Acoustic Detection Technology Research Articles

Research on underwater acoustic detection technology based on optical waveguide resonator cavity

Purpose In acoustic detection technology, optical microcavities offer higher detection bandwidth and sensitivity than traditional acoustic sensors. However, research on acoustic detection technologies involving optical microcavities has not yet been reported. Therefore, this paper aims to design and construct an underwater acoustic detection system based on optical microcavities and study its acoustic detection technology to improve its performance. Design/methodology/approach Based on the principles of optical microcavity acoustic sensors, a signal-detection circuit was designed to form a detection system in conjunction with a laser, an optical waveguide resonator and an oscilloscope. This circuit consists of two modules: a photodetection module and a filter amplification module. Findings The photodetection module features a baseline noise of −106.499 dBm and can detect device spectral line depths of up to 2410 mV. The gain stability of the filter amplification module was 58 dB ± 1 dB with a noise gain of −107.626 dBm. This design allows the acoustic detection system to detect signals with high sensitivity within the 10 Hz−1.2 MHz frequency band, achieving a maximum sensitivity of −126 dB re 1 V/µPa at 800 Hz and a minimum detectable pressure (MDP) of 0.37 mPa/Hz1/2, corresponding to a noise equivalent pressure (NEP) of 51.36 dB re 1 V/µPa. Originality/value This study designs and constructs a broadband underwater acoustic detection system specifically for optical waveguide resonators based on the sensing principles of silicon dioxide optical waveguide resonators. Experiments demonstrated that the signal detection module improves the sensitivity of underwater acoustic detection based on optical waveguides.

Acoustic Detection of Pipeline Blockages in Gas Extraction Systems: A Novel Approach

Gas extraction is crucial for coal mine safety, yet pipeline blockages by solid slag and water severely hinder efficiency and pose risks. Traditional detection methods are limited by rapid signal attenuation and noise interference. In this study, an acoustic detection technology is introduced for pipeline blockages, utilizing sensors at potential blockage points to collect sound wave data. Experiments with a scaled pipeline model reveal that slag blockages produce characteristic peaks in the 1200 Hz–2000 Hz range, while water blockages show peaks in the 1 kHz–2 kHz and 3.5 kHz–4.5 kHz bands. The longitudinal blockage intensity and extraction pressure significantly affect the sound pressure levels. A reliable fitting model predicts the blockage intensity based on acoustic signals, achieving high accuracy. This novel method enhances blockage identification, offering a non-invasive, cost-effective solution that improves coal mine safety and efficiency.

A review of potential fault detection methods for traditional collector stations and sensor fault diagnosis in wind farm doubly-fed generator converters

Collector stations sometimes face situations where existing relay protection systems may fail, making potential fault monitoring technology increasingly important. This technology plays a critical role in ensuring the stability of power supply and enhancing economic benefits. Simultaneously, in response to the nation's "dual carbon goals," the construction and operation of large-scale renewable energy power stations are booming. Therefore, potential fault monitoring technology for new power stations should not be overlooked. For traditional power stations, the application and development trends of infrared thermal imaging technology, vibration analysis and acoustic detection technology, chemical analysis, and oil quality detection technology will be compared and discussed. Regarding new power stations, methods for diagnosing sensor faults in doubly-fed generator converters in wind power will be analyzed and discussed.

Borehole radiation wavefield for multipole logging while drilling with an eccentric collar

Abstract This study investigates the radiation characteristics of the borehole-source in acoustic logging while drilling (ALWD) with an eccentric collar. Understanding these characteristics is crucial for the development of acoustic remote detection technology, which has shown great potential in ALWD. Considering the frequent occurrence of tool eccentricity during drilling, it is essential to take into account its impact on the acoustic wavefield outside the borehole. In this paper, we establish an ALWD model with an eccentric collar in a dual-cylindrical coordinate system. By applying the translational Bessel addition theorem and the steepest descent method, we obtain the far-field asymptotic solution of the problem and model the radiation wavefield. Our findings indicate that the asymmetrical radiation pattern resulting from drill collar eccentricity can effectively resolve ambiguities in determining the reflector azimuth.

Acoustic Rapid Detection Technology and Its Application for Rare Earth Element (REE)-Rich Sediments in the Pigafetta Basin of the Western Pacific

This study aims to investigate the stratigraphic features and rare earth element (REE) mechanisms of deep-sea REE-rich sediments in the West Pacific Pigafetta Basin using acoustic rapid detection technology. Through an analysis of sub-bottom profile data and synthesis of existing studies, this study reveals the acoustic properties and thickness distribution of the REE-rich sediments. Acoustic spectral records identify three distinct acoustic facies: opaque (O), transparent (T), and laminated (L). This study maps the thickness and spatial distribution of the REE-rich sediment layer in the research area, ranging from approximately 6 to 36 m in thickness. Regions with REE-rich sediments exceeding 30 m in depth are identified, showing concentrated distribution along the northwest–southeast axis and a contiguous zone in the southwest corner of the study area. The method employed in this study can determine the potential bottom boundary of the REE-rich layer by assessing the thickness range of the sedimentary layer, overcoming limitations of traditional sampling methods. Furthermore, the thickness distribution characteristics of the REE-rich sedimentary layer in the study area provide valuable insights for future research on resource evaluation and estimation.

Distributed Partial Discharge Acoustic Signal Detection and Localization Technology for GIL With Built-in Fiber Optics

Distributed Partial Discharge Acoustic Signal Detection and Localization Technology for GIL With Built-in Fiber Optics

Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective

Against the backdrop of severe leakage issue in water distribution systems (WDSs), numerous researchers have focused on the development of deep learning-based acoustic leak detection technologies. However, these studies often prioritize model development while neglecting the importance of data. This research explores the impact of data augmentation techniques on enhancing deep learning-based acoustic leak detection methods. Five random transformation-based methods—jittering, scaling, warping, iterated amplitude adjusted Fourier transform (IAAFT), and masking—are proposed. Jittering, scaling, warping, and IAAFT directly process original signals, while masking operating on time-frequency spectrograms. Acoustic signals from a real-world WDS are augmented, and the efficacy is validated using convolutional neural network classifiers to identify the spectrograms of acoustic signals. Results indicate the importance of implementing data augmentation before data splitting to prevent data leakage and overly optimistic outcomes. Among the techniques, IAAFT stands out, significantly increasing data volume and diversity, improving recognition accuracy by over 7%. Masking enhances performance mainly by compelling the classifier to learn global features of the spectrograms. Sequential application of IAAFT and masking further strengthens leak detection performance. Furthermore, when applying a complex model to acoustic leakage detection through transfer learning, data augmentation can also enhance the effectiveness of transfer learning. These findings advance artificial intelligence-driven acoustic leak detection technology from a data-centric perspective towards more mature applications.

Denoising of laser cladding crack acoustic emission signals based on wavelet thresholding method.

Due to the influence of thermal stress and other factors during laser cladding, cracks will appear in the cladding layer. To reduce cladding loss, an effective method is to detect cracks early and adjust fusion parameters promptly. The paper utilized acoustic signal detection technology to collect crack defect signals during the laser cladding process. Subsequently, the collected acoustic signals were subjected to time-frequency analysis and wavelet thresholding denoising. Then by applying a custom thresholding approach for each level of wavelet coefficients, the crack signals were denoised. The results showed that this method achieved better denoising effects compared to other signal denoising methods and preserved the crack information to a greater extent, confirming the feasibility of the custom thresholding denoising approach. It helps to achieve online detection and acoustic signal analysis of cracks during the cladding process.

Experimental study on acoustic signal characteristic analysis and time delay estimation of pipeline leakage in boilers

Acoustic signal detection technology has significant advantages in detecting the leakage and bursting of heat exchange pipes in boilers. To address the current lack of sound signal data for steam medium leakages and the problem of large errors in the complex sound field environment of power plants, we performed an innovative experimental comparative analysis of leakage acoustic signals under a dual medium of compressed air and steam to provide a reference for detecting leakage acoustic signals. During the experiment, the time and frequency domains were analyzed by changing the pressure of the leakage pipeline and aperture of the leakage hole, and the spectrogram and power spectrum of the leakage acoustic signal were obtained using fast Fourier transform and autocorrelation analysis. The results showed that the signal value of the leakage medium increased with increasing pipeline pressure and leakage aperture and that the energy of the steam leakage acoustic signal was greater than that of compressed air under the same pressure and aperture because of its larger specific heat capacity. In addition, the complete empirical mode decomposition of adaptive noise (CEEMDAN) algorithm was introduced into the denoising decomposition of the leakage sound signal in the furnace, and the average error of the time delay value of the leakage acoustic signal calculated using the CEEMDAN algorithm was observed to be within 5%.

SNR enhancement of quasi-distributed weak acoustic signal detection by elastomers and MMF integrated Φ-OTDR.

We have proposed and demonstrated a weak acoustic signal detection technology based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). Non-contact acoustic signals transmitting through air gap between the sound source and the receiver are difficult to detect due to fast attenuation. In order to improve the detection ability of non-contact weak acoustic signals, we demonstrate that multi-mode fiber (MMF) is a better solution than single-mode fiber (SMF) benefiting from its larger core and higher Rayleigh backscattering (RBS) capture coefficient. The frequency signal-to-noise ratio (SNR) has been enhanced by 9.26 dB. Then, with the help of 3D printing technology, elastomers have been designed to further enhance the detection ability due to the high-sensitive response to acoustic signals. Compared with the previous reported "I" type elastomer, the location and frequency SNR enhancement caused by our new proposed "n" type elastomer are 8.39 dB and 11.02 dB in SMF based system. The values are further improved to 10.51 dB and 13.38 dB in MMF and "n" type elastomer integrated system. And a phase-pressure sensitivity of -94.62 dB re rad/µPa has been achieved at 2.5 kHz. This non-contact weak acoustic signal detection technique has great application potential in the quasi-distributed partial discharge (PD) detection of smart grid.

Phononic Crystal Coupled Mie Structure for Acoustic Amplification

In the field of industrial structure detection, acoustic signals have been pivotal. A cost-effective and highly sensitive acoustic monitoring system that can enhance weak acoustic signals has always been an interesting topic in many research fields. However, environmental noise signals have consistently hindered the improvement of the signal-to-noise ratio (SNR) of traditional acoustic systems. In this work, we propose a structure (PC-Mie) that couples phononic crystal (PC) point defects and Mie resonance structures (Mies) to enhance weak effective signals from complex environments. Numerical simulations have confirmed that the PC-Mie exhibits superior sound pressure enhancement performance compared to each individual PC point defect and Mies. Moreover, the capability to amplify the sound pressure amplitude is related to the angle and position of the Mies at the center position. Simultaneously, the PC-Mie has a narrower bandwidth, giving the structure stronger frequency selectivity. Finally, the experiment proves that PC-Mie can function as an enhanced acoustic device or sensor to detect harmonic signals, verifying the validity of the PC-Mie structure for acoustically enhanced perception. Both numerical and experimental studies demonstrate that the PC-Mie can effectively enhance the energy of specific sound frequencies in complex air environments, making it suitable for collecting high-sensitivity acoustic signals. This research has significant implications for the development of weak acoustic signal detection technology and the application of self-powered sensors.

Research on the Development and Application of Acoustic Testing Technology

Taking the commonly used acoustic detection technology as the research object, the working principle of different types of acoustic detection technology and the application of adverse geological forecasting and nondestructive testing in the geotechnical field are described, and the advantages and disadvantages of different types of acoustic detection technology are pointed out through the discussion of the development of acoustic detection technology.

Acoustic Model for Leak Detection of Water Supply Pipeline

The leak detection of water supply pipelines is significant for the protection of water resources. Acoustic detection is a common method used to investigate water supply pipeline leaks. Although some acoustic methods for the leak detection of the water supply pipeline have been developed experimentally, the theoretical investigation of said acoustic methods is still limited. Compared with aeroacoustics, the development of quantitative jet acoustic theory towards flow field calculations of liquid pipeline leak has not been reported, and the characteristics of the liquid flow field cannot be quantitatively transformed into the acoustic model. In this paper, the liquid pipeline leak is combined with piston acoustics, and the acoustic model for the leak detection of the water supply pipeline is quantitatively studied for the first time. The acoustic pressure value can be directly calculated using pipe and liquid parameters, and the validity of the model can be verified experimentally. Based on theoretical and experimental investigations, it is found that the leak sound pressure increases significantly with increasing pipeline pressure and decreases with increasing detection distance. The material composition of the pipes has little influence on the leak sound pressure. The theoretical values based on the proposed acoustic model and the experimental values agree well, where the maximum difference between them is 8.5% and the average difference is 2.6%. This study presents a foundation for the development of acoustic leak detection technology of the liquid pipeline.

Mechanical damage characteristics and nondestructive testing techniques of fruits: a review

Fruits will be subjected inevitably to various external forces in the process of harvesting, transportation, processing, and storage, which will cause mechanical damage. The research on mechanical properties and damage mechanisms of fruit can effectively control its loss. In this study, fruits are divided into different types according to their morphology and structure. The impact, vibration, static pressure, and other mechanical damage on fruits are studied. It is important to identify the damaged parts of fruit after damage quickly and accurately. Therefore, this study analyzes the application of nondestructive testing technologies such as spectral detection technology, NMR (nuclear magnetic resonance) detection technology, and acoustic and electrical characteristics detection technology in fruit damage detection.

Application of Long-Range Cross-Hole Acoustic Wave Detection Technology in Geotechnical Engineering Detection: Case Studies of Tunnel-Surrounding Rock, Foundation and Subgrade

The adverse geological conditions of soil cave, karst cave and goaf in deep foundation directly affect the safety and economy of geotechnical engineering construction. It is a difficult problem in geotechnical engineering detection to detect the distribution of bad geological conditions efficiently and accurately. Aiming at the problems of short penetrating distance and low resolution of cross-hole acoustic wave detection in rock-soil mass, based on the characteristics of acoustic wave propagation in rock and soil layers and comprehensively utilizing the spark source and data acquisition device, a long-distance cross-hole acoustic wave detection technology is proposed. According to the indoor concrete model test and field tests of geotechnical engineering, the applicability of the long-distance cross-hole acoustic wave detection technology in the detection of geotechnical structure and adverse geological phenomena under complex geological conditions is verified. The results show that acoustic wave CT imaging can accurately detect the cavities in the indoor concrete model test. In addition, the field tests of the grouting effect of tunnel-surrounding rock, high-rise building foundation and subgrade further verify the rapidity, accuracy and intuitiveness of the long-distance cross-hole acoustic wave detection technology. This work provides a reference for eliminating the potential safety problems caused by adverse geological conditions and similar geotechnical engineering investigation.

Acoustic detection of the wood borer, Semanotus bifasciatus, as an early monitoring technology.

Semanotus bifasciatus Motschulsky (Coleoptera: Cerambycidae) is one of the most destructive wood-boring pests of Platycladus trees in East Asia, threatening the protection of antique cypresses and urban ecological safety. Early identification of Semanotus bifasciatus attacks can help forest managers mitigate the infestation before it turns into an outbreak. Acoustic detection technology is a non-destructive and continuous monitoring method with the potential to early identify and accurately evaluate the wood-boring damage. However, few studies have focused on the detection timing and corresponding acoustic features. In this study, we employed a manipulated insect infestation experiment to identify time windows in which early instar Semanotus bifasciatus larvae are most actively boring and feeding within logs and to identify acoustic features that distinguish larval sounds from typical background noise. The Semanotus bifasciatus larvae produced sounds most frequently between 13:00 and 20:00 while sounds were detectable from the first to the third instar during the larval growth stage, indicating a suitable time window for early detection. The stepwise regression (SR) model was optimal for detecting the larval instar [coefficient of determination (R2 )=0.71, root mean squared error of prediction (RMSEp )=0.42, and relative percent deviation (RPD)= 3.38] while the best model for predicting larval population size was the partial least squares regression (PLSR) model (R2 =0.97, RMSEp =61.96, and RPD=28.87). This study developed an acoustic method for identifying the early attack of Semanotus bifasciatus (including detection time window, feature variables and models for larval instar prediction and population size estimation). This technology integrated with internet of things (IoT) framework can be of value in developing an automated monitoring system for forest wood borer, and provide necessary guidance for integrated pest management (IPM). © 2022 Society of Chemical Industry.

Acoustic Denoising Using Artificial Intelligence for Wood-Boring Pests Semanotus bifasciatus Larvae Early Monitoring.

Acoustic detection technology is a new method for early monitoring of wood-boring pests, and the effective denoising methods are the premise of acoustic detection in forests. This paper used sensors to record Semanotus bifasciatus larval feeding sounds and various environmental noises, and two kinds of sounds were mixed to obtain the noisy feeding sounds with controllable noise intensity. Then, the time domain denoising models and frequency domain denoising models were designed, and the denoising effects were compared using the metrics of a signal-to-noise ratio (SNR), a segment signal-noise ratio (SegSNR), and log spectral distance (LSD). In the experiments, the average SNR increment could achieve 17.53 dB and 11.10 dB using the in the test data using the time domain features and frequency domain features, respectively. The average SegSNR increment achieved 18.59 dB and 12.04 dB, respectively, and the average LSD between pure feeding sounds and denoised feeding sounds were 0.85 dB and 0.84 dB, respectively. The experimental results demonstrated that the denoising models based on artificial intelligence were effective methods for S. bifasciatus larval feeding sounds, and the overall denoising effect was more significant, especially at low SNRs. In view of that, the denoising models using time domain features were more suitable for the forest area and quarantine environment with complex noise types and large noise interference.

An Adaptive Graph Morlet Wavelet Transform for Railway Wayside Acoustic Detection

Monitoring condition of train axle bearings is significant for ensuring the safety of train operation. Wayside acoustic detection technology is a promising tool for early fault detection of train axle bearings. However, the strong background noise exists in collected acoustic signals, which normally masks the fault feature of axle bearings. In this work, a novel method called adaptive graph Morlet wavelet transform (AGMWT), is proposed for fault feature extraction in railway wayside acoustic detection. In AGMWT, acoustic signals are firstly transformed into horizontal visibility graphs. Next, the inner product operation is conducted to measure the similarity between the graph and daughter wavelets which are translated and scaled versions of the mother Morlet wavelet. An adaptive wavelet threshold and a shrinkage strategy are then proposed to shrink the graph Morlet wavelet coefficient, and finally the denoised signal can be obtained using inverse transform. To improve denoising performance, parameters of the mother Morlet wavelet are then optimised according to the Hilbert envelope spectrum fault feature ratio and the Hilbert envelope entropy. The effectiveness of the proposed method has been verified by conducting simulation, laboratory and field experiments. In addition, the denoising ability of AGMWT has advantages comparing other methods.

The Unexpected Consequences of Automation in Policing

This Article has two aims. First, it explains how automated decision-making can produce unexpected results. This is a problem long understood in the field of industrial organization, but identifying its effects in policing is no easy task. The police are a notoriously difficult institution to study. They are insular, dislike outsiders, and especially dislike critical outsiders. Fortunately, we have the benefit of a decade’s worth of experimentation in police use of automated decision-making and the resulting political backlash against some of these uses. As a result, some large urban police departments have undergone external investigations to see whether tools like predictive policing or individual criminal risk assessments are biased, ineffective, or simply too costly despite their benefits. One of these recent reports, studying the use of acoustic gunshot detection software in Chicago, provides a window into one type of police automation. This leads to the Article’s second observation. Automation is not just a set of tools that the police use; it changes the environment of policing in unexpected ways. The increasing use of automated tools in policing has generated some widely shared criticisms, but they focus primarily on the flaws of the technologies used. The training data in facial recognition algorithms may be biased along lines of race, gender, and ethnicity. Risk assessments for gun violence may, in truth, be poor guides for police intervention. These claims are singularly technology-focused. Accordingly, errors and inefficiencies merit technological improvements. Even calls for bans on technologies like facial recognition are responses to the technology itself. As Chicago’s experience with acoustic gunshot detection technology demonstrates, however, automation serves not just as a tool for the police, but also leads to changes in police behavior. These changes in police conduct are documented in a 2021 report from the Chicago Office of Inspector General, and they are noteworthy. If automation unexpectedly changes police behaviors, these changes have implications for how we understand policing through the lens of inequality and unaccountability.

An Optical Acoustic Detection System Based on Fabry Pérot Etalon Stability Structure.

The optical acoustic detection system based on the Fabry Pérot Etalon (FPE) with high quality–factor (High Q) and stability structure is described and tested. The FPE contains two high–reflectivity Plano–Concave lenses, achieving high fineness and stability. The protective structure of the confocal stabilized FPE is composed of an invar tube, copper sheath, Bakelite sheath and aluminum housing to protect the sensor from the effects of ambient temperature and vibration. The audio signal is injected into the cavity through the sound hole located in the center of the cavity. Acoustic waves induce the vibration of the medium in the cavity, which leads to a simultaneous change in the FPE optical path and a shift of the interference spectrum. The acoustic detection system is built, and the frequency of the laser is locked on the resonant frequency points of the FPE by using phase modulation technology, so as to detect acoustic signals of different frequencies and amplitudes. In addition, the sensitivity of the proposed sensor exceeds 34.49 mV/Pa in the range of 20 Hz–20 kHz. A Signal-to-Noise Ratio (SNR) of 37 dB can be achieved at 20 Hz. Acoustic signal detection technology based on the FPE stability model is used to test the theoretical feasibility of the future high sensitivity Fabry Pérot Interferometric (FPI) acoustic sensors.