Pub Date : 2024-09-27DOI: 10.1016/j.apacoust.2024.110297
An acoustic metaslit model designed for regional sound insulation, employing waveguide modes and the directivity of openings, is introduced. This model characterizes the frequency responses of numerical simulations. The performance of metaslits, made from steel plates, is experimentally validated under diffuse sound field conditions and assessed by measuring acoustic power differences using intensity probes. The frequency response of single and multiple-unit metaslits shows peaks at multiples of the target frequencies. Additionally, experimental results demonstrate that the three-dimensional structure does not influence the two-dimensional regional sound insulation phenomenon. Furthermore, comparisons between experimental and numerical results indicate that obstacles near the outlets of metaslits can impact their performance.
{"title":"Acoustic metaslit for regional sound insulation for a three-dimensional diffuse sound field incidence","authors":"","doi":"10.1016/j.apacoust.2024.110297","DOIUrl":"10.1016/j.apacoust.2024.110297","url":null,"abstract":"<div><div>An acoustic metaslit model designed for regional sound insulation, employing waveguide modes and the directivity of openings, is introduced. This model characterizes the frequency responses of numerical simulations. The performance of metaslits, made from steel plates, is experimentally validated under diffuse sound field conditions and assessed by measuring acoustic power differences using intensity probes. The frequency response of single and multiple-unit metaslits shows peaks at multiples of the target frequencies. Additionally, experimental results demonstrate that the three-dimensional structure does not influence the two-dimensional regional sound insulation phenomenon. Furthermore, comparisons between experimental and numerical results indicate that obstacles near the outlets of metaslits can impact their performance.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.apacoust.2024.110313
To address the phase disturbance issue faced by sensor arrays in practical applications, a cascaded deep convolutional neural network structure is proposed to achieve direction-of-arrival (DOA) estimation for motion coprime arrays. Firstly, the synthesized covariance matrix obtained after motion is inputted into the first-level network for estimating the phase disturbance matrix. Then, we analyze the impact of phase perturbation on the synthesized covariance matrix and utilize the estimated disturbance phase to obtain an undisturbed synthesized covariance matrix. Finally, after phase compensation, the synthesized covariance matrix performs DOA estimation through the second-level network. Furthermore, to acquire three times the virtual unique lags of the coprime array, the synthesis condition about moving distance and unique lags is derived. The proposed method is shown to be effective and superior through the experiment results.
针对传感器阵列在实际应用中面临的相位干扰问题，提出了一种级联深度卷积神经网络结构，以实现运动共轭阵列的到达方向（DOA）估计。首先，将运动后获得的合成协方差矩阵输入一级网络，用于估计相位干扰矩阵。然后，分析相位扰动对合成协方差矩阵的影响，并利用估算出的扰动相位获得不受扰动的合成协方差矩阵。最后，经过相位补偿后，合成协方差矩阵通过二级网络执行 DOA 估计。此外，为了获得三次共轭阵列的虚拟唯一滞后，还推导出了移动距离和唯一滞后的合成条件。实验结果表明，所提出的方法是有效和优越的。
{"title":"Motion coprime array-based DOA estimation considering phase disturbance of sensor array","authors":"","doi":"10.1016/j.apacoust.2024.110313","DOIUrl":"10.1016/j.apacoust.2024.110313","url":null,"abstract":"<div><div>To address the phase disturbance issue faced by sensor arrays in practical applications, a cascaded deep convolutional neural network structure is proposed to achieve direction-of-arrival (DOA) estimation for motion coprime arrays. Firstly, the synthesized covariance matrix obtained after motion is inputted into the first-level network for estimating the phase disturbance matrix. Then, we analyze the impact of phase perturbation on the synthesized covariance matrix and utilize the estimated disturbance phase to obtain an undisturbed synthesized covariance matrix. Finally, after phase compensation, the synthesized covariance matrix performs DOA estimation through the second-level network. Furthermore, to acquire three times the virtual unique lags of the coprime array, the synthesis condition about moving distance and unique lags is derived. The proposed method is shown to be effective and superior through the experiment results.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.apacoust.2024.110312
Surface acoustic waves (SAW) are adequate regarding material characterization because they have low geometric attenuation compared to bulk waves. SAW can be generated easily by normal excitation using contact transducers or power lasers and have also a unique elliptic polarization, characterized by two parameters: the ellipticity (H/V) ratio between the horizontal and the vertical components of the elliptic motions and the orientation angle () between the horizontal axis of the ellipse and the surface. In the case of a viscoelastic isotropic material, a complete characterization is achieved by the association of the quantitative measurement of the polarization and the propagative characteristics, the complex wavenumber, of the SAW. In practice, this operation is performed using 3D lased vibrometry for propagation monitoring in space and time. The post-processing is carried out by Quaternion Fourier Transform, the Prony algorithm and the complex Lamé coefficients identification for the theoretical model of propagation on the material. Good agreement is observed between the obtained results and the ones of the pulse-echo method.
{"title":"Ellipsometry of surface acoustic waves using 3D vibrometry for viscoelastic material characterization by the estimation of complex Lamé coefficients versus the frequency","authors":"","doi":"10.1016/j.apacoust.2024.110312","DOIUrl":"10.1016/j.apacoust.2024.110312","url":null,"abstract":"<div><div>Surface acoustic waves (SAW) are adequate regarding material characterization because they have low geometric attenuation compared to bulk waves. SAW can be generated easily by normal excitation using contact transducers or power lasers and have also a unique elliptic polarization, characterized by two parameters: the ellipticity (<em>H/V</em>) ratio between the horizontal and the vertical components of the elliptic motions and the orientation angle (<span><math><mrow><mi>θ</mi></mrow></math></span>) between the horizontal axis of the ellipse and the surface. In the case of a viscoelastic isotropic material, a complete characterization is achieved by the association of the quantitative measurement of the polarization and the propagative characteristics, the complex wavenumber, of the SAW. In practice, this operation is performed using 3D lased vibrometry for propagation monitoring in space and time. The post-processing is carried out by Quaternion Fourier Transform, the Prony algorithm and the complex Lamé coefficients identification for the theoretical model of propagation on the material. Good agreement is observed between the obtained results and the ones of the pulse-echo method.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.apacoust.2024.110310
This study investigated the prevalence of voice disorders among healthcare workers in China and Indonesia. A questionnaire was administered to 721 healthcare workers, with 411 from China and 310 from Indonesia during the COVID-19 pandemic. The questionnaire assessed vocal-related symptoms, perceived voice handicap, frequently heard noise sources, and the quality of communications. The results revealed that the most frequently reported symptoms were ‘voice tiredness’ (20.5 %) and ‘clearing the throat’ (18.3 %). Specifically, 19.4 % of Chinese healthcare workers experienced voice-related symptoms, whereas only 6.3 % of Indonesians reported such symptoms. Longer working hours and working years were significantly associated with voice-related symptoms. Furthermore, the healthcare workers in China and Indonesia both reported mild (<30) voice disorders based on the Voice Handicap Index (VHI-30). Approximately one-fourth (23.8 %) of healthcare workers in China had VHI-30 scores exceeding 30, compared to only 7 % in Indonesia. The differences in vocal-related symptoms and voice handicap between the two countries were significant. Healthcare workers with 50–60 working hours per week exhibited higher scores than those working 40–50 h per week in both countries. Additionally, communication quality and the severity of voice problems for healthcare workers in China were worse than in Indonesia during the pandemic.
{"title":"Voice handicap prevalence among healthcare workers in China and Indonesia","authors":"","doi":"10.1016/j.apacoust.2024.110310","DOIUrl":"10.1016/j.apacoust.2024.110310","url":null,"abstract":"<div><div>This study investigated the prevalence of voice disorders among healthcare workers in China and Indonesia. A questionnaire was administered to 721 healthcare workers, with 411 from China and 310 from Indonesia during the COVID-19 pandemic. The questionnaire assessed vocal-related symptoms, perceived voice handicap, frequently heard noise sources, and the quality of communications. The results revealed that the most frequently reported symptoms were ‘voice tiredness’ (20.5 %) and ‘clearing the throat’ (18.3 %). Specifically, 19.4 % of Chinese healthcare workers experienced voice-related symptoms, whereas only 6.3 % of Indonesians reported such symptoms. Longer working hours and working years were significantly associated with voice-related symptoms. Furthermore, the healthcare workers in China and Indonesia both reported mild (<30) voice disorders based on the Voice Handicap Index (VHI-30). Approximately one-fourth (23.8 %) of healthcare workers in China had VHI-30 scores exceeding 30, compared to only 7 % in Indonesia. The differences in vocal-related symptoms and voice handicap between the two countries were significant. Healthcare workers with 50–60 working hours per week exhibited higher scores than those working 40–50 h per week in both countries. Additionally, communication quality and the severity of voice problems for healthcare workers in China were worse than in Indonesia during the pandemic.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.apacoust.2024.110309
Rolling bearings are extensively employed in industrial production as essential support components for rotating machinery. However, under conditions of high load and harsh operation, bearings are highly susceptible to failure. The weak vibration signals associated with these failures may be obscured by complex harmonic interference and strong noise, posing challenges for the accurate diagnosis of rolling bearing failures. In this paper, an autoregressive integrated moving average and competitive K-singular value decomposition (ARIMA-CK-SVD) algorithm is proposed to realize effective extraction of faulty pulse signals in a strong interference environment. First, the ARIMA model is used to preprocess the original signal to eliminate the interference of harmonic components. Second, a method is proposed for the adaptive selection of parameters in the ARIMA model, with consideration given to the characteristics of K-SVD. Subsequently, a competitive mechanism is introduced during the dictionary update phase of the algorithm to adjust the pattern of atomic updates and eliminate noise atoms. Finally, the effectiveness of the ARIMA-CK-SVD has been validated through simulation experiments and engineering tests.
{"title":"A denoising algorithm based on ARIMA and competitive K-SVD for the diagnosis of rolling bearing faults","authors":"","doi":"10.1016/j.apacoust.2024.110309","DOIUrl":"10.1016/j.apacoust.2024.110309","url":null,"abstract":"<div><div>Rolling bearings are extensively employed in industrial production as essential support components for rotating machinery. However, under conditions of high load and harsh operation, bearings are highly susceptible to failure. The weak vibration signals associated with these failures may be obscured by complex harmonic interference and strong noise, posing challenges for the accurate diagnosis of rolling bearing failures. In this paper, an autoregressive integrated moving average and competitive K-singular value decomposition (ARIMA-CK-SVD) algorithm is proposed to realize effective extraction of faulty pulse signals in a strong interference environment. First, the ARIMA model is used to preprocess the original signal to eliminate the interference of harmonic components. Second, a method is proposed for the adaptive selection of parameters in the ARIMA model, with consideration given to the characteristics of K-SVD. Subsequently, a competitive mechanism is introduced during the dictionary update phase of the algorithm to adjust the pattern of atomic updates and eliminate noise atoms. Finally, the effectiveness of the ARIMA-CK-SVD has been validated through simulation experiments and engineering tests.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.apacoust.2024.110307
Background and Objective
Parkinson’s Disease (PD) is one of the most commonly observed neurodegenerative disorders worldwide. Many researchers have utilized machine learning (ML) models to detect PD and understand its underlying causes automatically. In this research, our primary objective is to automatically detect PD and extract meaningful results using the proposed ML model.
Materials and Methods
In this study, an FNIRS dataset collected from PD patients and control participants under three conditions—(i) rest, (ii) walking, and (iii) finger tapping—was utilized. A new explainable feature engineering (XFE) model was proposed to detect PD and automatically extract meaningful information under these conditions. The XFE model consists of four main phases: (i) feature extraction using the proposed channel transformation and transition pattern (TPat), (ii) feature selection employing cumulative weighted neighborhood component analysis (CWNCA), (iii) classification using the k-nearest neighbors (kNN) classifier, and (iv) channel network extraction to obtain explainable results.
Results
The suggested TPat-based XFE model was applied to the FNIRS dataset. This dataset included three distinct cases. Our model achieved over 94% classification accuracy using leave-one-subject-out cross-validation (LOSO CV) and 100% classification accuracy using 10-fold cross-validation. Additionally, channel transitions for each case were identified and discussed.
Conclusions
Based on the results and findings, the proposed model demonstrated high accuracy in FNIRS signal classification and provided explainable results. In this regard, the presented TPat-based XFE model contributed significantly to both ML and neuroscience.
{"title":"TPat: Transition pattern feature extraction based Parkinson’s disorder detection using FNIRS signals","authors":"","doi":"10.1016/j.apacoust.2024.110307","DOIUrl":"10.1016/j.apacoust.2024.110307","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Parkinson’s Disease (PD) is one of the most commonly observed neurodegenerative disorders worldwide. Many researchers have utilized machine learning (ML) models to detect PD and understand its underlying causes automatically. In this research, our primary objective is to automatically detect PD and extract meaningful results using the proposed ML model.</div></div><div><h3>Materials and Methods</h3><div>In this study, an FNIRS dataset collected from PD patients and control participants under three conditions—(i) rest, (ii) walking, and (iii) finger tapping—was utilized. A new explainable feature engineering (XFE) model was proposed to detect PD and automatically extract meaningful information under these conditions. The XFE model consists of four main phases: (i) feature extraction using the proposed channel transformation and transition pattern (TPat), (ii) feature selection employing cumulative weighted neighborhood component analysis (CWNCA), (iii) classification using the k-nearest neighbors (kNN) classifier, and (iv) channel network extraction to obtain explainable results.</div></div><div><h3>Results</h3><div>The suggested TPat-based XFE model was applied to the FNIRS dataset. This dataset included three distinct cases. Our model achieved over 94% classification accuracy using leave-one-subject-out cross-validation (LOSO CV) and 100% classification accuracy using 10-fold cross-validation. Additionally, channel transitions for each case were identified and discussed.</div></div><div><h3>Conclusions</h3><div>Based on the results and findings, the proposed model demonstrated high accuracy in FNIRS signal classification and provided explainable results. In this regard, the presented TPat-based XFE model contributed significantly to both ML and neuroscience.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.apacoust.2024.110317
While the low weight and high bending stiffness of cross-laminated timber (CLT) are key to its popularity, these properties also contribute to poor acoustic performance. Notably flanking sound transmission is a critical factor, driving the need for vibration reduction solutions such as resilient interlayers in the junction design. However, due to the complex material behavior of CLT and resilient interlayers, the improvement related to these solutions is difficult to predict. In this research, an analytical model with low computational cost is developed to evaluate the vibration reduction index for CLT junctions with resilient interlayers. The CLT panels are considered as thin orthotropic plates with homogenized material properties. Three potential material models are proposed for the interlayer: it is considered as a thin plate, a thick flexible layer with out-of-plane motion governed by shear or distributed springs. The prediction model is experimentally validated for junctions consisting of CLT panels, with and without resilient interlayers. For junctions without interlayers, the predicted and experimentally determined vibration reduction index correspond most closely when the junction is realized with stiff connectors. In this case, the predictions are moderately accurate with deviations below 5 dB in 1/3 octave bands up to approximately 2000 Hz for both corner and coplanar transmission paths. For junctions with resilient interlayers, the shear interlayer model exhibits the best performance with deviations of less than 5 dB in most 1/3 octave bands. For frequencies below 1000 Hz, the accuracy of the simplified spring model is comparable to that of the thick layer model. Simulations with equivalent isotropic material parameters yield slightly inferior predictions than for orthotropic parameters if the degree of orthotropy of the panels is high.
{"title":"Prediction of flanking sound transmission through cross-laminated timber junctions with resilient interlayers","authors":"","doi":"10.1016/j.apacoust.2024.110317","DOIUrl":"10.1016/j.apacoust.2024.110317","url":null,"abstract":"<div><div>While the low weight and high bending stiffness of cross-laminated timber (CLT) are key to its popularity, these properties also contribute to poor acoustic performance. Notably flanking sound transmission is a critical factor, driving the need for vibration reduction solutions such as resilient interlayers in the junction design. However, due to the complex material behavior of CLT and resilient interlayers, the improvement related to these solutions is difficult to predict. In this research, an analytical model with low computational cost is developed to evaluate the vibration reduction index <span><math><msub><mrow><mi>K</mi></mrow><mrow><mi>i</mi><mi>j</mi></mrow></msub></math></span> for CLT junctions with resilient interlayers. The CLT panels are considered as thin orthotropic plates with homogenized material properties. Three potential material models are proposed for the interlayer: it is considered as a thin plate, a thick flexible layer with out-of-plane motion governed by shear or distributed springs. The prediction model is experimentally validated for junctions consisting of CLT panels, with and without resilient interlayers. For junctions without interlayers, the predicted and experimentally determined vibration reduction index <span><math><msub><mrow><mi>K</mi></mrow><mrow><mi>i</mi><mi>j</mi></mrow></msub></math></span> correspond most closely when the junction is realized with stiff connectors. In this case, the predictions are moderately accurate with deviations below 5 dB in 1/3 octave bands up to approximately 2000 Hz for both corner and coplanar transmission paths. For junctions with resilient interlayers, the shear interlayer model exhibits the best performance with deviations of less than 5 dB in most 1/3 octave bands. For frequencies below 1000 Hz, the accuracy of the simplified spring model is comparable to that of the thick layer model. Simulations with equivalent isotropic material parameters yield slightly inferior predictions than for orthotropic parameters if the degree of orthotropy of the panels is high.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1016/j.apacoust.2024.110304
In this study is presented a method for predicting the acoustic characteristics of microspeakers with mesh-type ventilation materials that combines specific airflow resistance with equivalent circuit model. The specific airflow resistances of eight types of ventilation materials were quantified using a self-made measurement system. These materials were then used to simulate the acoustic properties of 40 φ and 20 φ microspeakers, respectively. The results were compared with simulation results obtained using Dr. Maa’s porous plate acoustic impedance and actual measurements. After applying a stable correction factor, the self-made measurement system accurately determined specific airflow resistance values with high reproducibility and stability. A regression curve based on scanning electron microscope obtained porosity and measured specific airflow resistance effectively predicted the specific airflow resistance of materials with known porosity. Applying these values to acoustic simulations, the proposed method significantly improved accuracy, outperforming Dr. Maa’s method by a factor of six, and closely matched actual measurements. This innovative approach is versatile and applicable to various conditions and types of ventilation materials, as well as enhances predictions of their impact on electro-acoustic product performance.
本研究提出了一种结合比气流阻力和等效电路模型的方法，用于预测使用网状通风材料的微型扬声器的声学特性。使用自制的测量系统对八种通风材料的特定气流阻力进行了量化。然后用这些材料分别模拟了 40 φ 和 20 φ 微型扬声器的声学特性。结果与使用 Maa 博士的多孔板声阻抗模拟结果和实际测量结果进行了比较。在应用稳定的修正系数后，自制的测量系统准确地测定了具体的气流阻力值，具有很高的再现性和稳定性。基于扫描电子显微镜获得的孔隙率和测量的比气流阻力的回归曲线有效地预测了已知孔隙率材料的比气流阻力。将这些数值应用于声学模拟，所提出的方法显著提高了准确性，比 Maa 博士的方法高出六倍，并且与实际测量结果非常接近。这种创新方法用途广泛，适用于各种条件和类型的通风材料，并能增强对其电声产品性能影响的预测。
{"title":"Study on the predictions of acoustic characteristics of microspeakers based on the specific airflow resistance of ventilation materials: Mesh type","authors":"","doi":"10.1016/j.apacoust.2024.110304","DOIUrl":"10.1016/j.apacoust.2024.110304","url":null,"abstract":"<div><div>In this study is presented a method for predicting the acoustic characteristics of microspeakers with mesh-type ventilation materials that combines specific airflow resistance with equivalent circuit model. The specific airflow resistances of eight types of ventilation materials were quantified using a self-made measurement system. These materials were then used to simulate the acoustic properties of 40 φ and 20 φ microspeakers, respectively. The results were compared with simulation results obtained using Dr. Maa’s porous plate acoustic impedance and actual measurements. After applying a stable correction factor, the self-made measurement system accurately determined specific airflow resistance values with high reproducibility and stability. A regression curve based on scanning electron microscope obtained porosity and measured specific airflow resistance effectively predicted the specific airflow resistance of materials with known porosity. Applying these values to acoustic simulations, the proposed method significantly improved accuracy, outperforming Dr. Maa’s method by a factor of six, and closely matched actual measurements. This innovative approach is versatile and applicable to various conditions and types of ventilation materials, as well as enhances predictions of their impact on electro-acoustic product performance.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-24DOI: 10.1016/j.apacoust.2024.110306
This paper presents a comprehensive investigation, comprised of analytical, experimental and numerical approaches, into the interaction between a water cavity of varying fluid cavity height and the vibration of a thin circular plate subjected to its hydro-static pressure. We extend the application of the strong modal coupling method to derive a solution for this unexplored physical problem by utilising classic plate theory, Fourier-Bessel series formulation and the uncoupled solution of a thin clamped circular plate with uniform radial tension. Using a set of geometric and physical properties for the system, the resonance frequencies, response functions and non-dimensionalised added virtual mass incremental (NAVMI) factors are calculated and investigated as a function of hydro-static pressure and fluid cavity height, providing novel fundamental insights into the physical system. We construct an experimental rig to embody the conditions of the analytical investigation for the purpose of validation and to uncover experimental insights into the physical problem. Finite element analysis (FEA), employing modal analysis and transient dynamic analysis, is used to further validate and extend the analytical insights while more accurately mirroring the experimental conditions. The response functions, resonance frequencies and NAVMI factors and their dependence on the cavity pressure were experimentally measured and numerically simulated, with direct comparisons made with the analytical model. A high degree of accuracy for the analytical model is validated, along with its ability to describe the underlying physical phenomena. The validated analytical model is then leveraged to perform fundamental explorations into the modal compositions of the coupled system modes as a function of cavity height and hydro-pressure, the deformation of the coupled system mode shapes and a parametric sensitivity analysis on the effects of plate radii and plate thickness on the coupled system resonance frequencies and NAVMI factors. In totality, this study provides detailed modelling and prediction of the frequency response, resonance frequencies, added mass factors, modal contributions, and deformation of the coupled mode shapes, offering comprehensive insights with wide applicability.
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Pub Date : 2024-09-24DOI: 10.1016/j.apacoust.2024.110298
Stochastic resonance (SR) is highly favored for early mechanical fault signal extraction because it enhances weak features using external noise. However, traditional SR models suffer from output saturation. In this paper, a novel second-order hybrid multi-stable stochastic resonance (SHMSR) system is developed, where the sigmoid function is first employed to construct a continuous unsaturated multi-stable potential function. Then, system performance is theoretically evaluated by the steady-state probability density function under different external forces based on adiabatic approximation theory. Furthermore, the input energy is used as an index to measure the influence of system internal parameters on SR performance, which not only simplifies the metrics of stochastic processes in complex systems but also explains the physical nature of parameter-induced SR. Finally, an adaptive weak signal detection method is proposed to demonstrate the superiority of the novel system in practical engineering. Simulations and experiments with different noise conditions, rotational speeds and fault characteristics regarding the detection and enhancement of weak bearing fault signals under strong background noise are conducted to evaluate the adaptability and robustness of the novel method. The results demonstrate that SHMSR can effectively improving the signal-to-noise ratio (SNR) and amplifying the amplitude of the feature signal in early mechanical fault signal extraction.
随机共振（SR）非常适用于早期机械故障信号提取，因为它能利用外部噪声增强微弱特征。然而，传统的随机共振模型存在输出饱和的问题。本文开发了一种新型二阶混合多稳定随机共振（SHMSR）系统，首先利用 sigmoid 函数构建连续的非饱和多稳定势函数。然后，根据绝热近似理论，通过不同外力作用下的稳态概率密度函数对系统性能进行理论评估。此外，输入能量被用作衡量系统内部参数对 SR 性能影响的指标，这不仅简化了复杂系统中随机过程的度量，还解释了参数诱导 SR 的物理本质。最后，提出了一种自适应弱信号检测方法，以证明新系统在实际工程中的优越性。针对强背景噪声下轴承微弱故障信号的检测和增强，在不同噪声条件、转速和故障特征下进行了模拟和实验，以评估新方法的适应性和鲁棒性。结果表明，在早期机械故障信号提取中，SHMSR 能有效提高信噪比（SNR）并放大特征信号的振幅。
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