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29 pages, 523 KB

Open AccessArticle

A General Tensorial Formulation of Acoustoelasticity and Its Representation in Cylindrical Coordinates

by Yongjiang Ma, Chunguang Xu, Shuangxu Yang and Changhong Chen

Sensors 2026, 26(10), 3218; https://doi.org/10.3390/s26103218 - 19 May 2026

Viewed by 257

Abstract

Acoustoelasticity provides the physical sensing principle for ultrasonic stress measurement. However, most existing formulations are restricted to isotropic media, simple stress conditions, and Cartesian coordinate systems, which limits their applicability in practical sensing scenarios involving curved and anisotropic structures. In this work, a [...] Read more.

Acoustoelasticity provides the physical sensing principle for ultrasonic stress measurement. However, most existing formulations are restricted to isotropic media, simple stress conditions, and Cartesian coordinate systems, which limits their applicability in practical sensing scenarios involving curved and anisotropic structures. In this work, a general tensorial formulation of acoustoelasticity is developed based on the theory of incremental deformation. The proposed governing equations describe the motion of incremental displacement with explicit dependence on initial stress or strain, and are applicable to materials with arbitrary symmetry and general initial stress states. Owing to its coordinate-independent tensorial nature, the formulation can be expressed in any curvilinear coordinate system. To facilitate practical ultrasonic sensing applications, the general equations are further expanded in a cylindrical coordinate system for orthotropic materials. This enables the analysis of elastic wave propagation in curved structures such as pipelines, pressure vessels, and boreholes. The formulation establishes a direct relationship between initial stress and effective elastic properties, which determine wave velocities measurable by ultrasonic sensors, such as time-of-flight and phase velocity. The proposed approach provides a rigorous theoretical foundation for ultrasonic stress sensing and nondestructive testing, particularly for curved and anisotropic structures, and supports improved accuracy in sensor-based stress evaluation. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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16 pages, 2850 KB

Open AccessArticle

Synthetic Spectrogram Augmentation via Semi-Supervised WGAN-GP for Acoustic Industrial Quality Inspection of Turbine Housings Under Extreme Data Scarcity

by Ander Gracia Moisés, Óscar Del Barrio Farran, David Martinez García and María Puy Zudaire Latienda

Sensors 2026, 26(10), 3052; https://doi.org/10.3390/s26103052 - 12 May 2026

Viewed by 413

Abstract

Impact-based acoustic inspection provides a rapid non-destructive approach for screening metallic components by analyzing the sound radiated after a controlled mechanical excitation. However, the limited availability of labeled data from defective parts remains a major challenge for deploying deep learning classifiers in production. [...] Read more.

Impact-based acoustic inspection provides a rapid non-destructive approach for screening metallic components by analyzing the sound radiated after a controlled mechanical excitation. However, the limited availability of labeled data from defective parts remains a major challenge for deploying deep learning classifiers in production. This paper proposes a complete pipeline that converts raw impact-response audio recordings into magnitude log-spectrogram images and trains a semi-supervised Wasserstein GAN with gradient penalty (SS-WGAN-GP) designed to operate under extreme data scarcity. The architecture couples a shared convolutional backbone with two output heads: a Wasserstein critic for unsupervised discrimination between real and generated samples, and a binary classification head for supervised quality labeling, jointly optimized through a combined loss that balances Wasserstein distance, gradient penalty, and cross-entropy. A key property of the design is that the generator acts as a source of synthetic training samples, producing progressively more realistic spectrograms as training advances. These samples, in turn, enrich the feature representations learned by the shared backbone and improve the performance of the classification head. The classification head of the trained discriminator is deployed directly as the quality classifier, without requiring external data or post hoc retraining. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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19 pages, 940 KB

Open AccessArticle

Hydraulic Seal Wear Classification by Fine-Tuning a Transformer-Based Audio Model Using Acoustic Emission

by Lisa Maria Svendsen, Vignesh V. Shanbhag and Rune Schlanbusch

Sensors 2026, 26(9), 2856; https://doi.org/10.3390/s26092856 - 2 May 2026

Viewed by 1532

Abstract

Accurate classification of seal wear is essential for condition-based and predictive maintenance of hydraulic cylinders, where seal degradation can cause fluid leakage and impair normal system operation. This study investigates the adaptation of a Transformer-based audio model for classifying seal wear conditions using [...] Read more.

Accurate classification of seal wear is essential for condition-based and predictive maintenance of hydraulic cylinders, where seal degradation can cause fluid leakage and impair normal system operation. This study investigates the adaptation of a Transformer-based audio model for classifying seal wear conditions using acoustic emission (AE) signals. Specifically, we adapt the Audio Spectrogram Transformer (AST), a convolution-free, purely attention-based model that operates directly on audio spectrograms. The Transformer architecture enables the modeling of long-range dependencies, while the model learns discriminative representations directly from AE data without relying on manually engineered features. A selective fine-tuning strategy was implemented by adding layer-freezing functionality to the AST training pipeline, enabling different freezing configurations during fine-tuning. This allowed earlier pretrained representations to be preserved while adapting the later layers to the target AE signals, thereby reducing the risk of overfitting in the small-data setting. In addition, validation-driven early stopping was implemented to further improve generalization during fine-tuning. The model was initialized with ImageNet and AudioSet pretrained weights to exploit general-purpose representations learned from large-scale datasets. The AE data were acquired under varying pressure conditions on a hydraulic test rig designed to simulate hydraulic cylinder leakage. The datasets were partitioned into fine-tuning, validation, and evaluation subsets and labeled into three wear states: unworn, semi-worn, and worn. In addition, data augmentation techniques were applied to the fine-tuning data to increase diversity and mitigate class imbalance. The adapted model achieved 97.92% classification accuracy across all wear conditions and pressure settings, demonstrating its ability to learn discriminative wear-related patterns directly from AE data. Furthermore, the framework’s versatility was further assessed on a bearing strip dataset acquired from the same hydraulic test rig. Using the same fine-tuning configuration, the model achieved 95.65% accuracy and 100% recall for the worn state. These findings highlight the potential of transformer-based architectures for data-efficient, end-to-end AE-based diagnostics across hydraulic system components. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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25 pages, 20567 KB

Open AccessArticle

A Multi-Indicator Fusion-Based Technique for the Identification of Acoustic Emission Signals During Rock Failure

by Dexian Li, Hongwei Wang, Dengyu Wang, Xuemei Wang, Lianhui Li, Zhongwei Pei and Ying Wang

Sensors 2026, 26(9), 2759; https://doi.org/10.3390/s26092759 - 29 Apr 2026

Viewed by 369

Abstract

With the widespread application of acoustic emission (AE) technology in geotechnical engineering, effectively separating and identifying dense AE signals generated during rock fracturing remains a critical challenge. This study proposes an AE event identification technique based on waveform energy envelopes and multi-indicator characteristic [...] Read more.

With the widespread application of acoustic emission (AE) technology in geotechnical engineering, effectively separating and identifying dense AE signals generated during rock fracturing remains a critical challenge. This study proposes an AE event identification technique based on waveform energy envelopes and multi-indicator characteristic parameters. First, the waveform energy envelope is used to adaptively segment dense and partially overlapping AE waveforms without relying on fixed timing parameters. Then, a template sliding-window scan integrating waveform correlation, ring count, rise time, and signal energy is performed to identify candidate AE events. In addition, a time-difference correction and window-stacking strategy is adopted to improve multi-channel arrival picking. Experimental validation on representative single-peak single-event and double-peak multi-waveform cases extracted from laboratory rock-failure tests demonstrates that the proposed method can effectively separate and identify AE waveforms under the tested conditions. Compared with conventional timing-parameter-based segmentation and correlation-dominated matching, the proposed workflow is more robust to waveform attenuation and distortion. The method provides a methodological basis for AE waveform identification and arrival-time extraction in rock-failure monitoring and has potential to support early warning after further validation. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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23 pages, 5045 KB

Open AccessArticle

Numerical Analysis of Curvature Effects on Acoustoelastic Surface Waves in Cylindrical Structures

by Yongjiang Ma, Chunguang Xu, Changhong Chen and Shuangxu Yang

Sensors 2026, 26(4), 1206; https://doi.org/10.3390/s26041206 - 12 Feb 2026

Viewed by 490

Abstract

In this study, the influence of axial stress on surface wave propagation along cylindrical surfaces is investigated, with particular emphasis on quantifying curvature effects on acoustoelastic coefficients. The classical planar surface wave acoustoelastic formulation is first adopted as a reference. Three-dimensional transient finite [...] Read more.

In this study, the influence of axial stress on surface wave propagation along cylindrical surfaces is investigated, with particular emphasis on quantifying curvature effects on acoustoelastic coefficients. The classical planar surface wave acoustoelastic formulation is first adopted as a reference. Three-dimensional transient finite element simulations are then performed to model surface wave excitation, propagation, and reception on aluminum cylinders with different radii and excitation frequencies. Stress-free simulations are used to extract surface wave velocities and reference time signals, while prestressed simulations provide stress-induced time delays, from which effective acoustoelastic coefficients are determined. The results indicate that both the surface wave velocity and the acoustoelastic coefficient exhibit clear dependencies on cylinder radius and excitation frequency. Curvature effects are especially pronounced at low frequencies, whereas at higher frequencies the coefficients corresponding to different radii tend to converge. These findings demonstrate that planar surface wave theory may lead to non-negligible errors when applied to cylindrical geometries and provide quantitative guidance for curvature-aware stress evaluation. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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21 pages, 4862 KB

Open AccessArticle

Resonant Acoustic Spectroscopy for Measuring Complex Modulus of Bitumen

by Frederik A. Kollmus, Lucas Sassaki Vieira da Silva and Michael P. Wistuba

Sensors 2026, 26(2), 720; https://doi.org/10.3390/s26020720 - 21 Jan 2026

Viewed by 423

Abstract

The complex modulus is one of the intrinsic properties of bituminous materials, and, hence, is of importance for their rheological characterization. It was shown by various authors that the complex modulus of asphalt mixtures can be calculated from dynamic modulus measurements using the [...] Read more.

The complex modulus is one of the intrinsic properties of bituminous materials, and, hence, is of importance for their rheological characterization. It was shown by various authors that the complex modulus of asphalt mixtures can be calculated from dynamic modulus measurements using the Resonant Acoustic Spectroscopy (RAS). This paper extends the RAS technique to bitumen. For the purpose of validation, rheological data for the same bitumen are also derived from standard Dynamic Shear Rheometer (DSR) tests, and the master curves resulting from both methods are compared. The laboratory programme comprised a temperature range from −30 °C to 20 °C, and four different bitumens in unaged and aged condition, resulting in 36 different test variants. RAS successfully characterizes the complex modulus of bitumen and reflects temperature and ageing effects, with good agreement to DSR results at low temperatures. At higher temperatures, viscosity and damping introduce deviations, indicating that RAS is effective for modulus evaluation but not sufficient for complete master curve development. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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13 pages, 836 KB

Open AccessArticle

Testing the Reliability of a Procedure Using Shear-Wave Elastography for Measuring Longus Colli Muscle Stiffness

by Juan Izquierdo-García, Juan Antonio Valera-Calero, Marcos José Navarro-Santana, Ibai López-de-Uralde-Villanueva, Gabriel Rabanal-Rodríguez, María Paz Sanz-Ayán, Juan Ignacio Castillo-Martín and Gustavo Plaza-Manzano

Sensors 2026, 26(1), 65; https://doi.org/10.3390/s26010065 - 22 Dec 2025

Viewed by 1028

Abstract

Background: Objective, reproducible assessment of deep cervical muscle mechanics is clinically relevant, yet the reliability of shear-wave elastography (SWE) for the longus colli (LC) has not been established. Therefore, the aim of this study was to determine intra- and inter-examiner reliability of LC [...] Read more.

Background: Objective, reproducible assessment of deep cervical muscle mechanics is clinically relevant, yet the reliability of shear-wave elastography (SWE) for the longus colli (LC) has not been established. Therefore, the aim of this study was to determine intra- and inter-examiner reliability of LC stiffness measured by SWE under a tightly standardized protocol in patients with mechanical neck pain. Methods: A longitudinal reliability study was conducted. Adults suffering from neck pain for ≥6 months were recruited. Two examiners (with different levels of experience) acquired bilateral LC images using fixed presets. The SWE region of interest covered the full muscle thickness (excluding fascia) to measure the LC shear-wave speed and Young’s modulus. Intraclass correlation coefficients (ICCs), standard error of measurement and minimal detectable changes were computed. Results: Nineteen participants with neck pain completed imaging (left and right sides analyzed). Inter-examiner agreement was good to excellent for single measurements (ICC_3,2 > 0.818) and improved when averaging two acquisitions (ICC_3,2 > 0.866). Intra-examiner repeatability was good to excellent for the novel examiner (ICC_3,1 > 0.891) and excellent for the experienced examiner (ICC_3,1 > 0.973). No meaningful stiffness differences by sex or side were observed in this sample (p > 0.05). Conclusions: A standardized SWE workflow yields reproducible LC stiffness measurements in mechanical neck pain. For longitudinal use, keep a single operator when feasible; in multi-examiner settings, average at least two acquisitions per side to enhance sensitivity to true change. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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19 pages, 5586 KB

Open AccessArticle

Condition Monitoring System for Planetary Journal Bearings in Wind Turbines Based on Surface Acoustic Wave Measurements—Validation on a System Level

by Thomas Matthias Decker, Georg Jacobs, Tim Scholz, Julian Röder, Martin Knops, Julian Blumenthal and Tobias Bauer

Sensors 2026, 26(1), 58; https://doi.org/10.3390/s26010058 - 21 Dec 2025

Viewed by 1601

Abstract

Planetary journal bearings are enablers for wind turbine gearbox torque density and reliability increase due to their compactness and potentially unlimited lifetime. They are designed to withstand the load conditions during wind turbine operation. Despite their general robustness, abnormal events such as particle [...] Read more.

Planetary journal bearings are enablers for wind turbine gearbox torque density and reliability increase due to their compactness and potentially unlimited lifetime. They are designed to withstand the load conditions during wind turbine operation. Despite their general robustness, abnormal events such as particle contamination, strong overload or operation without sufficient oil supply may be harmful to the bearings. In these cases, damage can occur quickly and with little warning time. Such spontaneous failure leads to turbine downtime and cost-intensive repair work on the wind turbine drive train. Thus, reliable load and condition monitoring systems, which allow the detection of critical operating states before damage occurs, would be beneficial. For journal bearings in wind turbine gearboxes, no commercially available monitoring system exists to date. The existing studies on journal bearing condition monitoring are limited to experiments on component test rigs or small gearboxes, and their transferability to full-size systems has yet to be proven. This work presents the results of a system test with an 850 kW wind turbine gearbox equipped with planetary journal bearings and a novel condition monitoring system based on the measurement of surface acoustic waves. First, the journal bearing design, including the sensor setup, is explained. Second, the test campaign layout is presented. The gearbox is tested under load conditions specific to wind turbines, and the condition monitoring signals are examined in detail. An algorithm based on a machine learning model is presented for evaluating the monitoring signals and predicting the friction state of the bearings. Finally, the practical feasibility and quality of the monitoring approach for planetary journal bearings presented in this work is discussed. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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15 pages, 4606 KB

Open AccessArticle

A Study on the Evaluation of Ultrasonic Propagation Properties and Nonlinearity According to Temperature Changes of Aluminium Alloys for Each Aluminium Alloy by Temperature

by Junpil Park and Jaesun Lee

Sensors 2025, 25(24), 7494; https://doi.org/10.3390/s25247494 - 9 Dec 2025

Viewed by 775

Abstract

Aluminium alloys are widely used across various industrial sectors due to their suitability for enhancing structural safety and reducing weight, thereby improving operational efficiency. This study investigates the feasibility of using ultrasonic techniques as an alternative to thermistors for temperature monitoring in electric [...] Read more.

Aluminium alloys are widely used across various industrial sectors due to their suitability for enhancing structural safety and reducing weight, thereby improving operational efficiency. This study investigates the feasibility of using ultrasonic techniques as an alternative to thermistors for temperature monitoring in electric vehicle motors and batteries. The extent to which ultrasonic maximum amplitude and propagation velocity are temperature-dependent was examined, and the material nonlinearity was analyzed. Step-wedge specimens of Al3003, Al6061, and Al6063—commonly used in electric vehicle components—were fabricated with thicknesses of 4, 6, 8, 10, and 12 mm to examine thickness-dependent behavior. Although the three alloys differ in composition and mechanical properties, their ultrasonic propagation characteristics were found to be highly similar. As temperature increased, ultrasonic attenuation increased while propagation velocity decreased. For intact specimens, nonlinearity increased with temperature. However, the variation remained constant beyond a certain temperature range. In contrast, tensile-fatigued specimens showed increased nonlinearity with fatigue cycles, and the variation decreased at elevated temperatures, producing a more pronounced nonlinear response. These findings suggest that ultrasonic techniques may provide a cost-effective solution for temperature measurement and defect diagnosis, potentially replacing high-cost thermistors currently used in electric vehicles. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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19 pages, 7287 KB

Open AccessArticle

Reducing Risks in Petrochemical Plants Through the Integration of Existing and Emerging Gas Leak Detection Technologies

by Joon Hyuk Lee, Sung Yoon Lim, Jae Joon Lee, Hongjin Shin, Youngsik Kim and Inkwon Kim

Sensors 2025, 25(23), 7197; https://doi.org/10.3390/s25237197 - 25 Nov 2025

Cited by 1 | Viewed by 1445

Abstract

Leakage of flammable gases and the resulting explosions in petrochemical plants remain latent risks, capable of occurring at any moment. Therefore, to address these worst-case scenarios within a virtual reality framework, we conducted simulations aimed at predicting and effectively responding to potential damages [...] Read more.

Leakage of flammable gases and the resulting explosions in petrochemical plants remain latent risks, capable of occurring at any moment. Therefore, to address these worst-case scenarios within a virtual reality framework, we conducted simulations aimed at predicting and effectively responding to potential damages due to gas leakage. This study presents an analysis of the hazards that can lead to leaks and potential explosions in a petrochemical plant using the BREEZE Incident Analyst program. Rapid and accurate recognition of the risk associated with gas leaks, which can cause extensive damage and explosions, is of paramount importance. This study addresses two main aspects: the prediction of the consequences of gas leaks through simulations and the implementation of appropriate detection measures. Better, more efficient risk management and mitigation strategies were implemented by predicting gas leak paths using BREEZE. Using ultrasonic detection technology, detection was demonstrated to be possible in approximately one-third the time required by conventional detectors, and it is weather-insensitive. Simultaneously, considering plant characteristics such as utility configurations, we propose an additional method to prevent leaks from going undetected. This is achieved by integrating gas detection technology that combines both existing and new technologies. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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29 pages, 40108 KB

Open AccessArticle

Decomposing and Modeling Acoustic Signals to Identify Machinery Defects in Industrial Soundscapes

by Christof Pichler, Markus Neumayer, Bernhard Schweighofer, Christoph Feilmayr, Stefan Schuster and Hannes Wegleiter

Sensors 2025, 25(16), 4923; https://doi.org/10.3390/s25164923 - 9 Aug 2025

Cited by 2 | Viewed by 1474

Abstract

Acoustic sound-based condition monitoring (ASCM) systems, which typically utilize machine learning algorithms on established audio features, have demonstrated effectiveness under controlled conditions. However, their application in real-world industrial environments presents significant challenges due to complex and variable soundscapes with high noise and limited [...] Read more.

Acoustic sound-based condition monitoring (ASCM) systems, which typically utilize machine learning algorithms on established audio features, have demonstrated effectiveness under controlled conditions. However, their application in real-world industrial environments presents significant challenges due to complex and variable soundscapes with high noise and limited fault data. The presence of random interfering sounds and variability in operating conditions can lead to lower performance and high false-positive rates. To overcome these limitations, we propose a fault detection method that leverages the underlying physical characteristics of the sound signals. By investigating the components of the acoustic signal, we found that fault-related sounds can be modeled as exponentially decaying oscillations. This insight allows for the development of a physically based signal model, setting our approach apart from purely data-driven methods. Using this model, we developed a robust detection method based on a Generalized Likelihood Ratio Test (GLRT). The effectiveness of this approach was validated using both synthetic and real-world data from a steel industry facility. Our results demonstrate that the proposed model-based approach provides superior performance compared to standard audio features, particularly in high-noise conditions. On real-world data, the GLRT-based approach outperformed all audio features, as clearly shown by the Receiver Operating Characteristic (ROC) analysis. Specifically, the Partial Area Under the Curve (pAUC) of the GLRT is more than twice that of the best-performing audio feature, demonstrating good detection at significantly lower-false-positive rates compared to audio features. Furthermore, simulations showed that our method maintains robust detection down to a Signal-to-Noise Ratio (SNR) of −13

dB

, significantly outperforming audio feature-based detection, which was limited to approximately −10

dB

. The physically informed nature of our model not only provides a more reliable and robust solution but also enables the method to be generalized to other industrial scenarios with similar fault properties, offering broader applicability for reliable acoustic condition monitoring. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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24 pages, 6218 KB

Open AccessArticle

The Design and Data Analysis of an Underwater Seismic Wave System

by Dawei Xiao, Qin Zhu, Jingzhuo Zhang, Taotao Xie and Qing Ji

Sensors 2025, 25(13), 4155; https://doi.org/10.3390/s25134155 - 3 Jul 2025

Cited by 2 | Viewed by 2168

Abstract

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage [...] Read more.

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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Review

Jump to: Research

33 pages, 6090 KB

Open AccessReview

From Fixed-Frequency to Tunable: Advances in Acoustic Sensors for Physiological Acoustic Monitoring

by Jiantao Wang, Chuting Liu, Peiyan Dong, Jiamiao Li, Kaiyuan Tan, Bo Li, Jianhua Zhou and Yancong Qiao

Sensors 2026, 26(9), 2580; https://doi.org/10.3390/s26092580 - 22 Apr 2026

Viewed by 339

Abstract

Continuous, non-invasive cardiopulmonary monitoring is receiving increasing attention as population aging and chronic diseases rise. Acoustic sensing provides diagnostically relevant information with relatively simple hardware. Yet, physiological body sounds span heterogeneous and partially overlapping spectra and are highly susceptible to environmental noise and [...] Read more.

Continuous, non-invasive cardiopulmonary monitoring is receiving increasing attention as population aging and chronic diseases rise. Acoustic sensing provides diagnostically relevant information with relatively simple hardware. Yet, physiological body sounds span heterogeneous and partially overlapping spectra and are highly susceptible to environmental noise and motion artifacts, which limit conventional stethoscopes and fixed-frequency sensors. Frequency-Tunable Acoustic Sensors (FTAS) offer a promising route toward frequency-selective amplification and adaptive interference suppression by matching their resonance to target signals, thereby potentially supporting multi-site monitoring and personalized diagnostics on a single platform. This review starts with an overview of physiological sound generation and the evolution of auscultation, then surveys mainstream medical acoustic transducers (piezoelectric, capacitive microelectromechanical systems (MEMS), piezoresistive and triboelectric) and their limitations in frequency selectivity. Resonance-tuning strategies are classified into three paradigms: electrical tuning, material-based tuning, and geometric reconfiguration, and their tuning ranges, response characteristics, and representative implementations are comparatively discussed. Finally, this review discusses the potential translational value of FTAS in physiological acoustic signal monitoring, particularly in cardiovascular and respiratory assessment, and emphasizes the remaining challenges, including the trade-off between sensitivity and selectivity, as well as long-term biocompatibility. At the same time, this review highlights their development prospects in customizable acoustic sensing platforms. Full article

(This article belongs to the Special Issue Acoustic Sensing for Condition Monitoring)

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