Acoustic Method Research Articles

Influence of start-up modes on the noise characteristics of mixed-flow pump during start-up process

The evolutions of noise characteristics of the pump have significant implications for monitoring the stability of the mixed-flow pump (MFP) start-up process. To investigate the variations in noise characteristics under different start-up modes and start-up times, the noise, shaft vibration and pressure pulsation of MFP under linear and nonlinear start-up modes were collected by an acoustic and vibration experimental system. Subsequently, an analysis was conducted based on acoustic signal processing methods. The results show that the high-amplitude sound pressure under different start-up modes appears in the later stage of start-up, and the time for the high-amplitude noise region to appear is the shortest under the concave exponential (CCEF) start-up mode. The shock noise under the CCEF start-up mode is relatively small, indicating that this start-up mode is conducive to avoiding severe mechanical collisions and shocks during the start-up process. The highest A-weighted sound pressure level (SPL) of the noise under different start-up modes appears at the center frequency of 250 Hz, and the CCEF start-up mode brings about the smallest total effective SPL. Adopting the CCEF start-up mode and extending the start-up time is beneficial to avoid severe mechanical collisions, reduce the SPL in each frequency band, and weaken the continuous impact of noise on the environment during the MFP start-up process. In addition, CCEF start-up modes can effectively reduce the tone of the noise during the start-up of the MFP.

High-resolution acoustic imaging method based on equivalent source method and reweighted l1 minimization

Sparse regularization has been successfully applied to equivalent source method (ESM) in order to improve the acoustic imaging resolution. However, the application is not always feasible, especially at low frequencies. To overcome the problem, this paper proposes a high-resolution acoustic imaging method. In this method, reweighted l1 minimization is introduced to ESM to deal with the ill-posed inverse problems. Then the obtained equivalent source strengths are used to locate the sound sources. Compared to the sparse regularization-based ESM, the proposed method can provide a low side lobe and higher spatial resolution of acoustic imaging. Meanwhile, by arranging equivalent sources in three-dimensional space, the proposed method can also realize the acoustic imaging in three-dimensional sound field with high resolution. The results of the simulation and experiment demonstrate the validations.

Analysis of Deep Generative Model Impact on Feature Extraction and Dimension Reduction for Short Utterance Text-Independent Speaker Verification

AbstractSpeaker verification is a biometric-based method for individual authentication. However, there are still several challenging problems in achieving high performance in short utterance text-independent conditions, maybe for weak speaker-specific features. Recently, deep learning algorithms have been used extensively in speech processing. This manuscript uses a deep belief network (DBN) as a deep generative method for feature extraction in speaker verification systems. This study aims to show the impact of using the proposed method in various challenging issues, including short utterances, text independence, language variation, and large-scale speaker verification. The proposed DBN uses MFCC as input and tries to extract more efficient features. This new representation of speaker information is evaluated in two popular speaker verification systems: GMM-UBM and i-vector-PLDA methods. The results show that, for the i-vector-PLDA system, the proposed feature decreases the EER considerably from 15.24 to 10.97%. In another experiment, DBN is used to reduce feature dimension and achieves significant results in decreasing computational time and increasing system response speed. In a case study, all the evaluations are performed for 1270 speakers of the NIST SRE2008 dataset. We show deep belief networks can be used in state-of-the-art acoustic modeling methods and more challenging datasets.

Combined acoustic methods in monitoring the crack development in granite

Detecting cracks of various sizes is crucial for monitoring and predicting rock failure. This study combines two acoustic methods—the passive acoustic emission (AE) and the active ultrasonic P-wave velocity to investigate the cracking process from initiation to propagation and coalescence of rocks containing pre-existing flaws, which simulate the intrinsic defects in natural rocks or rock masses, across different scales in three-dimensional space. The resultant AE activity is found sensitive to microcracks even in the early loading stage and can be divided into three stages: quiet, stable, and high growth periods. The positions of the microcracks can be obtained with the assistance of several AE sensors. It is also found that the P-wave velocity attenuation is more sensitive to macrocracks but less sensitive to microcracks. The results indicate that combining the AE and active ultrasonic-wave methods can provide comprehensive information to evaluate the damage levels and localize the internal cracks with multiple scales in the entire cracking process of rocks. The study also indicates the potential for predicting crack coalescence and failure based on the combination of these two nondestructive methods. In addition, the findings can also be applicable in the monitoring and prediction of failure in other brittle materials.

Target strength measurements of yellowfin tuna (Thunnus albacares) and acoustic discrimination of three tropical tuna species

Abstract Tropical tuna fisheries support some of the largest artisanal and industrial fisheries worldwide. Approximately 37% of the tropical tuna catch by industrial purse seiners is obtained from tuna associated with drifting fish aggregating devices (DFADs), where three tuna species coexist: skipjack (Katsuwonus pelamis), bigeye (Thunnus obesus), and yellowfin tuna (Thunnus albacares), with stocks subject to different health status. Purse seine fishers heavily rely on acoustic technology to detect and assess the quantity of tuna at DFADs. Yet, accurately distinguishing between species using solely acoustic methods is limited by insufficient knowledge about each species' acoustic response across frequencies. This study was carried out on six swimbladdered individuals belonging to two sets with mean lengths of 51.9 ± 9.5 and 52.9 ± 2 cm. It focuses on the frequency response obtained from ex situ measurements of yellowfin tuna recorded at 38, 70, 120, and 200 kHz, which revealed a flat response across frequencies, with b20 values of −72.4 ± 9, −73.2 ± 8, -72.3 ± 8, and −72.3 ± 9 dB, respectively. These results, contrast with previous findings on bigeye and skipjack, demonstrating the discrimination potential of acoustics in these three species. To harness this potential, a discrimination algorithm was developed.

Iron ore grade's impact on uniaxial compression behavior and acoustic emission characteristics

The mechanical properties of iron ore are influenced by various factors, including its grade, which consequently impacts stope stability and equipment efficiency in the mining process. In order to explore the influence and mechanism of grade on these properties, handheld XRF testing was conducted to determine the grade of iron ore samples ranging from 28% to 40%. Subsequently, the acoustic emission method was employed to acquire signals during the uniaxial compression tests. Additionally, scanning electron microscopy was utilized to examine the fracture surface, for performing a microscopic analysis of the influence mechanism of grade on the mechanical properties of iron ore. The results revealed that lower-grade iron ore exhibited larger transverse deformation, with a corresponding dominance of tensile failure. This observation aligned with the AF-RA value (Parameters of reaction crack type), which depicted a shift in the types of microcracks generated during the compression process from tensile cracks to shear cracks with the increasing grade. The content of gangue minerals diminishing as the grade increased. Consequently, the location of the cracks transitioned from the primary failure Through the gangue minerals to the primary microcracks in metallic minerals, resulting in a higher occurrence of acoustic emission events during the early loading stage and the development speed of microcracks accelerates during the loading process. The dominant frequency (Parameters of reaction crack size) of the acoustic emission signal increased from 150 kHz at a grade of 28% to 264 kHz at a grade of 40%, accompanied by a linear decrease in the proportion of low-frequency signals. The b value (indicator for quantifying the degree of damage) demonstrates a decrease with increasing grade. Overall, the content of metal minerals in the iron ore increased with the grade, resulting in polymerization. However, the influence of crack propagation on the overall strength of iron ore remained relatively minor. Macroscopically, the uniaxial compressive strength increased almost linearly with the iron ore grade.

An acoustic emission identification model for train axle fatigue cracks based on Deep Belief Network

Abstract Aiming to effectively identify train axle fatigue cracks, some scholars are now trying to consider introducing acoustic emission detection technology into axle health monitoring and combining it with intelligent neural networks to achieve better monitoring results. In the field of axle fatigue crack acoustic emission identification, the commonly used methods include parameter analysis, wavelet analysis, and traditional artificial neural networks. Though these methods did work in the field of axle fatigue crack identification, they still have two deficiencies. （1）The features are manually extracted depending on large artificial experience. (2) The methods mentioned above have shallow architectures, which cannot handle some complex fatigue crack linear relationships. In this paper, an acoustic emission signal analysis method for axle fatigue cracks based on deep belief networks is proposed to overcome the aforementioned deficiencies. The main purpose of this paper is to emphasize the feasibility of the deep belief network to intelligently identify fatigue crack signals from different types of acoustic emission signals of axles and to classify crack signals at different stages. The research method is to use acoustic emission (AE) technology to obtain the axle fatigue crack AE signal, pretreat the collected time-domain signal, identify whether cracks appear, and classify the stages of crack emergence by deep belief network (DBN) so as to find the axle crack in advance. The experimental analysis results show that the method of identification of axle fatigue cracks based on DBN not only can identify fatigue crack signals but also has a very high identification rate of fatigue cracks at different stages. In the axle fatigue crack acoustic emission identification field, it can be seen that the proposed method in this paper will be a promising approach.

Study on movement measurement for internal defects in concrete structures by noncontact acoustic inspection method using correlation processing

The noncontact acoustic inspection method using acoustic irradiation induced vibration and a scanning laser Doppler vibrometer can detect internal defects in the surface layer of concrete structures, etc, over long distances and without contact. Since this method uses flexural vibration in principle, it can be used as an alternative method to tapping inspection. Therefore, there has been an increase in requests for realizing movement measurement. Thus, an experiment was conducted to verify whether movement measurement was possible by mounting multiple LDVs and sound sources that did not have scanning mechanisms on a moving cart. The experimental results revealed that by using cross-correlation processing between the emitted and received waveforms, it is possible to perform movement measurements at low speeds of about 2 km h−1.

UOWC Integration with ROVs: A Fiber-Optic Approach for Enhanced Underwater Exploration

Underwater Optical Wireless Communication (UOWC) revolutionizes underwater exploration, leveraging seawater's unique property—reduced absorption of blue-green light. This offers superior advantages over traditional communication methods, providing higher bandwidth and lower latency. This paper explores the integration of UOWC with Remotely Operated Vehicles (ROVs), enhancing data exchange and command capabilities in challenging underwater environments. Seawater's unique properties empower UOWC to outperform acoustic and RF methods, crucial for real-time applications. The study utilizes advanced techniques like Fiber-Optic Communication, High Order Sliding Mode Control, and Hybrid Blockage Detection for ROV integration and control. While the abstract focuses on theory and design, it sets the stage for empirical validations, emphasizing the need for future research to quantify improvements in real-world underwater scenarios

Optical fiber-based acoustic intensity microphone for high-intensity airborne ultrasound measurement

Abstract The increasing use of airborne ultrasonic waves in daily life, driven by advances in parametric and phased arrays, has led to innovative applications like highly directional speakers, non-contact tactile feedback, 3D acoustic levitation, and medical therapies. These advancements necessitate accurate measurement of high-intensity ultrasonic waves, exceeding the capability of traditional microphones limited to around 160 dB , and highlight the growing importance of measuring the sound field not merely as scalar (sound pressure) but as vector (acoustic intensity) to accommodate future technological developments. This paper introduces an acoustic intensity microphone using optical fibers as probes to overcome these limitations. The proposed method replaces the two ordinary microphones used in the traditional acoustic intensity measurement method with thin optical fibers, minimizing sound field disturbance. Experimental validation and the structure of a practical acoustic intensity microphone are discussed, building upon foundational work presented at USE2023 with added verification and insights.

Concrete acoustic emission signal augmentation method based on generative adversarial networks

With the development of concrete structure health monitoring (SHM), the utilization of acoustic emission (AE) as an effective non-destructive testing method has attracted increasing attention among researchers. However, the acquisition of concrete AE signals in real-world scenarios is often limited, which poses challenges to the training as well as the practical application of deep learning (DL) models. To address this problem, a concrete acoustic emission signal augmentation method based on generative adversarial networks (GAN) is proposed in this paper. This method features a unique architecture where both the generator and discriminator are built upon a Bidirectional Long Short-Term Memory (BiLSTM) network. Additionally, the generator integrates an attention mechanism between the BiLSTM layers, enhancing its ability to capture the temporal dynamics of AE signals. To improve the local details of the generated signals, a temporal difference mean square error (TD-MSE) regularization is incorporated into the loss function. Experimental results demonstrate that the method not only substantially increases the amount of data available but also significantly enhances the model’s generalization capabilities and classification accuracy.

An acoustic impedance design method for tubular structures with broadband sound insulations and efficient air ventilation

In this research, we proposed an acoustic impedance-based design method to increase the soundproofing bandwidths and maintain efficient air ventilation for tubular structures consisting of hollow air-flowing channels. This research has great potential to provide design guidelines based on an acoustic impedance model and even/odd mode analysis for hollow-tube ventilated devices containing resonance and antiresonances. We introduced a thin ventilated structure with a subwavelength thickness of 0.107 λ where λ corresponds to the lowest frequency within the filtering bandwidth, a 19.4 % cross-section open for air passage, and a 10 dB fractional bandwidth of 110.2 % (i.e., 90 % sound energy is blocked), which is broader than that of a conventional Fano-like structure of 44.7 %. To experimentally verify the proposed approach, two prototypes, including lateral-coupled and longitudinal-coupled devices, were designed, fabricated with 3D printing, and experimentally characterized via a commercial-available impedance tube system. The simulations matched the measurements and demonstrated the 10 dB fractional bandwidth of 65 % and 91.5 % and ventilation efficiencies of 21.4 % and 33.4 %, respectively. The main contribution of this work is that the proposed approach can be adopted for lateral or longitudinal coupled ventilated structures with broadband sound insulations and lower the effects of resonance-induced sound transmissions. Besides, the proposed acoustic-impedance model can significantly save the required tremendous computational resources and time compared to FEM simulations. Moreover, the proposed lateral and longitudinal ventilated structures are expected to benefit broadband sound filtering and noise reduction for thin panel or tubular silencers

Petroleum System Analysis and Burial History of Middle Permian Source Rock in Turpan–Hami Basin, NW China

The pre-Jurassic in the north depression of the Tuha depression is the most favorable replacement strata to obtain new reserves in the Turpan–Hami Basin. (Pre-Jurassic, in this paper, refers to the Permian and Triassic.) The main source rocks are the Taodonggou Group, of which the burial history and hydrocarbon generation potential remain unconfirmed. The investigation of the burial and thermal history is vital for the basin analysis and hydrocarbon exploration. Therefore, in this paper, by using the acoustic time difference method, vitrinite reflectance method, stratigraphic trend method and PetroMod-1D software, the differential characteristics of denudation thickness, burial history and thermal evolution history of different tectonic units in different periods of Taibei Sag in the Turpan–Hami basin are studied, and their influence on the petroleum system is analyzed, and then the zones with exploration potential are optimized. The results show that the Taibei Sag has experienced multiple tectonic uplift events. The Late Indosinian movement has profound effects on the Taodonggou Group source rocks. The rather large uplift amplitude postpones the maturation of source rocks. In addition, the Turpan–Hami Basin is a typical cold basin. Therefore, the thermal maturity of the source rocks is relatively low, with respect to the relatively deep burial. The thermal histories of the different sub-sags in the study area are slightly differentiated from each other. The Taodonggou Group source rocks in the Taibei Sag generally became mature during the Mid–Late Jurassic epoch, except for those in the Central–Southern Shanbei sub-sag, represented by Well LT-1, which reached the mature stage during the Late Triassic epoch. The study area has well-developed reservoir rocks, and effective reservoir bodies are formed in the slope zone and near the Tainan Sag, due to the higher porosity and permeability of reservoir rocks. The statistics related to the faults and an analysis of the structural styles of oil reservoirs indicate that the structural slope and anticline of the Huobei, Lianbei and Shanbei sub-sags are favorable for increasing reserves and production of hydrocarbons.

Cyclic dynamic-static loading and acoustic emission signal monitoring method of coal in solid–gas coupling environment and its application

Cyclic dynamic-static loading and acoustic emission signal monitoring method of coal in solid–gas coupling environment and its application

Recent advances on the fabrication and application of sound absorption coating-based textile composites

Noise pollution has been considered as a serious threat to the health of humans. The application of sound absorption materials is an effective noise reduction approach. Fibrous materials exhibit the unique micro-structures including small diameter, large specific surface area, and high porosity, which has been widely developed for sound absorption. Recently, rapid growth has been observed in the coating treatment of fibrous based materials and composites for acoustic applications, which can be attributed to the advantages of a facile production process, cost-effectiveness, and good sound absorption capability. This review aims to provide a brief overview on different coating layers of fibrous materials for improved sound absorption properties. The details of various factors affecting the acoustic behavior of coating materials are described. Among them, polymer coating is a widely used acoustic treatment method, adding particles and nanofiber materials to the polymer matrix, and carrying out structural design, which can further improve the sound absorption performance of textile materials. According to the different needs of the application field, different coating treatment methods have their own advantages. In summary, coating materials are promising to be used in buildings and interiors for efficient noise reduction and control.

Leakage detection method of underground heating pipeline based on improved wavelet threshold function

For urban heating pipeline network, the intelligent and precise detection is an important guarantee for urban heating infrastructure of the safe and low-carbon operation. Heating pipe network was directly buried in the soil and difficult to detect the leakage point due to lack of non-excavation detection method for rapid repair. An improved noise reduction algorithm of the wavelet threshold function coupled with the acoustic method was used for the directly buried pipe leakage detection. Large-scale leakage experimental tests were used to validate the reliability. The influences of temperature, pressure, flowrates and leakage distance on the frequency and time domains were then investigated. Results show that the improved threshold function could well detect the leakage for directly buried hot water heating pipes. When the temperature increases, the leakage frequency domain spreads from 200 - 800 Hz to 50–1500 Hz. The pressure, flow rate, and leak point location could affect the amplitude feature. The accuracy ranges of the improved method in the different location, temperature and pressure and flowrates are separately 0.11–2.36%, 0.11–1.96%,0.11–3.49% and 0.16–1.55%, respectively.

Research on the effects of resonance tube bending in loop thermoacoustic refrigeration system

A high-power-density heat-driven loop thermoacoustic refrigeration system has the potential to meet miniaturization requirements of vehicle waste heat recovery systems. Given the strict space constraints in vehicle power compartments and the need to reduce equipment footprint, the resonance tubes need to be bent and coiled. However, the introduction of curvature may increase losses in resonance tubes. In order to evaluate the minor losses caused by bending on resonance tubes, a double-acoustic-source driven acoustic field modulation platform was constructed to experimentally investigate bent resonance tubes with different arc lengths under two common bending angles (90° and 180°) under high working pressure. Through the acoustic field reconstruction method, the acoustic field distributions of each resonance tube were obtained, and the effects of changes in bending angle and arc length on acoustic field and acoustic power dissipation were analyzed. The results indicated that the introduction of curvature leads to an increase in traveling-standing wave ratio, acoustic power, and acoustic power dissipation, with the degree of increase influenced by both curvature and arc length. Under the same bending angle, the minor loss is directly proportional to curvature, and there is an ExpAssoc function relationship between minor loss coefficient and curvature. Under the same curvature, the minor loss is not proportional to arc length, and minor loss coefficient slightly increases with the arc length doubles. The findings fill the research gap in resonance tube bending in loop thermoacoustic systems and provide guidance for the selection of resonance tube bending parameters.

Evaluation of a Local Acoustic Resonance Method for Coating Thickness Determination on Stochastic Metal Hybrid Foams

Novel cellular materials promise a combination of high strength and low specific weight for sustainable and energy‐efficient material design. Inhomogeneities of the materials, such as varying coating thickness, are inherent to the production process but require advanced characterization in order to better understand mechanical properties. Herein, global and local acoustic analysis for nondestructive characterization of local coating thickness on an open‐porous hybrid metal foam system is studied. Experiments and numerical simulations are carried out to evaluate vibration behavior stimulated by a low‐energy impact. The novelty of the work is the use of local excitation and analysis of decay times to determine coating thickness gradients. This qualitative measurement can inform and speed up the material development process due to its simplicity, speed, and low cost. Furthermore, it provides a basis for quality control when scaling up the technology of metal hybrid foams for industrial applications.

A convenient acoustic measurement of femtosecond filamentation based on a cell phone

The acoustic diagnostic method has been widely used in diagnosing femtosecond filamentation. In this work, acoustic measurements of femtosecond laser filamentation and weakly air-ionized region are conducted using a readily available portable device - a cell phone. The signal is extracted by employing digital signal processing method of Fourier transformation filtering. The results demonstrate that the method can be used for effective measurements even in noisy environments. This greatly enhances the accuracy and portability of the acoustic method, providing a practical approach for improving acoustic diagnostics.

Advanced acoustic leak detection in water distribution networks using integrated generative model

Water distribution networks (WDNs) experience significant water loss due to leaks, necessitating advanced water leak detection methods. However, machine learning-based acoustic method heavily relies on signal information and is limited by data scarcity and the limited diversity of available data. To address this challenge and enhance water leak detection in WDNs, this study proposes an LSTM-GAN approach. Acoustic signals are collected from WDNs to train the LSTM-GAN model, which generates synthetic leak signals to enhance the dataset. The validity of the generative method is evaluated through t-SNE and acoustic characteristics analysis. LSTM-based water leak detection models are established and compared using the original and the generated datasets to confirm the efficacy of generated samples in improving water leak detection performances. The capability of LSTM-GAN has been evaluated through different perspectives, including sensitivity analysis and model comparison. The results validate the quality and consistency of the generated acoustic signals under leak conditions. Besides, the optimal number of generated samples should be determined according to the requirements and characteristics of the leak detection task. Furthermore, the comparison between the proposed method and other acoustic generative methods demonstrates the superiority of LSTM-GAN-generated signals in enhancing the performance of leak detection models. The proposed generative method offers an innovative approach to facilitate machine learning-based leak detection models with limited data, thereby enhancing robustness.