Analysis of Abnormal High-Current Discharge of Cylindrical Lithium-Ion Battery Based on Acoustic Characteristics Research

The improvement of battery management systems (BMSs) requires the incorporation of advanced battery status detection technologies to facilitate early warnings of abnormal conditions. In this study, acoustic data from batteries under two discharge rates, 0.5 C and 3 C, were collected using a specially designed battery acoustic test system. By analyzing selected acoustic parameters in the time domain, the acoustic signals exhibited noticeable differences with the change in discharge current, highlighting the potential of acoustic signals for current anomaly detection. In the frequency domain analysis, distinct variations in the frequency domain parameters of the acoustic response signal were observed at different discharge currents. The identification of acoustic characteristic parameters demonstrates a robust capability to detect short-term high-current discharges, which reflects the sensitivity of the battery’s internal structure to varying operational stresses. Acoustic emission (AE) technology, coupled with electrode measurements, effectively tracks unusually high discharge currents. The acoustic signals show a clear correlation with discharge currents, indicating that selecting key acoustic parameters can reveal the battery structure’s response to high currents. This approach could serve as a crucial diagnostic tool for identifying battery abnormalities.

Recognition Algorithm of AE Signal of Rock Fracture Based on Multiscale 1DCNN-BLSTM

Acoustic emission (AE) signals produced by different types of rocks have different characteristics of information. Determining the brittle mineral content of rock according to the acoustic emission characteristics of rock is helpful to understand the mechanical behavior of rock in field monitoring. This article constructs a deep learning algorithm model to identify acoustic emission signals released from rock fractures with different brittle mineral contents. In response to the interference characteristics of acoustic emission signal data, a multiscale one-dimensional convolutional neural network embedded with efficient channel attention (ECA) module was incorporated into the model, and multiscale convolutional kernels were used to extract features of different levels of precision. In the latter half of the model, the BLSTM network was incorporated to extract time series-related features, local spatial uncorrelated features, and weak periodic pattern features from the acoustic emission signal data. To solve the problem that the recognition accuracy of minority samples decreases, this study replaces ReLU activation function with SELU. The results show that the multiscale 1DCNN-BLSTM model embedded in ECA module has a good antinoise performance, and the recognition accuracy can reach over 90%. The discovery of this work provides a new idea for exploring the mechanism of rock mass instability.

Multi-Sensor Fusion Approach to Drinking Activity Identification for Improving Fluid Intake Monitoring

People nowadays often ignore the importance of proper hydration. Water is indispensable to the human body’s function, including maintaining normal temperature, getting rid of wastes and preventing kidney damage. Once the fluid intake is lower than the consumption, it is difficult to metabolize waste. Furthermore, insufficient fluid intake can also cause headaches, dizziness and fatigue. Fluid intake monitoring plays an important role in preventing dehydration. In this study, we propose a multimodal approach to drinking activity identification to improve fluid intake monitoring. The movement signals of the wrist and container, as well as acoustic signals of swallowing, are acquired. After pre-processing and feature extraction, typical machine learning algorithms are used to determine whether each sliding window is a drinking activity. Next, the recognition performance of the single-modal and multimodal methods is compared through the event-based and sample-based evaluation. In sample-based evaluation, the proposed multi-sensor fusion approach performs better on support vector machine and extreme gradient boosting and achieves 83.7% and 83.9% F1-score, respectively. Similarly, the proposed method in the event-based evaluation achieves the best F1-score of 96.5% on the support vector machine. The results demonstrate that the multimodal approach performs better than the single-modal in drinking activity identification.

Investigating electromagnetic acoustic emission signals denoising for alloy materials non-destructive detecting: a CRQA method

Aiming at the problem that signals collected from local electromagnetic loading operations are usually mixed with background noises (especially white noise), this paper proposed an electromagnetic acoustic emission signal denoising technology based on cross recurrence quantification analysis (CRQA). Firstly, the decomposition layer and penalty factor of variational mode decomposition (VMD) are set by experience or optimization algorithm, and then the original signal is decomposed. Secondly, the main components are selected by the CRQA algorithm, and the electromagnetic acoustic emission signal after denoising is obtained by superposition reconstruction. The simulation and experimental results show that when 5dB noise is added, CRQA can effectively remove the background noises in electromagnetic acoustic emission signals compared to the correlation coefficient algorithm, and it can assist in realizing the high-precision non-destructive testing of alloy materials.

Acoustic analysis of marine vibrators based on LES/FW-H model

In the process of marine acoustic data processing, the information on acoustic traces is acquired by correlating the control signals of marine vibrators with the acoustic data in the seawater. Since the signals controlled by marine vibrators are fixed, the resolution of marine profiles is highly dependent on acoustic signals. However, conventional fluid models have lower accuracy in measuring (or predicting) the waveform, resulting in significant differences between the data received at sea and the actual designed signal. To avoid this, a hybrid method based on Ffowcs Williams and Hawkings (FW-H) equations with the large eddy simulation (LES) is proposed to simulate the process of interactions between marine vibrators and seawater, and ultimately predict the acoustic waveform. Experiment results demonstrate the method discussed in this paper is in accord with the experimental data and provides a powerful tool for the development and design of marine vibrators.

CFD Analysis of Drone Blade

Drone technology is seen to be rapidly advancing in various fields and applications including photography, military, transportation, sports, and many more. Therefore, each drone design requires different aerodynamic requirements, which includes different types of propeller designs. By revolving and generating airflow, the propellers give drones or unmanned aerial vehicles (UAV) a lift force or thrust. This paper presents a novel integrated study of the aerodynamic performance and acoustic signature of different propellers with a specific focus on the blade twist angle effect. Designed using CATIA V5 and computational fluid dynamic (CFD) simulations were utilized to examine and compare the aerodynamic performance, Drag and Lift between different shapes of the drone propellers. Therefore, this work falls on the study of the aerodynamic effect of different drone blades

Solution to the cocktail party problem: A time-reversal active metasurface for multipoint focusing

The cocktail party effect is the capability to focus one’s auditory attention on particular audio sources while ignoring other audio sources. We propose an experimental strategy to reproduce this ability by designing a time-dependent metasurface composed of independent active mirrors. Each active mirror is a programmable acoustic unit cell capable of hearing, computing, and re-emitting acoustic signals: each of them acts as a convolution filter. The proper configuration of the metasurface temporal filters allows one to establish an acoustic communication link between groups of individuals immersed in the noisy environment: a multiple-user multiple-input, multiple-output acoustic system is built. Published by the American Physical Society 2024

The relative contribution of acoustic signals versus movement cues in group coordination and collective decision-making.

To benefit from group living, individuals need to maintain cohesion and coordinate their activities. Effective communication thus becomes critical, facilitating rapid coordination of behaviours and reducing consensus costs when group members have differing needs and information. In many bird and mammal species, collective decisions rely on acoustic signals in some contexts but on movement cues in others. Yet, to date, there is no clear conceptual framework that predicts when decisions should evolve to be based on acoustic signals versus movement cues. Here, we first review how acoustic signals and movement cues are used for coordinating activities. We then outline how information masking, discrimination ability (Weber's Law) and encoding limitations, as well as trade-offs between these, can identify which types of collective behaviours likely rely on acoustic signals or movement cues. Specifically, our framework proposes that behaviours involving the timing of events or expression of specific actions should rely more on acoustic signals, whereas decisions involving complex choices with multiple options (e.g. direction and destination) should generally use movement cues because sounds are more vulnerable to information masking and Weber's Law effects. We then discuss potential future avenues of enquiry, including multimodal communication and collective decision-making by mixed-species animal groups. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamic'.

Exploring the role of vocalizations in regulating group dynamics.

Because of the diverging needs of individuals, group life can lead to disputes and competition, but it also has many advantages, such as reduced predation risk, information sharing and increased hunting success. Social animals have to maintain group cohesion and need to synchronize activities, such as foraging, resting, social interactions and movements, in order to thrive in groups. Acoustic signals are highly relevant for social dynamics, some because they are long-ranging and others because they are short-ranging, which may serve important within-group functions. However, although there has been an increase in studies concentrating on acoustic communication within groups in the past decade, many aspects of how vocalizations relate to group dynamics are still poorly understood. The aim of this review is to present an overview of our current knowledge on the role of vocalizations in regulating social group dynamics, identify knowledge gaps and recommend potential future research directions. We review the role that vocalizations play in (i) collective movement, (ii) separation risk and cohesion maintenance, (iii) fission-fusion dynamics, and (iv) social networks. We recommend that future studies aim to increase the diversity of studied species and strengthen the integration of state-of-the-art tools to study social dynamics and acoustic signals. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

The ecology of zebra finch song and its implications for vocal communication in multi-level societies.

Acoustic signalling is crucial in affecting movements and in social interactions. In species with dynamic social structures, such as multi-level societies, acoustic signals can provide a key mechanism allowing individuals to identify and find or avoid each other and to exchange information. Yet, if the spacing between individuals regularly exceeds the maximum signalling range, the relation between movements and signals becomes more complex. As the best-studied songbird in captivity, the zebra finch (Taeniopygia castanotis) is a species with individually distinct songs that are audible over just a few metres and a widely ranging dynamic multi-level social organization in the wild, raising questions on the actual role of its song in social cohesion and coordination. Here, we provide an overview of birdsong in social organizations (networks) and use the ecology of the zebra finch and male song to discuss how singing can facilitate social cohesion and coordination in species where the signal range is very short. We raise the question of the extent to which zebra finches are a representative species to understand the function of song in communication, and we broaden current views on the function of birdsong and its individual signature. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Using machine learning techniques in inverse problems of acoustical oceanography

AbstractThe goal of the work presented here is to study a novel approach for inverting acoustic signals recorded in the marine environment for the estimation of environmental parameters of the water column and/or the seabed. The proposed approach is based on signal feature extraction using a discrete wavelet packet transform, applied to the measured signal, and hidden Markov models that exploit the sequential patterns of the signals. The signal feature is thereafter used in the framework of a mixture density network, which, after training with sets of simulated signals calculated within a predefined search space, provides conditional posterior distributions of the recoverable parameters. The technique is tested with two test cases corresponding to different types of inverse problems. The first case corresponds to a simple problem of geoacoustic inversion, while the second is referred to a, rather unusual, still interesting problem of recovering the shape of a seamount using long‐range acoustic data. Both test cases are based on simulated experiments. The inversion results obtained using the proposed scheme are compared with inversion results using statistical features of the acoustic signal, which is another inversion approach well documented in the literature and is also based on the wavelet packet transform of the measured signal.

Not your private tête-à-tête: leveraging the power of higher-order networks to study animal communication.

Animal communication is frequently studied with conventional network representations that link pairs of individuals who interact, for example, through vocalization. However, acoustic signals often have multiple simultaneous receivers, or receivers integrate information from multiple signallers, meaning these interactions are not dyadic. Additionally, non-dyadic social structures often shape an individual's behavioural response to vocal communication. Recently, major advances have been made in the study of these non-dyadic, higher-order networks (e.g. hypergraphs and simplicial complexes). Here, we show how these approaches can provide new insights into vocal communication through three case studies that illustrate how higher-order network models can: (i) alter predictions made about the outcome of vocally coordinated group departures; (ii) generate different patterns of song synchronization from models that only include dyadic interactions; and (iii) inform models of cultural evolution of vocal communication. Together, our examples highlight the potential power of higher-order networks to study animal vocal communication. We then build on our case studies to identify key challenges in applying higher-order network approaches in this context and outline important research questions that these techniques could help answer. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Where's Whaledo: A software toolkit for array localization of animal vocalizations.

Where's Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of Ziphius cavirostris. We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins.