Characteristics Of Acoustic Signals Research Articles

Research on wavelet packet energy entropy extraction method for acoustic signal of tunnel lining cavity

Abstract Tunnel lining cavity is a common disease in high-speed railway tunnels, which seriously affects the safe operation of the line.At present, the commonly used method is manual knocking inspection, but it relies on manual judgment, subjective factors have great influence, and there are errors. This paper proposes a feature extraction method of acoustic vibration signal of tunnel lining cavity based on wavelet packet energy entropy and uses wavelet packet energy entropy to realize the identification of cavity defects.The full-scale simulation model of tunnel with a radius of 6m was constructed by COMSOL and different cavity conditions were set up. The wavelet packet energy entropy characteristics of acoustic signals were extracted and analyzed. The mapping relationship between cavity depth and cavity size and wavelet packet energy entropy was explored. The wavelet packet energy entropy and frequency band distribution characteristics were extracted to realize the identification of cavity defects.The local model which can characterize the whole tunnel is designed and the embedded defects are verified by experiments. The results show that the wavelet packet energy entropy can be used as the identification feature of tunnel lining cavity defects. When the wavelet packet energy entropy is less than 2.53, the existence of tunnel lining cavity defects is characterized.When the wavelet packet energy entropy is in the range of 2.53-2.94, it characterizes the existence of the transition boundary between tunnel lining cavity defect and no disease.This index can be used to realize the rapid and effective identification of cavity defects.

Research on the identification method based on energy distribution of adaptive WPD for railway tunnel lining void

ABSTRACT As the main disease of railway tunnels, void disease affects the safe operation of railways. The traditional detection technology recognition void relies on the intuitive characteristics of the signal, there are still problems such as poor void edge recognition capability and low intelligence level. To solve this problem, this study used Comsol software to establish a sound-solid coupling finite element model of the tunnel, extracted the acoustic signals of voids under various conditions, analysed the acoustic signal characteristics under different conditions and proposed the acoustic sensitive frequency band of tunnel lining void. The test model of Partial tunnel lining is established by the method of layering pouring. The wavelet packet algorithm is improved by the maximum cross correlation coefficient, the optimal wavelet basis decomposition algorithm is proposed. The layer number of the wavelet packet decomposition (WPD) algorithm was calculated based on the sensitive frequency band and the energy distribution of the sub-signal is obtained. Then, the energy distribution of the sensitive frequency band is used to estimate the void boundary. The experimental results show that the acoustic frequency band of the tunnel void mainly distributes in the range of 0–10,000 Hz and the sensitive frequency band of the void concentrated around 0–3000 Hz. According to the sampling frequency and the sensitive frequency band of the void, the sub-signal energy distribution of each layer is obtained. The energy distribution of sub-signal 1 has strong void boundary recognition ability. The research results will be beneficial to the damage detection and state assessment of tunnel lining.

Features of Recording Energy Parameters of Acoustic Emission Signals from Materials

The acoustic emission method is used as a means of analyzing the state of materials and structures, non-destructive testing and diagnosing them during operation. An important advantage over other methods is that it reacts only to the development of truly dangerous defects and is capable of monitoring large volumes and surfaces without scanning them. The article presents the results of the development of methods and algorithms designed to analyze the energy characteristics of acoustic signals. The use of spectral methods is justified.

A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature

With the aim of solving the problem of identifying complex underwater acoustic targets using a single signal feature in the Time–Frequency(TF) feature, this paper designs a method that recognizes the underwater targets based on the Delay-Doppler joint feature. First, this method uses symplectic finite Fourier transform (SFFT) to extract the Delay-Doppler features of underwater acoustic signals, analyzes the Time–Frequency features at the same time, and combines the Delay-Doppler (DD) feature and Time–Frequency feature to form a joint feature (TF-DD). This paper uses three types of convolutional neural networks to verify that TF-DD can effectively improve the accuracy of target recognition. Secondly, this paper designs an object recognition model (TF-DD-CNN) based on joint features as input, which simplifies the neural network’s overall structure and improves the model’s training efficiency. This research employs ship-radiated noise to validate the efficacy of TF-DD-CNN for target identification. The results demonstrate that the combined characteristic and the TF-DD-CNN model introduced in this study can proficiently detect ships, and the model notably enhances the precision of detection.

Detection of Gate Valve Leaks through the Analysis Fractal Characteristics of Acoustic Signal

This paper considers the possibility of using monofractal and multifractal analysis of acoustic signals to detect water leaks through gate valves. Detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MF-DFA) were used. Experimental studies were conducted on a ½-inch nominal diameter wedge valve, which was fitted to a ¾-inch nominal diameter steel pipeline. The water leak was simulated by opening the valve. The resulting leakage rates for different valve opening conditions were 5.3, 10.5, 14, 16.8, and 20 L per minute (L/min). The Hurst exponent for acoustic signals in a hermetically sealed valve is at the same level as a deterministic signal, while the width of the multifractal spectrum closely matches that of a monofractal process. When a leak occurs, turbulent flow pulsations appear, and with small leak sizes, the acoustic signals become anticorrelated with a high degree of multifractality. As the leakage increases, the Hurst exponent also increases and the width of the multifractal spectrum decreases. The main contributor to the multifractal structure of leak signals is small, noise-like fluctuations. The analysis of acoustic signals using the DFA and MF-DFA methods enables determining the extent of water leakage through a non-sealed gate valve. The results of the experimental studies are in agreement with the numerical simulations. Using the Ansys Fluent software (v. 19.2), the frequencies of flow vortices at different positions of gate valve were calculated. The k-ω SST turbulence model was employed for calculations. The calculations were conducted in a transient formulation of the problem. It was found that as the leakage decreases, the areas with a higher turbulence eddy frequency increase. An increase in the frequency of turbulent fluctuations leads to enhanced energy dissipation. Some of the energy from ordered processes is converted into the energy of disordered processes.

Bearing fault detection technology for automated machinery based on acoustic analysis

Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency.

Визначення крупності продуктів подрібнення

Mathematical approaches to particle size determination in closed grinding cycles are considered. The features of average particle size calculation for different fractions with account for the grinding kinetics are shown. Particle size calculation algorithms for the entire fraction range are proposed. Particular attention is paid to output determination for fractions of arbitrarily small particles. A particle size determination method based on a lognormal distribution function is shown. In choosing the mathematical approach, the process requirements are taken into account. The basis of in-flow noncontact particle size control is the acoustic monitoring of the process and the established relationships between the particle size and the acoustic characteristics. The signal amplitude during material transportation in the energy carrier flow and jet grinding was found as a function of the particle size and grinding conditions. In order to determine the fractional composition of a mixture, the frequency characteristics of acoustic signals and their variation during the transportation of narrow fractions and mixtures were considered. The analysis of the amplitude-frequency characteristics of acoustic signals during the compressed-air transportation of narrow fractions in the jet mill channels confirmed the presence of signals with frequencies characteristic for each fraction. These frequencies were experimentally related to the particle size of a fraction in a mixture. These studies form a basis for a noncontact method of determining the particle size distribution of a material in an air flow, in particular in jet grinding. The results may be used for engineering and technological calculations in mineral dressing and the development of process equipment for the chemical industry, construction, mining, and metallurgy.

Detection of Pipeline Leaks Using Fractal Analysis of Acoustic Signals

In this paper, the possibility of using monofractal and multifractal analysis of acoustic signals of pipelines to detect leaks is considered. An experimental stand has been created to study the fractal characteristics of acoustic signals of pipelines with “slit” type defects. During the experiments, defects of the “slit” type pipeline with dimensions of 2 mm, 8 mm, and 20 mm were modeled. Detrended fluctuation analysis (DFA) and the multifractal detrended fluctuation analysis (MF-DFA) were used. As a result of the experimental studies, it was found that the occurrence of leakage leads to the occurrence of anticorrelated vibrations in a pipeline with multifractal properties. The analyses of acoustic signals by DFA and MF-DFA methods make it possible to reliably determine the leakage. The Hurst exponent and the width of the multifractal spectrum can serve as indicators of the occurrence of leaks in pipelines.

Research on the Application of Speech Database based on Emotional Feature Extraction in International Chinese Education and Teaching

The advanced analysis of the relationship between acoustic and emotional characteristics of speech signals can effectively improve the interactivity and intelligence of computers. Given the current status of speech recognition and the problems encountered in international Chinese education, the study proposes to extract emotional characteristics to achieve speech construction of the database. Based on considering the emotional characteristics of speech, a hybrid algorithm based on spectral sequence context features is proposed. The DBN-BP algorithm is used to process emotional data of different dimensions, and a speech database is constructed. After testing and analyzing the algorithm model, it is found that the dynamic recognition accuracy of the DBN-BP model fused with emotional features is over 90%, and the negative emotion recognition rates in the three databases are all above 60%. At the same time, the accuracy rate of the model in the algorithm comparison experiment remains above 85%, the data information extraction is relatively complete, and the average test time of less than 1s is less than 3%. The speech database based on multi-emotional feature extraction can effectively provide a new reference for the improvement of the quality of Chinese international education and the improvement of the speech recognition system.

АНАЛІЗ АКУСТИЧНИХ СИГНАЛІВ ЗАСОБІВ ПОВІТРЯНОГО НАПАДУ

This paper analyzes the acoustic signals of aerodynamic aerial attack weapons used by the Russian Federation during a full-scale invasion. These include strike unmanned aerial vehicles, cruise missiles, jet and turboprop aircraft, and helicopters. It has been established that the time for observing an acoustic signal is limited by the speed of the vehicle. For high-speed targets (cruise missiles, airplanes), it averages 10 s, and for slower targets, 40-50 s. The Welch periodogram method was used to extract the spectral characteristics of acoustic signals. It is shown that the acoustic signal of propeller-driven vehicles is the sum of harmonic and noise-like broadband components, and that of turbojet-powered vehicles has a predominantly noise-like structure with several narrowband components. It was found that at the moment of maximum convergence, the signal spectrum has the greatest width. The characteristic change in the frequency of harmonic components associated with the Doppler effect is investigated. It can be used to estimate the parameters of motion and identify the vehicle. By correlation analysis of acoustic signals, it was found that broadband components have a noise-like structure. An acoustic signal for the case of simultaneous over flight of different types of vehicles was formed by adding records of different acoustic signals. It is shown that the characteristic spectral characteristics of all objects are preserved on the frequency-time plane. The results of the analysis can be used to build mathematical models of acoustic signals and to develop methods for processing signals of aerial attack weapons in an acoustic airspace monitoring system. Keywords: aerial attack weapons; acoustic signal; spectrogram; acoustic monitoring system; harmonic component.

Acoustic signal-based automated control of welding penetration using digital twin technology

Weld penetration control has emerged as a critical area of research in the field of online control for ensuring the quality of robotic welds. Acoustic signals, which are known for their distinct temporal characteristics, play a pivotal role in the online assessment of weld quality. This study proposes a novel filter bank specifically tailored for robotic welding and investigates the working environment of robot welding. Twenty-six time-domain and frequency-domain features were extracted from weld acoustic signals, and statistical analyses and comparative methods were used to identify variations in defective signal features and interpret their physical significance. By leveraging these acoustic signal characteristics, this study established a predictive identification model and an online feedback controller. The predictive identification model effectively identified different penetration levels in the welding process, and the identification results served as a reference input for online regulation of the welding speed by the controller. Additionally, a digital twin system was developed, where the identification model and controller functioned as digital objects on a computer and an edge computer, respectively. Experimental tests demonstrated the superior performance of the system and model in accurately reflecting welding process penetration, regulating and stabilising the welding speed, and significantly enhancing the welding quality. The research presented in this paper offers notable advantages for the online control of welding penetration.

Analysis and identification of transformer acoustic signal based on compressed sensing

The operation state of the transformer affects the operation of the whole power system. Aiming at the application of online monitoring of transformer operation, based on compressive sensing theory and wavelet packet analysis technology, a transformer acoustic fault diagnosis method based on compressive sensing is proposed. Firstly, the partial Hadamard matrix is constructed as the observation matrix to compress the acoustic signal of the transformer. The energy decomposition of the compressed signal is completed based on the wavelet packet. Finally, the feature selection is completed by considering the energy distribution characteristics using wavelet information entropy. The acoustic signal characteristics of transformer core fault simulation data are extracted by this method, and the fault diagnosis simulation is completed by using a particle swarm -SVM classifier. The results show that this method can obtain higher fault identification accuracy under the condition of a high compression ratio.

Experimental study on the evolutionary characteristics of acoustic signals produced by granite under biaxial compression with different intermediate principal stresses

Biaxial compression is a typical stress state experienced by the surrounding rock near the excavation boundaries under deep underground engineering, frequently resulting in engineering geological disasters (spalling and rockburst). The motivation to mitigate the risk and damage of these disasters has led us to compressively examine the evolutionary characteristics of acoustic signals [microseismic (MS) events, sound and acoustic emission (AEs)] produced by granite under biaxial compression with different intermediate principal stresses. These characteristics include time (activeness and b value) and frequency (main frequency and proportion of the advantage frequency bands) domains. The results suggest that: 1) the signal properties-driven order of activeness under low and high intermediate principal stresses for the initial stresses were as follows: AE accounted for 37.4% and 43.5% of σpeak, MS for 61.1% and 66% of σpeak, and sound for 81.8% and 85.5% of σpeak. 2) The notable distinction in precursors of different acoustic signals before granite failure was confirmed: the sequential relationship in the continuous decrease rate of the b value (AE < MS < sound), the occurrence (only existing in AE signals) of a few signals with extremely high amplitude (the “quiescent period”) and the different frequency-change rule in the proportion of the advantage frequency bands. 3) The strong influences of intermediate principal stress on the signal precursors were determined; these precursors in the activeness, b value, and proportion are negative to intermediate principal stress, whereas that of the main frequency shows a positive correlation. Consequently, these findings can contribute integrated usage of the multifrequency signals in the prediction and warning of geological disasters under deep underground engineering.

Enhanced Feature Extraction Network Based on Acoustic Signal Feature Learning for Bearing Fault Diagnosis.

The method of acoustic radiation signal detection not only enables contactless measurement but also provides comprehensive state information during equipment operation. This paper proposes an enhanced feature extraction network (EFEN) for fault diagnosis of rolling bearings based on acoustic signal feature learning. The EFEN network comprises four main components: the data preprocessing module, the information feature selection module (IFSM), the channel attention mechanism module (CAMM), and the convolutional neural network module (CNNM). Firstly, the one-dimensional acoustic signal is transformed into a two-dimensional grayscale image. Then, IFSM utilizes three different-sized convolution filters to process input image data and fuse and assign weights to feature information that can attenuate noise while highlighting effective fault information. Next, a channel attention mechanism module is introduced to assign weights to each channel. Finally, the convolutional neural network (CNN) fault diagnosis module is employed for accurate classification of rolling bearing faults. Experimental results demonstrate that the EFEN network achieves high accuracy in fault diagnosis and effectively detects rolling bearing faults based on acoustic signals. The proposed method achieves an accuracy of 98.52%, surpassing other methods in terms of performance. In comparative analysis of antinoise experiments, the average accuracy remains remarkably high at 96.62%, accompanied by a significantly reduced average iteration time of only 0.25 s. Furthermore, comparative analysis confirms that the proposed algorithm exhibits excellent accuracy and resistance against noise.

Experimental Investigation on Near-Field Acoustic Propagation Characteristics of Leakage Detection in Submarine Pipelines

The leakage of subsea oil and gas pipelines can have adverse impacts on production progress and the ecological environment. Investigating the sound source and near-field sound propagation of pipeline leaks is essential for understanding the acoustic characteristics of and variations in these leaks. Such understanding is significant for the accurate detection and location of small leaks in pipelines. In this study, we designed an experimental system to study the characteristics of leakage sound signals. We introduced the formation mechanism of leakage sound sources and reviewed corresponding theoretical research. The leakage sound signal’s characteristic frequency range was determined to be between 1 kHz and 2 kHz. We examined the effects of pipeline pressure, leakage aperture, and detection distance on the acoustic signal characteristics. The results show that as internal pipe pressure increases, the leakage sound signal intensity first increases and then decreases. As the leakage aperture increases, the intensity of the leakage sound signal increases. Within a short distance, the intensity remains consistent regardless of detection distance. The results of this experimental study can guide the acoustic internal detection of pipelines. This study has practical significance in the timely detection of small leaks in pipelines and preventing leakage accidents from occurring.

Removal mechanism of Hydroides fouling on high-strength steel in the marine environment during nanosecond pulsed laser cleaning

The surface of metal materials will be contaminated by marine organisms when serving in the marine environment, which seriously affects the service life of the materials, and must be cleaned regularly. The Hydroides fouling on the surface of 30Cr3 high-strength steel was taken as the object to be cleaned; the cleaning quality at different laser fluences was studied; the desorption behavior and the characteristics of spectral and acoustic signals during laser cleaning were analyzed; and the removal mechanism of Hydroides fouling on the surface of high-strength steel was summarized. The results show that the cleaning effect of nanosecond pulsed laser on Hydroides fouling increases with the increase of laser fluence, and the laser fluence of 9.95 J/cm2 can be used to clean the Hydroides fouling with the maximum diameter of 1.62 mm completely with little damage to the substrate. The removal mechanism of Hydroides fouling is the inverse bremsstrahlung mechanism. In the process of cleaning, the surface fouling is partially vaporized and ionized into high-temperature plasma under the action of pulsed laser. The laser energy is absorbed through inverse bremsstrahlung absorption to form shock waves to compress the surface, which leads to cracks on Hydroides fouling. When the surface compressive stress is released, the Hydroides fouling is removed from the surface, and the removal effect increases with the increase of laser fluence.

Mel-frequency cepstral and spectral flux analysis of the acoustic signal for real-time status monitoring of laser cleaning

Due to the fact that the laser-based cleaning process is quick, efficient, and environmentally friendly, it has been utilized in a various industry, which has increased the number of studies pertaining to this process. In addition to process optimization, the real-time monitoring system was essential in preventing the overexposure of the laser beam to the cleansed surface, which would result in an engraving effect. This article demonstrated the analysis of the acquired sound signal to identify an overexposed laser beam during laser cleaning. In order to accomplish the aim of this work, the corroded boron steel plate was prepared. The laser cleaning procedure involved a four-loop laser scan. Variable scanning speeds between 100 and 1,000 mm s−1 were configured. Concurrently, the acoustic signal within the frequency range of 20 Hz to 10 kHz was acquired. The results indicate that the process with a scanning speed of 1000 mm s−1 recorded the clear surface without morphological change on the cleaned area, whereas an unacceptable deep gouge was formed during the second and third loops of the process with speeds of 100 mm s−1 and 300 mm s−1, respectively. According to an analysis of the acquired sound signal, the trend of the Mel Frequency Cepstral Coefficient (MFCC) was indicative of the existence of the ablated corroded substrate. In addition, the spectral flux can provide important information regarding the formation of a deep groove on a cleansed surface. This research demonstrates the feasibility of using the auditory signal to monitor the laser cleaning process. By characterizing the acoustic signal feature, it is possible to detect the completion of the cleaning process before the morphological change of the cleaned area existed. With further development, it was possible that this method would become the most efficient, resilient, and demanding in the future.

Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction

The natural gas pipeline leakage detection task based on acoustic signal has some problems such as background noise coverage, lack of effective features, and low fault identification accuracy caused by small sample data. However, only one of these problems was usually studied in previous technologies. Almost no one has attempted to challenge multiple issues at the same time. In this study, a natural gas pipeline leak detection model that integrates acoustic feature processing techniques and feature reconstruction is proposed to resolve the above problems collaboratively. This model consists of two components. The first component is a feature processing technique of the acoustic signal that integrates frequency domain vector denoising and time domain associative function feature enhancement. The second component is a one-dimensional convolutional neural network with an expanded structural feature encoder (FAE) for feature reconstruction (FAE-1D-CNN). In the feature processing stage of the acoustic signal, firstly, the acoustic signal collected by the acoustic sensor is discretized into a digital signal. Secondly, the energy modal function is used to perform high/low energy modal clustering of digital signal features. The feature validity is enhanced by adding association factors to the low-energy modal features matrix. A low-pass filtering method is then used in the high-energy modal features to remove the background noise coverage of the high-frequency components. In the fault feature extraction stage, a feature encoder (FAE) is introduced in the 1D-CNN network to extract effective fault features while performing secondary reconstruction of local spatial features, addressing the problem of small sample leakage signals with few effective fault characteristics. The global average pooling layer is used instead of the fully connected layer, and the Softmax function is adopted as the classifier for fault discrimination. The performance of the proposed method was evaluated on the GPLA-12 dataset, and the fault identification accuracy is up to 95.17%. Compared with other competing methods, the method in this paper exhibits optimal performance and has broad application prospects.

Deep learning-based mechanical fault detection and diagnosis of electric motors using directional characteristics of acoustic signals

Early identification of rotating machinery faults is crucial to avoid catastrophic fail- ures upon installation. Contact-based vibration acquisition approaches are traditionally used for the purpose of machine health monitoring and end-of-line quality control. In complex working conditions, it can be difficult to perform an accurate accelerometer based vibration test. Acoustic signals (sound pressure and particle velocity) also contain important information about the operating state of mechanical equipment and can be used to detect different faults. A deep learning approach, namely, one-dimensional convolutional neural networks (1D-CNNs) can directly process raw time signals, thereby eliminating the human dependence on fault feature extraction. An experimental research study is conducted to test the proposed 1D-CNN methodology on three different electric motor faults. The results from the study indicate that the fault detection performance from the acoustic-based measurement method is very effective and thus can be a good replacement to the conventional accelerometer-based methods for detection and diagnosis of mechanical faults in electric motors.

Relative divergence of mobbing calls and songs structures in passerine birds

Divergence and convergence in acoustic signals may be driven by different processes. Uncertainty about which of these processes best explains the patterns of acoustic variation between species has fuelled a long-standing debate in evolutionary biology. In particular, the features of acoustic signals are expected to vary according to their functional support. To compare the relative divergence of vocalizations according to their function, we examine two types of signals within 23 bird species, whilst controlling for species size, phylogeny and within-species variation: (i) mobbing calls emitted to recruit both conspecifics and heterospecifics against a predator and (ii) territorial songs, a signal playing an important role in sexual and social communication. We found that divergence of acoustic features varies according to their function (mobbing calls vs. territorial songs). Furthermore, species size influences spectral features, while phylogeny explained acoustic variation in only one of the variables measured: note richness. Finally, our results reveal that the acoustic characteristics can vary greatly within species, regardless of the vocalization type, indicating that such variations cannot be ignored when performing comparative analyses across species.