Research on Vibration Accumulation Self-Powered Downhole Sensor Based on Triboelectric Nanogenerators

In drilling operations, measuring vibration parameters is crucial for enhancing drilling efficiency and ensuring safety. Nevertheless, the conventional vibration measurement sensor significantly extends the drilling cycle due to its dependence on an external power source. Therefore, we propose a vibration-accumulation-type self-powered sensor in this research, aiming to address these needs. By leveraging vibration accumulation and electromagnetic power generation to accelerate charging, the sensor's output performance is enhanced through a complementary charging mode. The experimental results regarding sensing performance demonstrate that the sensor possesses a measurement range spanning from 0 to 11 Hz, with a linearity of 3.2% and a sensitivity of 1.032. Additionally, it exhibits a maximum average measurement error of less than 4%. The experimental results of output performance measurement indicate that the sensor unit and generator set exhibit a maximum output power of 0.258 μW and 25.5 mW, respectively, and eight LED lights can be lit at the same time. When the sensor unit and power generation unit output together, the maximum output power of the sensor is also 25.5 mW. Furthermore, we conducted tests on the sensor's output signal in conditions of high temperature and humidity, confirming its continued functionality in such environments. This sensor not only achieves self-powered sensing capabilities, addressing the power supply challenges faced by traditional downhole sensors, but also integrates energy accumulation with electromagnetic power generation to enhance its output performance. This innovation enables the sensor to harness downhole vibration energy for powering other micro-power devices, showcasing promising application prospects.

Cardiac Multi-Frequency Vibration Signal Sensor Module and Feature Extraction Method Based on Vibration Modeling

Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor's performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.

Vibration Sensors on Flexible Substrates Based on Nanoparticle Films Grown by Physical Vapor Deposition

Flexible electronics have gained a lot of attention in recent years due to their compatibility with soft robotics, artificial arms, and many other applications. Meanwhile, the detection of acoustic frequencies is a very useful tool for applications ranging from voice recognition to machine condition monitoring. In this work, the dynamic response of Pt nanoparticles (Pt NPs)-based strain sensors on flexible substrates is investigated. the nanoparticles were grown in a vacuum by magnetron-sputtering inert-gas condensation. Nanoparticle sensors made on cracked alumina deposited by atomic layer deposition on the flexible substrate and reference nanoparticle sensors, without the alumina layer, were first characterized by their response to strain. The sensors were then characterized by their dynamic response to acoustic frequency vibrations between 20 Hz and 6250 Hz. The results show that alumina sensors outperformed the reference sensors in terms of voltage amplitude. Sensors on the alumina layer could accurately detect frequencies up to 6250 Hz, compared with the reference sensors, which were sensitive to frequencies up to 4250 Hz, while they could distinguish between two neighboring frequencies with a difference of no more than 2 Hz.

A Flexible, Adaptive, and Self-Powered Triboelectric Vibration Sensor with Conductive Sponge-Silicone for Machinery Condition Monitoring

Vibration sensors for continuous and reliable condition monitoring of mechanical equipment, especially detection points of curved surfaces, remain a great challenge and are highly desired. Herein, a highly flexible and adaptive triboelectric vibration sensor for high-fidelity and continuous monitoring of mechanical vibration conditions is proposed. The sensor is entirely composed of flexible materials. It consists of a conductive sponge-silicone layer and a fluorinated ethylene propylene film. It can detect vibration acceleration of 5 to 50 m s-2 and vibration frequency of 10 to 100 Hz. It has strong robustness and stability, and the output performance barely changes after the durability test of 168 000 working cycles. Additionally, the flexible sensor can work even when the detection point of the mechanical equipment is curved, and the linear fit of the output voltage and acceleration is very close to that when the detection point is flat. Finally, it can be applied to monitoring the working condition of blower and vehicle engine, and can transmit vibration signal to mobile phone application through Wi-Fi module for real-time monitoring. The flexible triboelectric vibration sensor is expected to provide a practical paradigm for smart, green, and sustainable wireless sensor system in the era of Internet of Things.

Pullulan-1-Ethyl-3-Methylimidazolium Tetrafluoroborate Composite as a Water-Soluble Active Component of a Vibration Sensor

In recent years, the issue of electronic waste production has gained significant attention. To mitigate the environmental impact of e-waste, one approach under consideration involves the development of biodegradable electronic devices or devices that dissolve in the environment at the end of their life cycle. This study presents results related to the creation of a sensor that effectively addresses both criteria. The device was constructed using a composite material formed by impregnating a pullulan membrane (a biodegradable water-soluble biopolymer) with 1-Ethyl-3-Methylimidazolium tetrafluoroborate (a water-soluble ionic liquid) and coating the product with a conductive silver-based varnish. Capitalizing on the piezoionic effect, the device has demonstrated functionality as a vibration sensor with a sensitivity of approximately 5.5 × 10-5 V/mm and a resolution of about 1 mm. The novelty of this study lies in the unique combination of materials. Unlike the use of piezoelectric materials, this combination allows for the production of a device that does not require an external potential difference generator to function properly as a sensor. Furthermore, the combination of a biopolymer, such as pullulan, and an ionic liquid, both readily soluble in water, in creating an active electronic component represents an innovation in the field of vibration sensors.

Quasi-Zero Stiffness Vibration Sensing and Energy Harvesting Integration Based on Buckled Piezoelectric Euler Beam

This paper presents a novel quasi-zero stiffness vibration sensing and energy harvesting integration system for absolute displacement measurements based on a buckled piezoelectric Euler beam (BPEB) with quasi-zero stiffness (QZS) characteristics. On one hand, BPEB provides negative stiffness to the system, thus creating a vibration-free point within the system and transforming the absolute displacement measurement problem into a relative motion sensing problem. On the other hand, during the measurement process, the BPEB collects the vibration energy from the system, which can provide electrical energy for low-power relative motion sensing devices and remarkably suppress the frequency range of the jump phenomenon, thereby further expanding the frequency domain measurement range of the sensing system. The research results have shown that this system can measure the absolute motion signal of the tested object in low-frequency vibration with small excitation. By adjusting parameters such as the force-electric coupling coefficient and damping ratio, the measurement accuracy of the sensing system can be improved. Furthermore, the system can convert the mechanical energy of vibrations into electrical energy to power the surrounding low-power sensors or provide partial power. This could potentially achieve self-powering integrated quasi-zero stiffness vibration sensing, offering another approach and possibility for the automation development in wireless sensing systems and the Internet of Things field.

A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA

Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)-variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.

A Highly Sensitive Triboelectric Quasi-Zero Stiffness Vibration Sensor with Ultrawide Frequency Response

Sensors based on triboelectric nanogenerators (TENGs) have gained worldwide interest owing to their advantages of low cost and self-powering. However, the detection of most triboelectric vibration sensors (TVS) is restricted to low frequency, whereas high-frequency vibration signals are successfully measured in recent studies; their sensitivity still requires improvement. Hence, a highly sensitive vibration sensor based on TENG (HSVS-TENG) with ultrawide frequency response is presented. This study is the first to introduce a quasi-zero stiffness structure into the TENG to minimize the driving force by optimizing the magnetic induction intensity and the weight of the moving part. The results show that the HSVS-TENG can measure vibrations with frequencies ranging from 2.5 to 4000 Hz, with a sensitivity ranging from 0.32 to 134.9 V g^-1 . Moreover, the sensor exhibits a good linear response versus the applied acceleration, and the linearity ranges from 0.08 to 2.81 V g^-1 . The self-powered sensor can monitor the running state and fault type of the key components with a recognition accuracy of 98.9% by leveraging machine-learning algorithms. The results reach a new height for the ultrawide frequency response and high sensitivity of the TVS and provide an idea for a follow-up high-resolution TVS.

A High-Temperature Accelerometer with Excellent Performance Based on the Improved Graphene Aerogel

A high-temperature accelerometer plays an important role for ensuring normal operation of equipment in aerospace, such as monitoring and identifying abnormal vibrations of aircraft engines. Phase transitions of piezoelectric crystals, mechanical failure and current leakage of piezoresistive/capacitive materials are the prominent inherent limitations of present high-temperature accelerometers working continuously above 973 K. With the rapid development of aerospace, it is a great challenge to develop a new type of vibration sensor to meet the crucial demands at high temperature. Here we report a high-temperature accelerometer working with a contact resistance mechanism. Based on the improved graphene aerogel (GA) prepared by a modulated treatment process, the accelerometer can operate continuously and stably at 1073 K and intermittently at 1273 K. The developed sensor is lightweight (sensitive element <5 mg) and has high sensitivity (an order of magnitude higher than MEMS accelerometers) and wide frequency response range (up to 5 kHz at 1073 K) with marked stability, repeatability and low nonlinearity error (<1%). These merits are attributed to the excellent and stable mechanical properties of the improved GA in the range of 299-1073 K. The accelerometer could be a promising candidate for high-temperature vibration sensing in space stations, planetary rovers and others.

Sagnac with Double-Sense Twisted Low-Birefringence Standard Fiber as Vibration Sensor

In this work, we study a double-sense twisted low-birefringence Sagnac loop structure as a sound/vibration sensing device. We study the relation between the adjustments of a wave retarder inside the loop (which allows controlling the transmission characteristic to deliver 10, 100, and 300 μW average power at the output of the system) and the response of the Sagnac sensor to vibration frequencies ranging from 0 to 22 kHz. For a 300 m loop Sagnac, two sets of experiments were carried out, playing at the same time all the sound frequencies mixed for ∼1 s, and playing a sweep of frequencies for 30 s. In both cases, the time- and frequency-domain transmission amplitudes are larger for an average power of 10 μW, and smaller for an average power of 300 μW. For mixed frequencies, the Fourier analysis shows that the Sagnac response is larger for low frequencies (from 0 to ∼5 kHz) than for high frequencies (from ∼5 kHz to ∼22 kHz). For a sweep of frequencies, the results reveal that the interferometer perceives all frequencies. However, beyond ∼2.5 kHz, harmonics are present each ∼50 Hz, revealing that some resonances are present. The results about the influence of the power transmission through the polarizer and power emission of laser diode (LD) on the Sagnac interferometer response at high frequencies reveal that our system is robust, and the results are highly reproducible, and harmonics do not depend on the state of polarization at the input of the Sagnac interferometer. Furthermore, increasing the LD output power from 5 mW to 67.5 mW allows us to eliminate noisy signals at the system output. in our setup, the minimum sound level detected was 56 dB. On the other hand, the experimental results of a 10 m loop OFSI reveal that the response at low frequencies (1.5 kHz to 5 kHz) is minor compared with the 300 m loop OFSI. However, the response at high frequencies is low but still enables the detection of these frequencies, yielding the possibility of tuning the response of the vibration sensor by varying the length of the Sagnac loop.

Ultra-Wide Range Vibration Frequency Detection Sensors Based on Elastic Steel Triboelectric Nanogenerators for Intelligent Machinery Monitoring

Vibration measurement and analysis play an important role in diagnosing mechanical faults, but existing vibration sensors are limited by issues such as dependence on external power sources and high costs. To overcome these challenges, the use of triboelectric nanogenerator (TENG)-based vibration sensors has recently attracted attention. These vibration sensors measure a small range of vibration frequencies and are not suitable for measuring high-frequency vibrations. Herein, a self-powered vibration sensor based on an elastic steel triboelectric nanogenerator (ES-TENG) is proposed. By optimizing the elastic steel sheet structure and combining time-frequency transformation and filtering processing methods, the measurement of medium- and high-frequency vibrations is achieved. These results demonstrate that the ES-TENG can perform vibration measurements in the range of 2-10,000 Hz, with a small average error (~0.42%) between the measured frequency and external vibration frequency values. Therefore, the ES-TENG can be used as a self-powered, highly-accurate vibration sensor for intelligent machinery monitoring.

A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces

Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.

Transparent and Flexible Vibration Sensor Based on a Wheel-Shaped Hybrid Thin Membrane

With the advent of human–machine interaction and the Internet of Things, wearable and flexible vibration sensors have been developed to detect human voices and surrounding vibrations transmitted to humans. However, previous wearable vibration sensors have limitations in the sensing performance, such as frequency response, linearity of sensitivity, and esthetics. In this study, a transparent and flexible vibration sensor was developed by incorporating organic/inorganic hybrid materials into ultrathin membranes. The sensor exhibited a linear and high sensitivity (20 mV/g) and a flat frequency response (80–3000 Hz), which are attributed to the wheel-shaped capacitive diaphragm structure fabricated by exploiting the high processability and low stiffness of the organic material SU-8 and the high conductivity of the inorganic material ITO. The sensor also has sufficient esthetics as a wearable device because of the high transparency of SU-8 and ITO. In addition, the temperature of the post-annealing process after ITO sputtering was optimized for the high transparency and conductivity. The fabricated sensor showed significant potential for use in transparent healthcare devices to monitor the vibrations transmitted from hand-held vibration tools and in a skin-attachable vocal sensor.

Channel-Crack-Designed Suspended Sensing Membrane as a Fully Flexible Vibration Sensor with High Sensitivity and Dynamic Range

Vibration sensors are essential for signal acquisition, motion measuring, and structural health evaluations in civil and industrial applications. However, the mechanical brittleness and complicated installation process of micro-electromechanical system vibration sensors block their applications in wearable devices and human-machine interaction. The development of flexible vibration sensors satisfying the requirements of good flexibility, high sensitivity, and the ability to attach conformably on curved critical components is highly demanded but still remains a challenge. Here, we demonstrate a highly sensitive and fully flexible vibration sensor with a channel-crack-designed suspended sensing membrane for high dynamic vibration and acceleration monitoring. The flexible sensor is designed as a suspended vibration membrane structure by bonding a channel-crack-sensing membrane on a cavity substrate, of which the suspended sensing membrane can freely vibrate out of plane under external vibration. By inducing the cracks to be generated in the embedded multiwalled carbon nanotube channels and fully cracked across the conducting routes, the suspended vibration membrane shows high sensitivity, good reproducibility, and robust sensing stability. The resultant vibration sensor demonstrates an ultrawide frequency vibration response range from 0.1 to 20,000 Hz and exhibits the ability to respond to acceleration vibration with a broad response of 0.24-100 m/s². The high sensitivity, wide bandwidth, and fully flexible format of the vibration sensor enable it to be directly attached on human bodies and curvilinear surfaces to conduct in situ vibration sensing, which was demonstrated by motion detection, voice identification, and the vibration monitoring of mechanical equipment.

Fiber Optic Based Distributed Mechanical Vibration Sensing

The distributed long-range sensing system, using the standard telecommunication single-mode optical fiber for the distributed sensing of mechanical vibrations, is described. Various events generating vibrations, such as a walking or running person, moving car, train, and many other vibration sources, can be detected, localized, and classified. The sensor is based on phase-sensitive optical time-domain reflectometry (ϕ-OTDR). Related sensing system components were designed and constructed, and the system was tested both in the laboratory and in the real deployment, with an 88 km telecom optical link, and the results are presented in this paper. A two-fiber sensor unit, with a double-sensing range was also designed, and its scheme is described. The unit was constructed and the initial measurement results are presented.

A Self-Powered and Highly Accurate Vibration Sensor Based on Bouncing-Ball Triboelectric Nanogenerator for Intelligent Ship Machinery Monitoring

With the development of intelligent ship, types of advanced sensors are in great demand for monitoring the work conditions of ship machinery. In the present work, a self-powered and highly accurate vibration sensor based on bouncing-ball triboelectric nanogenerator (BB-TENG) is proposed and investigated. The BB-TENG sensor consists of two copper electrode layers and one 3D-printed frame filled with polytetrafluoroethylene (PTFE) balls. When the sensor is installed on a vibration exciter, the PTFE balls will continuously bounce between the two electrodes, generating a periodically fluctuating electrical signals whose frequency can be easily measured through fast Fourier transform. Experiments have demonstrated that the BB-TENG sensor has a high signal-to-noise ratio of 34.5 dB with mean error less than 0.05% at the vibration frequency of 10 Hz to 50 Hz which covers the most vibration range of the machinery on ship. In addition, the BB-TENG can power 30 LEDs and a temperature sensor by converting vibration energy into electricity. Therefore, the BB-TENG sensor can be utilized as a self-powered and highly accurate vibration sensor for condition monitoring of intelligent ship machinery.

Research on the Potential of Spherical Triboelectric Nanogenerator for Collecting Vibration Energy and Measuring Vibration

The traditional downhole drilling vibration measurement methods which use cable or battery as power supplies increase the drilling costs and reduce the drilling efficiency. This paper proposes a spherical triboelectric nanogenerator, which shows the potential to collect the downhole vibration energy and measure the vibration frequency in a self-powered model. The power generation tests show that the output signal amplitude of the spherical triboelectric nanogenerator increases as the vibration frequency increases, and it can reach a maximum output voltage of 70 V, a maximum current of 3.3 × 10⁻⁵ A, and a maximum power of 10.9 × 10⁻⁹ W at 8 Hz when a 10-ohm resistor is connected. Therefore, if the power generation is stored for a certain period of time when numbers of the spherical triboelectric nanogenerators are connected in parallel, it may provide intermittent power for the low-power downhole measurement instruments. In addition, the sensing tests show that the measurement range is 0 to 8 Hz, the test error is less than 2%, the applicable working environment temperature is below 100 degrees Celsius, and the installation distance between the spherical triboelectric nanogenerator and the vibration source should be less than the critical value of 150 cm because the output signal amplitude is inversely proportional to the distance.

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms

In this study, vibration sensors were used to measure blast-induced ground vibration (PPV). Different evolutionary algorithms were assessed for predicting PPV, including the particle swarm optimization (PSO) algorithm, genetic algorithm (GA), imperialist competitive algorithm (ICA), and artificial bee colony (ABC). These evolutionary algorithms were used to optimize the support vector regression (SVR) model. They were abbreviated as the PSO-SVR, GA-SVR, ICA-SVR, and ABC-SVR models. For each evolutionary algorithm, three forms of kernel function, linear (L), radial basis function (RBF), and polynomial (P), were investigated and developed. In total, 12 new hybrid models were developed for predicting PPV in this study, named ABC-SVR-P, ABC-SVR-L, ABC-SVR-RBF, PSO-SVR-P, PSO-SVR-L, PSO-SVR-RBF, ICA-SVR-P, ICA-SVR-L, ICA-SVR-RBF, GA-SVR-P, GA-SVR-L and GA-SVR-RBF. There were 125 blasting results gathered and analyzed at a limestone quarry in Vietnam. Statistical criteria like R², RMSE, and MAE were used to compare and evaluate the developed models. Ranking and color intensity methods were also applied to enable a more complete evaluation. The results revealed that GA was the most dominant evolutionary algorithm for the current problem when combined with the SVR model. The RBF was confirmed as the best kernel function for the GA-SVR model. The GA-SVR-RBF model was proposed as the best technique for PPV estimation.

PVDF Nanofiber Sensor for Vibration Measurement in a String

Flexible, self-powered and miniaturized sensors are extensively used in the areas of sports, soft robotics, health care and communication devices. Measurement of vibration is important for determining the mechanical properties of a structure, specifically the string tension in strings. In this work, a flexible, lightweight and self-powered sensor is developed and attached to a string to measure vibrations characteristics in strings. Electrospun poly(vinylidene) fluoride (PVDF) nanofibers are deposited on a flexible liquid crystal polymer (LCP) substrate for the development of the sensor. The electrospinning process is optimized for different needle sizes (0.34–0.84 mm) and flow rates (0.6–3 mL/h). The characterization of the sensor is done in a cantilever configuration and the test results indicate the sensor’s capability to measure the frequency and strain in the required range. The comparison of the results from the developed PVDF sensor and a commercial Laser Displacement Sensor (LDS) showed good resemblance (±0.2%) and a linear voltage profile (0.2 mV/με). The sensor, upon attachment to a racket string, is able to measure single impacts and sinusoidal vibrations. The repeatability of the results on the measurement of vibrations produced by an impact hammer and a mini shaker demonstrate an exciting new application for piezoelectric sensors.

In-Situ Monitoring and Diagnosing for Process Based on Vibration Sensors

Fused filament fabrication (FFF) is one of the most widely used additive manufacturing (AM) technologies and it has great potential in fabricating prototypes with complex geometry. For high quality manufacturing, monitoring the products in real time is as important as maintaining the FFF machine in the normal state. This paper introduces an approach that is based on the vibration sensors and data-driven methods for in-situ monitoring and diagnosing the FFF process. The least squares support vector machine (LS-SVM) algorithm has been applied for identifying the normal and filament jam states of the FFF machine, besides fault diagnosis in real time. The identification accuracy for the case studies explored here using LS-SVM is greater than 90%. Furthermore, to ensure the product quality during the FFF process, the back-propagation neural network (BPNN) algorithm has been used to monitor and diagnose the quality defects, as well as the warpage and material stack caused by abnormal leakage for the products in-situ. The diagnosis accuracy for the case studies explored here using BPNN is greater than 95%. Results from the experiments show that the proposed approach can accurately recognize the machine failures and quality defects during the FFF process, thus effectively assuring the product quality.

Leak Detection and Location of Water Pipes Using Vibration Sensors and Modified ML Prefilter

This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected.

Detecting Subtle Vibrations Using Graphene-Based Cellular Elastomers

Ultralight graphene elastomer-based flexible sensors are developed to detect subtle vibrations within a broad frequency range. The same device can be employed as an accelerometer, tested within the experimental bandwidth of 20-300 Hz as well as a microphone, monitoring sound pressures from 300 to 20 000 Hz. The sensing element does not contain any metal parts, making them undetectable by external sources and can provide an acceleration sensitivity of 2.6 mV/g, which is higher than or comparable to those of rigid Si-based piezoresistive microelectromechanical systems (MEMS).

Diaphragm Based Fiber Bragg Grating Acceleration Sensor with Temperature Compensation

A novel fiber Bragg grating (FBG) sensing-based acceleration sensor has been proposed to simultaneously decouple and measure temperature and acceleration in real-time. This design applied a diaphragm structure and utilized the axial property of a tightly suspended optical fiber, enabling improvement in its sensitivity and resonant frequency and achieve a low cross-sensitivity. The theoretical vibrational model of the sensor has been built, and its design parameters and sensing properties have been analyzed through the numerical analysis. A decoupling method has been presented with consideration of the thermal expansion of the sensor structure to realize temperature compensation. Experimental results show that the temperature sensitivity is 8.66 pm/°C within the range of 30-90 °C. The acceleration sensitivity is 20.189 pm/g with a linearity of 0.764% within the range of 5~65 m/s². The corresponding working bandwidth is 10~200 Hz and its resonant frequency is 600 Hz. This sensor possesses an excellent impact resistance for the cross direction, and the cross-axis sensitivity is below 3.31%. This implementation can avoid the FBG-pasting procedure and overcome its associated shortcomings. The performance of the proposed acceleration sensor can be easily adjusted by modifying their corresponding physical parameters to satisfy requirements from different vibration measurements.

A Fiber-Optic Interferometric Tri-Component Geophone for Ocean Floor Seismic Monitoring

For the implementation of an all fiber observation network for submarine seismic monitoring, a tri-component geophone based on Michelson interferometry is proposed and tested. A compliant cylinder-based sensor head is analyzed with finite element method and tested. The operation frequency ranges from 2 Hz to 150 Hz for acceleration detection, employing a phase generated carrier demodulation scheme, with a responsivity above 50 dB re rad/g for the whole frequency range. The transverse suppression ratio is about 30 dB. The system noise at low frequency originated mainly from the 1/f fluctuation, with an average system noise level −123.55 dB re rad/Hz ranging from 0 Hz to 500 Hz. The minimum detectable acceleration is about 2 ng/Hz, and the dynamic range is above 116 dB.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

A Fiber Bragg Grating Sensing-Based Micro-Vibration Sensor and Its Application

This paper proposes a fiber Bragg grating sensing-based micro-vibration sensor. The optical fiber has been directly treated as an elastomer to design the micro-vibration sensor, which possesses two FBGs. The mass is fixed on the middle of the fiber, and the vertical vibration of the mass has been converted into the axial tension/compression of the fiber. The principle of the sensor has been introduced, and the experiment conclusions show that the sensor sensitivity is 2362 pm/g within the range of 200–1200 mm/s², which is consistent with theoretical analysis sensitivity of 2532.6 pm/g, and it shows an excellent linearity of 1.376%, while the resonant frequency of the sensor is 34 Hz, and the flat frequency range resides in the 0–22 Hz range. When used to measure micro-vibrations, its measured frequency relative error is less than 1.69% compared with the values acquired with a MEMS accelerometer, and the amplitude values of its measured vibration signal are consistent with the MEMS accelerometer under different excitation conditions too, so it can effectively realize the micro-vibration measurements.

A Fiber Bragg Grating Sensing Based Triaxial Vibration Sensor

A fiber Bragg grating (FBG) sensing based triaxial vibration sensor has been presented in this paper. The optical fiber is directly employed as elastomer, and the triaxial vibration of a measured body can be obtained by two pairs of FBGs. A model of a triaxial vibration sensor as well as decoupling principles of triaxial vibration and experimental analyses are proposed. Experimental results show that: sensitivities of 86.9 pm/g, 971.8 pm/g and 154.7 pm/g for each orthogonal sensitive direction with linearity are separately 3.64%, 1.50% and 3.01%. The flat frequency ranges reside in 20–200 Hz, 3–20 Hz and 4–50 Hz, respectively; in addition, the resonant frequencies are separately 700 Hz, 40 Hz and 110 Hz in the x/y/z direction. When the sensor is excited in a single direction vibration, the outputs of sensor in the other two directions are consistent with the outputs in the non-working state. Therefore, it is effectively demonstrated that it can be used for three-dimensional vibration measurement.

Harmonic-resonator-based triboelectric nanogenerator as a sustainable power source and a self-powered active vibration sensor

A harmonic-resonator-based triboelectric nanogenerator (TENG) is presented as a sustainable power source and an active vibration sensor. It can effectively respond to vibration frequencies ranging from 2 to 200 Hz with a considerably wide working bandwidth of 13.4 Hz. This work not only presents a new principle in the field of vibration energy harvesting but also greatly expands the applicability of TENGs.

Surface acoustic wave (SAW) vibration sensors

In the paper a feasibility study on the use of surface acoustic wave (SAW) vibration sensors for electronic warning systems is presented. The system is assembled from concatenated SAW vibration sensors based on a SAW delay line manufactured on a surface of a piezoelectric plate. Vibrations of the plate are transformed into electric signals that allow identification of the sensor and localization of a threat. The theoretical study of sensor vibrations leads us to the simple isotropic model with one degree of freedom. This model allowed an explicit description of the sensor plate movement and identification of the vibrating sensor. Analysis of frequency response of the ST-cut quartz sensor plate and a damping speed of its impulse response has been conducted. The analysis above was the basis to determine the ranges of parameters for vibrating plates to be useful in electronic warning systems. Generally, operation of electronic warning systems with SAW vibration sensors is based on the analysis of signal phase changes at the working frequency of delay line after being transmitted via two circuits of concatenated four-terminal networks. Frequencies of phase changes are equal to resonance frequencies of vibrating plates of sensors. The amplitude of these phase changes is proportional to the amplitude of vibrations of a sensor plate. Both pieces of information may be sent and recorded jointly by a simple electrical unit.