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To enhance the field of continuous motor health monitoring, we present FAN-COIL-I, an extensive vibration sensor dataset derived from a Fan Coil motor. This dataset is uniquely positioned to facilitate the detection and prediction of motor health issues, enabling a more efficient maintenance scheduling process that can potentially obviate the need for regular checks. Unlike existing datasets, often created under controlled conditions or through simulations, FAN-COIL-I is compiled from real-world operational data, providing an invaluable resource for authentic motor diagnosis and predictive maintenance research. Gathered using a high-resolution 32 KHz sampling rate, the dataset encompasses comprehensive vibration readings from both the forward and rear sides of the Fan Coil motor over a continuous two-week period, offering a rare glimpse into the dynamic operational patterns of these systems in a corporate setting. FAN-COIL-I stands out not only for its real-world applicability but also for its potential to serve as a reliable benchmark for researchers and practitioners seeking to validate their models against genuine engine conditions.

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In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this TENG onto the inner surface of a mask allows for the capture of conversational speech signals through airflow vibrations, generating a comprehensive dataset. Employing advanced signal processing techniques, including short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning neural networks, facilitates the accurate identification of speaker content and verification of their identity. The accuracy rates for each category of vocabulary and identity recognition exceed 92% and 90%, respectively. This system represents a pivotal advancement in facilitating secure and efficient unobtrusive communication in quiet settings, with promising implications for smart home applications, virtual assistant technology, and potential deployment in security and confidentiality-sensitive contexts.

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Self-powered vibration sensor is highly desired for distributed and continuous monitoring requirements of Industry 4.0. Herein, a flexible fiber-shaped triboelectric nanogenerator (F-TENG) with a coaxial core-shell structure is proposed for the vibration monitoring. The F-TENG exhibits higher adaptability to the complex surfaces, which has an outstanding application prospect due to vital compensation for the existing rigid sensors. Initially, the contact characteristics between the dielectric layers, that related to the perceiving performance of the TENG, are theoretically analyzed. Such a TENG with 1D structure endows high sensitivity, allowing for accurately responding to a wide range of vibration frequencies (0.1 to 100 Hz). Even applying to the real diesel engine, the error in detecting the vibration frequencies is only 0.32% compared with the commercial vibration sensor, highlighting its potential in practical application. Further, assisted by deep learning, the recognition accuracy in monitoring nine operating conditions of the system achieves 97.87%. Overall, the newly designed F-TENG with the merits of high-adaptability, cost-efficiency, and self-powered, has offered a promising solution to fulfill an extensive range of vibration sensing applications in the future.

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Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data.

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This study investigated the innovative use of magnetoelastic sensors to detect the formation of single cracks in cement beams under bending vibrations. The detection method involved monitoring changes in the bending mode spectrum when a crack was introduced. The sensors, functioning as strain sensors, were placed on the beams, and their signals were detected non-invasively using a nearby detection coil. The beams were simply supported, and mechanical impulse excitation was applied. The recorded spectra displayed three distinct peaks representing different bending modes. The sensitivity for crack detection was determined to be a 24% change in the sensing signal for every 1% decrease in beam volume due to the crack. Factors influencing the spectra were investigated, including pre-annealing of the sensors, which improved the detection signal. The choice of beam support material was also explored, revealing that steel yielded better results than wood. Overall, the experiments demonstrated that magnetoelastic sensors enabled the detection of small cracks and provided qualitative information about their location.

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In order to ensure the safe operation of buried polyethylene pipelines adjacent to blasting excavations, controlling the effects of blasting vibration loads on the pipelines is a key concern. Model tests on buried polyethylene pipelines under blasting loads were designed and implemented, the vibration velocity and dynamic strain response of the pipelines were obtained using a TC-4850 blast vibrometer and a UT-3408 dynamic strain tester, and the distribution characteristics of blast vibration velocity and dynamic strain were analyzed based on the experimental data. The results show that the blast load has the greatest effect on the circumferential strain of the polyethylene pipe, and the dynamic strain response is greatest at the section of the pipe nearest to the blast source. Pipe peak vibration velocity (PPVV), ground peak particle velocity (GPPV), and the peak dynamic strain of the pipe were highly positively correlated, which verifies the feasibility of using GPPV to characterize pipeline vibration and strain level. According to the failure criteria and relevant codes, combined with the analysis of experimental results, the safety threshold of additional circumferential stress on the pipeline is 1.52 MPa, and the safety control vibration speed of the ground surface is 21.6 cm/s.

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In this paper, the viability of MEMS accelerometers is investigated to measure vibration parameters related to different locations of a vehicle with respect to the automotive dynamic functions. The data is collected to compare the accelerometer performances in different locations on the vehicle, including on the hood above the engine, on the hood above the radiator fan, over the exhaust pipe, and on the dashboard. The power spectral density (PSD), together with the time and frequency domain results, confirm the strength and frequencies of the sources of vehicle dynamics. The frequencies obtained from the vibrations of the hood above the engine and radiator fan are approximately 44.18 Hz and 38 Hz, respectively. In terms of the vibration amplitude, the measured amplitudes are between 0.5 g and 2.5 g in both cases. Furthermore, the time domain data collected on the dashboard during driving mode reflects the road condition. Overall, the knowledge obtained from the various tests conducted in this paper can be advantageous for further control and development of vehicle diagnostics, safety, and comfort.

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In the last decade, soft acoustic/vibration sensors have gained tremendous research interest due to their unique ability to detect broadband acoustic/vibration stimuli, potentializing futuristic applications including voice biometrics, voice-controlled human-machine-interfaces, electronic skin, and skin-mountable healthcare devices. Importantly, to benefit most from these sensors, it is inevitable to use machine learning (ML) to process their output signals; with ML, a more accurate and efficient interpretation of original data is possible. This paper is dedicated to offering an overview of recent advances empowering the development of soft acoustic/vibration sensors and their signal processing using ML. First, the key performance parameters of the sensors are discussed. Second, popular transduction mechanisms for the sensors are addressed, followed by an in-depth overview of each type, covering materials used, structural designs, and sensing performances. Third, potential applications of the sensors are elaborated and fourth, a thorough discussion on ML is conducted, exploring different types of ML, specific ML algorithms suitable for processing acoustic/vibration signals, and current trends in ML-assisted applications. Finally, the challenges and potential opportunities in soft acoustic/vibration sensor and ML research are revealed to offer new insights into future prospects in these fields.

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Smart manufacturing systems are considered the next generation of manufacturing applications. One important goal of the smart manufacturing system is to rapidly detect and anticipate failures to reduce maintenance cost and minimize machine downtime. This often boils down to detecting anomalies within the sensor data acquired from the system which has different characteristics with respect to the operating point of the environment or machines, such as, the RPM of the motor. In this paper, we analyze four datasets from sensors deployed in manufacturing testbeds. We detect the level of defect for each sensor data leveraging deep learning techniques. We also evaluate the performance of several traditional and ML-based forecasting models for predicting the time series of sensor data. We show that careful selection of training data by aggregating multiple predictive RPM values is beneficial. Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor to perform defect type classification. We release our manufacturing database corpus (4 datasets) and codes for anomaly detection and defect type classification for the community to build on it. Taken together, we show that predictive failure classification can be achieved, paving the way for predictive maintenance.

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Industry 5.0, also known as the "smart factory", is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system's operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

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Piezoceramic films are an essential class of energy-conversion materials that have been widely used in the electronics industry. Although current methods create a great freedom for fabricating high-quality piezoceramic films, it requires well-controlled synthesis conditions, including special high-cost equipment and planar substrates particularly. The limited substrate selections hinder the applications of piezoceramic films in 3D conformal structures where most objects possess complex curvilinear surfaces. To overcome such limitations, a fast, energy-efficient, and cost-effective approach, named flame treated spray (FTS) coating, is developed for preparing piezoceramic films on free-form surfaces. The flame treatment significantly enhances the hydrophilicity of a substrate, assisting in forming a uniform and continuous thin film. The followed spray coating deposits hundreds of nanometers to several micrometers thick films on 3D free-form surfaces. Given the size controllability and arbitrary surface compatibility of the FTS method, a highly conformal piezoelectric tactile sensor array (4 × 4) is assembled on a spherical surface for mimicking robot fingers and an on-site thin-film sensor on the wing of an aircraft model to monitor the vibration in real-time during flight. The FTS film deposition offers a highly promising methodology for the application of functional thin-film from micro- to marcoscale devices, regardless of conformal problems.

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In the current work, magnetoelastic material ribbons are used as vibration sensors to monitor, in real time and non-destructively, the mechanical health state of rotating beam blades. The magnetoelastic material has the form of a thin ribbon and is composed of Metglas alloy 2826 MB. The study was conducted in two stages. In the first stage, an experiment was performed to test the ability of the ribbon to detect and transmit the vibration behavior of four rotating blades, while the second stage was the same as the first but with minor damages introduced to the blades. As far as the first stage is concerned, the results show that the sensor can detect and transmit with great accuracy the vibratory behavior of the rotating blades, through which important information about the mechanical health state of the blade can be extracted. Specifically, the fast Fourier transform (FFT) spectrum of the recorded signal revealed five dominant peaks in the frequency range 0-3 kHz, corresponding to the first five bending modes of the blades. The identification process was accomplished using ANSYS modal analysis, and the comparison results showed deviation values of less than 1% between ANSYS and the experimental values. In the second stage, two types of damages were introduced to the rotating blades, an edge cut and a hole. The damages were scaled in number from one blade to another, with the first blade having only one side cut while the last blade had two side cuts and two holes. The results, as was expected, show a measurable shifting on the frequency values of the bending modes, thus proving the ability of the proposed magnetoelastic sensors to detect and transmit changes of the mechanical state of rotating blades in real time.

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This paper presents a low-frequency electromagnetic vibrational energy harvester (EVEH) with two degrees of freedom and two resonant modes. The proposed EVEH is based on a disc magnet suspended in a pendulum fashion by a polymeric spring between two sets of polymer coil stacks. The fabricated EVEH is capable of harvesting vibration energy on two directions with an extended bandwidth. With a sinusoidal acceleration of ±1 g on Z direction, a peak-to-peak closed-circuit output voltage of 0.51 V (open-circuit voltage: 1 V), and an output power of 35.1 µW are achieved at the resonant frequency of 16 Hz. With a sinusoidal acceleration of ±1.5 g on X direction, a peak-to-peak output voltage of 0.14 V and power of 2.56 µW are achieved, at the resonant frequency of 20 Hz.

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With advances in internet of things technology and fossil fuel depletion, energy harvesting has emerged rapidly as a means of supplying small electronics with electricity. As a method of enhancing the electrical output of the triboelectric nanogenerator, specialized for harvesting mechanical energy, structural modification to amplify the input force is receiving attention due to the limited input energy level. In this research, a lever structure was employed for delivering the amplified input force to a triboelectric nanogenerator. With structural optimization of a 2.5 cm : 5 cm distance ratio of the first and second parts using two lever structures, the highest electrical outputs were achieved: a VOC of 51.03 V, current density of 3.34 mA m-2, and power density of 73.5 mW m-2 at 12 MΩ in the second part. As applications of this triboelectric generator, a vertical vibration sensor and a wearable reloading trigger in a gun shooting game were demonstrated. The possibility for a wearable finger bending sensor with low-level input was checked using a minimized device. Enhanced low-detection limit with amplified input force from the structural advantage of this lever-based triboelectric nanogenerator device can expand its applicability to the mechanical trigger for wearable electronics.

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We demonstrate the use of a graded-index perfluorinated optical fiber (GI-POF) for distributed static and dynamic strain measurements based on Rayleigh scattering. The system is based on an amplitude-based phase-sensitive Optical Time-Domain Reflectometry (ϕ-OTDR) configuration, operated at the unconventional wavelength of 850 nm. Static strain measurements have been carried out at a spatial resolution of 4 m and for a strain up to 3.5% by exploiting the increase of the backscatter Rayleigh coefficient consequent to the application of a tensile strain, while vibration/acoustic measurements have been demonstrated for a sampling frequency up to 833 Hz by exploiting the vibration-induced changes in the backscatter Rayleigh intensity time-domain traces arising from coherent interference within the pulse. The reported tests demonstrate that polymer optical fibers can be used for cost-effective multiparameter sensing.

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The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.

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Underwater vibration detection is of great importance in personal safety, environmental protection, and military defense. Sealing layers are required in many underwater sensor architectures, leading to limited working-life and reduced sensitivity. Here, a flexible, superhydrophobic, and conductive tungsten disulfide (WS2) nanosheets-wrapped sponge (SCWS) is reported for the high-sensitivity detection of tiny vibration from the water surfaces and from the grounds. When the SCWS is immersed in water, a continuous layer of bubbles forms on its surfaces, providing the sensor with two special abilities. One is sealing-free feature due to the intrinsic water-repellent property of SCWS. The other is functioning as a vibration-sensitive medium to convert mechanical energy into electric signals through susceptible physical deformation of bubbles. Therefore, the SCWS can be used to precisely detect tiny vibration of water waves, and even sense those caused by human footsteps, demonstrating wide applications of this amphibious (water/ground) vibration sensor. Results of this study can initiate the exploration of superhydrophobic materials with elastic and conductive properties for underwater flexible electronic applications.

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The aim of this study is to investigate that fetal heart rates (fHR) extracted from fetal phonocardiography (fPCG) could convey similar information of fHR from cardiotocography (CTG). Four-channel fPCG sensors made of low cost (<$1) ceramic piezo vibration sensor within 3D-printed casings were used to collect abdominal phonogram signals from 20 pregnant mothers (>34 weeks of gestation). A novel multi-lag covariance matrix-based eigenvalue decomposition technique was used to separate maternal breathing, fetal heart sounds (fHS) and maternal heart sounds (mHS) from abdominal phonogram signals. Prior to the fHR estimation, the fPCG signals were denoised using a multi-resolution wavelet-based filter. The proposed source separation technique was first tested in separating sources from synthetically mixed signals and then on raw abdominal phonogram signals. fHR signals extracted from fPCG signals were validated using simultaneous recorded CTG-based fHR recordings.The experimental results have shown that the fHR derived from the acquired fPCG can be used to detect periods of acceleration and deceleration, which are critical indication of the fetus' well-being. Moreover, a comparative analysis demonstrated that fHRs from CTG and fPCG signals were in good agreement (Bland Altman plot has mean = -0.21 BPM and ±2 SD = ±3) with statistical significance (p < 0.001 and Spearman correlation coefficient ρ = 0.95). The study findings show that fHR estimated from fPCG could be a reliable substitute for fHR from the CTG, opening up the possibility of a low cost monitoring tool for fetal well-being.