Search Results (56)

Background: In just twenty years, three dangerous human coronaviruses—SARS-CoV, MERS-CoV, and SARS-CoV-2 have exposed critical gaps in early detection of emerging viral threats. Current diagnostics remain pathogen-focused, often missing the earliest phase of infection. A virus-agnostic, host-based diagnostic capable of detecting responses to viral intrusion is urgently needed. Methods: We hypothesized that the lungs act as biomechanical instruments, with infection altering tissue tension, wave propagation, and flow dynamics in ways detectable through subaudible vibroacoustic signals. In a matched case–control study, we enrolled 19 RT-PCR-confirmed COVID-19 inpatients and 16 matched controls across two Johns Hopkins hospitals. Multimodal data were collected, including passive vibroacoustic auscultation, lung ultrasound, peak expiratory flow, and laboratory markers. Machine learning models were trained to identify host-response biosignatures from anterior chest recordings. Results: 19 COVID-19 inpatients and 16 matched controls (mean BMI 32.4 kg/m², mean age 48.6 years) were successfully enrolled to the study. The top-performing, unoptimized, vibroacoustic-only model achieved an AUC of 0.84 (95% CI: 0.67–0.92). The host-covariate optimized model achieved an AUC of 1.0 (95% CI: 0.94–1.0), with 100% sensitivity (95% CI: 82–100%) and 99.6% specificity (95% CI: 85–100%). Vibroacoustic data from the anterior chest alone reliably distinguished COVID-19 cases from controls. Conclusions: This proof-of-concept study demonstrates that passive, noninvasive vibroacoustic biosignatures can detect host response to viral infection in a hospitalized population and supports further testing of this modality in broader populations. These findings support the development of scalable, host-based diagnostics to enable early, agnostic detection of future pandemic threats (ClinicalTrials.gov number: NCT04556149). Full article

Considering that piston internal combustion engines will remain essential converters of chemical energy into mechanical energy for an extended period, providing optimal diagnostic tools for their operation is imperative. Mechanical vibrations generated during machine operation constitute one of the most valuable sources of information about their technical condition. Their primary advantage lies in conveying diagnostic data with minimal time delay. This article presents a novel approach to vibroacoustic diagnostics of the combustion process in internal combustion piston engines. It leverages vibration signals carrying information about the pressure in the engine cylinder during fuel–air mixture combustion. In the proposed method, cylinder pressure information is reconstructed from vibration signals recorded on the cylinder head of the internal combustion engine. This method of signal-to-signal processing uses deep artificial neural network (ANN) models for signal reconstruction, providing an extensive exploration of the abilities of the presented models in the reconstruction of the pressure measurements. Furthermore, a novel two-network model, utilizing a U-net architecture with a dedicated smoothing network (SmN), allows for producing signals with minimal noise and outperforms other commonly used signal-to-signal architectures explored in this paper. To test the proposed methods, the study was limited to a single-cylinder engine, which presents certain constraints. However, this initial approach may serve as an inspiration for researchers to extend its application to multi-cylinder engines. Full article

Acoustic methods are a promising direction when determining the position of buried non-metallic pipelines. Under difficult soil conditions, one of the most effective methods is the vibroacoustic method, which has a maximum range of application when acoustic waves propagate through the transported medium. However, due to limited energy input into the pipeline, signal detection at significant distances from the source becomes challenging. This article considers the mechanism of acoustic oscillations attenuation in pipes and suggests possible directions for optimization of the investigated technology. The evaluation of mathematical modeling methods for the investigated process is conducted, and the key signal attenuation relationships are presented. The analysis allowed us to establish that the vibroacoustic method has the potential of increasing the efficiency by approximately 10–20%. Full article

Background: This review evaluates needle navigation technologies in minimally invasive cardiovascular surgery (MICS), identifying their strengths and limitations and the requirements for an ideal needle navigation system that features optimal guidance and easy adoption in clinical practice. Methods: A systematic search of PubMed, Web of Science, and IEEE databases up until June 2024 identified original studies on needle navigation in MICS. Eligible studies were those published within the past decade and that performed MICS requiring needle navigation technologies in adult patients. Animal studies, case reports, clinical trials, or laboratory experiments were excluded to focus on actively deployed techniques in clinical practice. Extracted data included the study year, modalities used, procedures performed, and the reported strengths and limitations, from which the requirements for an optimal needle navigation system were derived. Results: Of 36 eligible articles, 21 used ultrasound (US) for real-time imaging despite depth and needle visibility challenges. Computer tomography (CT)-guided fluoroscopy, cited in 19 articles, enhanced deep structure visualization but involved radiation risks. Magnetic resonance imaging (MRI), though excellent for soft-tissue contrast, was not used due to metallic tool incompatibility. Multimodal techniques, like US–fluoroscopy fusion, improved accuracy but added cost and workflow complexity. No single technology meets all the criteria for an ideal needle navigation system, which should combine real-time imaging, 3D spatial awareness, and tissue integrity feedback while being cost-effective and easily integrated into existing workflows. Conclusions: This review derived the criteria and obstacles an ideal needle navigation system must address before its clinical adoption, along with novel technological approaches that show potential to overcome those challenges. For instance, fusion technologies overlay information from multiple visual approaches within a single interface to overcome individual limitations. Additionally, emerging diagnostic methods like vibroacoustic sensing or optical fiber needles offer information from complementary sensory channels, augmenting visual approaches with insights into tissue integrity and structure, thereby paving the way for enhanced needle navigation systems in MICS. Full article

Unlike traditional tap changers, which require transformers to be de-energized before making changes, On-Load Tap Changers (OLTCs) can adjust taps while the transformer is in service, ensuring continuous power supply during voltage regulation. OLTCs enhance grid reliability and support load balancing, reducing strain on the network and optimizing power quality. Their importance has grown as the demand for stable voltage and the integration of renewables has increased, making them vital for modern and resilient power systems. While enhanced OLTCs often incorporate stronger materials and improved designs, mechanical components like contacts and diverter switches can still experience wear over time. This can result in longer maintenance intervals. In the era of digitalization, advanced diagnostic techniques capable of detecting early signs of wear or malfunction are essential to enable preventive maintenance for these important components. This contribution introduces a novel method for detecting faults and irregularities in OLTCs, leveraging vibroacoustic signals to enhance OLTC diagnostics. This paper proposes a tolerance-based approach using the envelope of vibroacoustic signals to identify faults. A significant challenge in this field is the limited availability of faulty signal data, which hinders the performance of machine learning algorithms. To address this, this study introduces a nonlinear model utilizing amplitude modulation with a Gaussian carrier to simulate faults by introducing controlled distortions. The dataset used in this study, with data recorded under real operating conditions from 2016 to 2023, is free of anomalies, providing a robust foundation for the analysis. The results demonstrate a marked improvement in the robustness of detecting simulated faults, offering a promising solution for enhancing OLTC diagnostics and preventive maintenance in modern power systems. Full article

Stored products, such as grains and processed foods, are susceptible to infestation by various insects. The early detection of insects in the supply chain is crucial, as introducing invasive pests to new environments may cause disproportionate harm. The STAR Center at Stevens Institute of Technology developed the Acoustic Stored Product Insect Detection System (A-SPIDS) to detect pests in stored products. The system, which comprises a sound-insulated container for product samples with a built-in internal array of piezoelectric sensors and additional electret microphones to record outside noise, was used to conduct numerous measurements of the vibroacoustic signatures of various insects, including the Callosobruchus maculatus, Tribolium confusum, and Tenebrio molitor, in different materials. A normalization method was implemented using the ambient noise of the sensors as a reference, to accommodate for the proprietary, non-calibrated sensors and allowing to set relative detection thresholds for unknown sensitivities. The normalized envelope of the filtered signals was used to characterize and compare the insect signals by estimating the Normalized Signal Pulse Amplitude (NSPA) and the Normalized Spectral Energy Level (NSEL). These parameters characterize the insect detection Signal Noise Ratio (SNR) for pulse-based detection (NSPA) and averaged energy-based detection (NSEL). These metrics provided an initial step towards the design of a reliable detection algorithm. In the conducted tests NSPA was significantly larger than NSEL. The NSPA reached 70 $\mathrm{d}$$\mathrm{B}$ for T. molitor in corn flakes. The insect signals were lower in flour where the averaged NSPA and NSEL values were around 40 dB and 11 $\mathrm{d}$$\mathrm{B}$ to 16 $\mathrm{d}$$\mathrm{B}$, respectively. Full article

Emotional communication is a multi-modal phenomenon involving posture, gestures, facial expressions, and the human voice. Affective states systematically modulate the acoustic signals produced during speech production through the laryngeal muscles via the central nervous system, transforming the acoustic signal into a means of affective transmission. Additionally, a substantial body of research in sonobiology has shown that audible acoustic waves (AAW) can affect cellular dynamics. This pilot study explores whether the physical–acoustic changes induced by gratitude states in human speech could influence cell proliferation and Ki67 expression in non-auditory cells (661W cell line). We conduct a series of assays, including affective electroencephalogram (EEG) measurements, an affective text quantification algorithm, and a passive vibro-acoustic treatment (PVT), to control the CO₂ incubator environment acoustically, and a proliferation assay with immunolabeling to quantify cell dynamics. Although a larger sample size is needed, the hypothesis that emotions can act as biophysical agents remains a plausible possibility, and feasible physical and biological pathways are discussed. In summary, studying the impact of gratitude AAW on cell biology represents an unexplored research area with the potential to enhance our understanding of the interaction between human cognition and biology through physics principles. Full article

Improving the capabilities of online condition monitoring systems, able to detect arising of catastrophic wear on cutting tools, has been an important target to be pursued for the metal cutting industry. Currently, different systems have been proposed, moved by the rising need of part quality improvements and production cost control. Despite this, cutter wear development, being related to several process variables and conditions, is still really difficult to be predicted accurately. This paper presents a detection wear method based on the time-domain analysis of vibro-acoustic signals. Specifically, cutter wear monitoring, using sound signals of a milling process, was performed at a laboratory level in a well-isolated working room. Sound signals were recorded at fixed main machining parameters, i.e., cutting speed, feed rate and depth of cut. The tests were carried out starting with a new set of inserts with significant wear conditions for the investigated process configuration. Results showed a consistent overlapping between the beginning of the catastrophic wear and an evident increment in the trend of the root mean square of the monitored acoustic signal, showing the potential of the methodology in detecting a suitable time to stop the milling process and to change the worn-out cutters. Full article

Laparoscopic access, a critical yet challenging step in surgical procedures, often leads to complications. Existing systems, such as improved Veress needles and optical trocars, offer limited safety benefits but come with elevated costs. In this study, a prototype of a novel technology for guiding needle interventions based on vibroacoustic signals is evaluated in porcine cadavers. The prototype consistently detected successful abdominal cavity entry in 100% of cases during 193 insertions across eight porcine cadavers. The high signal quality allowed for the precise identification of all Veress needle insertion phases, including peritoneum puncture. The findings suggest that this vibroacoustic-based guidance technology could enhance surgeons’ situational awareness and provide valuable support during laparoscopic access. Unlike existing solutions, this technology does not require sensing elements in the instrument’s tip and remains compatible with medical instruments from various manufacturers. Full article

Vibration diagnostics based on vibroacoustic signal data belong to the most common ways to monitor the technical condition of equipment and technical structures. The paper considers the general issues of vibration-based diagnostics and shows that in general, it is required to monitor both axial and torsional oscillations, as well as the inclination angle, occurring during the operation of various technical objects. To comprehensively monitor these parameters, a hybrid vibration sensor is proposed, simultaneously implementing three operating modes: recording linear displacements of the vibrating object; recording the rotation angle of the object at its torsional oscillations; recording the object angular deviation from the vertical component of the natural local geomagnetic field, i.e., the inclinometer mode. The proposed hybrid sensor design is described, and a theoretical analysis of the sensor’s operation in each of the aforementioned operating modes is performed. The authors show that in the inclinometer mode the sensor actually operates as a fluxgate meter. Generalizing the results of the sensor’s operation simultaneously in all three operating modes, an equation for the total output data signal has been obtained, which allows for obtaining the required information on the current values of linear displacements and rotation and inclination angles by selectively filtering it with respective three filters tuned to specific frequencies. The experimental studies of the proposed hybrid vibration sensor confirmed its ability to record various vibrational disturbances and changes in the inclination angle of the monitored object. Full article

An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In recent years, Hydro Quebec initiated a project to monitor the OLTC’s condition in power transformers using vibro-acoustic signals. A data acquisition system has been installed on real OLTCs, which has been continuously measuring their generated vibration signal envelopes over the past few years. In this work, the multivariate deep autoencoder, a reconstruction-based method for unsupervised anomaly detection, is employed to analyze the vibration signal envelopes generated by the OLTC and detect abnormal behaviors. The model is trained using a dataset obtained from the normal operating conditions of the transformer to learn patterns. Subsequently, kernel density estimation (KDE), a nonparametric method, is used to fit the reconstruction errors (regarding normal data) obtained from the trained model and to calculate the anomaly scores, along with the static threshold. Finally, anomalies are detected using a deep autoencoder, KDE, and a dynamic threshold. It should be noted that the input variables responsible for anomalies are also identified based on the value of the reconstruction error and standard deviation. The proposed method is applied to six different real datasets to detect anomalies using two distinct approaches: individually on each dataset and by comparing all six datasets. The results indicate that the proposed method can detect anomalies at an early stage. Also, three alarms, including ignorable anomalies, long-term changes, and significant alterations, were introduced to quantify the OLTC’s condition. Full article

Monitoring blood pressure, a parameter closely related to cardiovascular activity, can help predict imminent cardiovascular events. In this paper, a novel method is proposed to customize an existing mechanistic model of the cardiovascular system through feature extraction from cardiopulmonary acoustic signals to estimate blood pressure using artificial intelligence. As various factors, such as drug consumption, can alter the biomechanical properties of the cardiovascular system, the proposed method seeks to personalize the mechanistic model using information extracted from vibroacoustic sensors. Simulation results for the proposed approach are evaluated by calculating the error in blood pressure estimates compared to ground truth arterial line measurements, with the results showing promise for this method. Full article

Bucket elevators generally operate on a 24/7 basis, and for this reason, one of the main requirements is their high reliability. This reliability can be ensured, among other things, by assessing the technical condition of drive assemblies and working assemblies and taking appropriate measures. Carrying out diagnostic measurements enables periodical monitoring of those mechanisms. Vibroacoustic methods are usually employed in operating conditions to measure vibration velocity and acceleration at specific points, and are used as diagnostic signals. This paper presents the results of tests of the intensity of vibrations generated in the drive unit of a large industrial bucket elevator. The analysis of the results in the time domain and frequency domain served as the basis for evaluating the suitability of the drive, and thus the elevator, for long-term operation. Full article

This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics. Full article

Tool wear is a crucial factor in machining as it directly impacts surface quality and indirectly decreases machining efficiency, which leads to significant economic losses. Hence, monitoring tool wear state is of the utmost importance for achieving high performance and efficient machining. Although monitoring tool wear state using a single sensor has been validated in laboratory settings, it has certain drawbacks such as limited feature information acquisition and inability to learn important features adaptively. These limitations pose challenges to quickly extending the monitoring function of tool wear state of the machine tools. To solve these problems, this paper proposes a double-attention deep learning network based on vibroacoustic signal fusion feature infographics. The first solution is the construction of novel infographics using tool-intrinsic characteristics and multi-domain fusion features of multi-sensor inputs, which includes correlation analysis, principal component analysis, and feature fusion. The second solution is to build a novel deep network with a double-attention module and a spatial pyramid pooling module which can accurately and quickly identify tool wear state by successfully extracting critical spatial data from the infographics at various scales. The validity of the network is examined through an interpretability analysis based on the class activation graph. In terms of the tool wear status recognition task, the F1 score of the double-attention model based on an information graph is 11.61% higher than Resnet18, and peak recognition accuracy reaches 97.98%. Full article