Search

One of the challenging problems in the research and development of vibration sensors relates to the formation of Ohmic contacts for the removal of an electrical signal. In some cases, it is proposed to use arrays of carbon nanotubes (CNTs), which can serve as highly elastic electrode materials for vibration sensors. The purpose of this work is to study the effect of a current-collecting layer of CNTs grown over silicon on the properties of a lead zirconate titanate (PZT) film, which is frequently employed in mechanical vibration sensors or energy harvesters. For the experiments, a vibration sensor mock-up was created with the PZT-CNT-Ni-V-SiO2-Si and PZT-CNT-Ni-V-Si structures where an array of vertically oriented CNTs was grown over an oxidized or high-alloyed silicon substrates by plasma chemical deposition from a gas phase. Then, a thin film of PZT was applied to the CNT layer with a high-frequency reactive plasma spraying. For comparison, the PZT film was applied to silicon without a CNT layer (PZT-Si structure). The calculated average value of the piezoelectric module is 112 pm/V for the Ni-PZT-PT-Ni-Si-SiO2 sample, and 35 pm/V for PZT-Ni-SiO2-Si. It can be seen that the contact realized with the help of CNT ensures more than three times the best efficiency in terms of the piezoelectric module. The value of the piezoelectric module of the vibration sensor with the PZT-CNT-Ni-V-Si structure was 186 pm/V, and the value of the residual polarization was 23.2 µC/cm2, which is more than eight and three times, respectively, higher than the values of these properties for the vibration sensor with the PZT-Si structure. It is shown that the vibration sensor can operate in the frequency range of 0.1–10 kHz. [ABSTRACT FROM AUTHOR]

The drilling process requires real-time measurement of drill string vibration, which is crucial for drilling and downhole safety. However, the traditional power supply mode used for downhole vibration sensors has been found to increase drilling costs and reduce drilling efficiency. Therefore, sensors with self-powered capabilities are considered more suitable for practical conditions. A downhole arrayed self-powered deformable vibration sensor was proposed based on the principle of triboelectric nanogenerators. Experimental results demonstrate that the sensor synchronized measurements of amplitude and frequency. The vibration frequency is measured within a range of 0 Hz to 11 Hz, with a measurement error of less than ± 4% . Additionally, the sensor is able to measure three discrete amplitude values (10, 25, 40 mm) with a measurement error of ± 3 mm. The sensor can working normally within a temperature range of 0 ℃ to 85 ℃. Furthermore, the sensor has power generation capabilities, with experiments revealing a maximum output power of 8. 3 × 10-7 W. Notably, when multiple sensors are used in parallel, the power generation capacity is significantly enhanced. These research findings provide new insights for the development of downhole sensors and downhole generators. [ABSTRACT FROM AUTHOR]

This paper presents a Fiber Bragg Grating (FBG)-based vibration sensor to measure the low frequency signals. The low frequency vibration signals are induced in heavy vehicle transport industries, coal mines, and oil and gas exploration industry. These low frequency vibration affects the musculoskeletal, vascular, and neurological systems of human body working with heavy vehicles and vibration-generating tools. A U-shape structure with a cantilever beam attached to FBG is proposed and illustrated experimentally. The sensitivity and resonant frequency of the vibration sensor are extrapolated theoretically, and the structural parameters were optimized by MATLAB and verified through COMSOL-based FEM analysis. The experimental result shows that the resonant frequency of the proposed vibration sensor is 94 Hz, the sensitivity in z-direction is 140 pm/g within the flat frequency range 0.5–70 Hz, and the lateral interferences are 3.94% and 2.8% in x and y directions, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vibration signals at the bottom of the drill string during geological drilling are crucial for lithological identification and drilling parameter optimization. However, existing downhole vibration sensors suffer from limitations in power supply and reliability. This study proposes a self-powered vibration sensor with high redundancy based on the triboelectric nanogenerator principle, which is capable of measuring both axial and transverse vibrations, thereby reducing the dependence on external power sources. The experimental results show that the sensor can measure axial vibration frequencies ranging from 0 to 11 Hz with an error of less than 4% and transverse vibration frequencies ranging from 0 to 5 Hz with an error of less than 5%. It can operate stably in temperatures from 0 to 180 °C and relative humidities from 0 to 95%. The sensor's axial vibration measurement features six identical measurement structures, providing high redundancy and effectively enhancing its reliability. Furthermore, the sensor exhibits power generation capabilities. When an external load of 1 MΩ is applied to the axial measurement module and 10 MΩ to the transverse measurement module, the sensor achieves its maximum power output for both axial and transverse measurements, reaching 32.4 × 10−9 W and 2.1 × 10−9 W, respectively. Compared to traditional bottom-of-the-hole vibration sensors, this sensor possesses self-powering capabilities and high reliability, which can improve the operational efficiency and hold significant practical value for future applications. [ABSTRACT FROM AUTHOR]

Slope monitoring has become mandatory in opencast mines. Despite various slope monitoring systems, Wireless Sensor Network is faster and more efficient. It is essential to test the sensors before installation in the field to assess their effectiveness and suitability for a particular slope condition. The significant factors affecting the slope's stability are soil moisture and vibration intensity. This study aims to analyze the functions and performance of sensors used in the advanced real-time wireless slope monitoring system. The sensors were validated by comparing the results from the lab experiment with existing methodologies like the Gravimetric method and Geophone method, then installed in a slope model of clay built on a laboratory scale to analyze their performance. The results are drawn and discussed in conclusion. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PM) is a data-driven approach to performing proactive maintenance by analyzing the condition of the equipment in any industrial setting. The high-precision sensors are widely adapted to meticulously analyze critical maintenance conditions using such a datadriven approach. In a similar context, a fiber brag grating (FBG) sensor is a passive and high-precision sensor that is widely used in industries where conventional sensors are not preferred. Broadly, this article presents four sub elements of the proposed integrated system such as the design of the sensor element, signal processing scheme (SPS), machine learning (ML) model for predicting anomalies, and decision support system (DSS) to suggest maintenance actions. Also, this article highlights an experimental case study on vibration monitoring and analysis of real-time signals for making proactive maintenance decisions. An FBG vibration sensor of center wavelength 1,550 nm is designed and utilized to acquire real-time vibration signatures of a rotating machine under test. A piezoelectric vibration sensor is used with the FBG sensor to compare the vibration response obtained during the test. Pre-processing of raw signals is performed using a moving average filter (MAV) followed by a low pass filter to nullify the effect of noise. To obtain proactive maintenance decisions, a DSS model is prepared by considering the processed vibration signatures. Various maintenance conditions are tested during the experimental analysis and detailed results analysis are presented. [ABSTRACT FROM AUTHOR]

Vibration sensors are widely used in many fields like industry, agriculture, military, medicine, environment, etc. However, due to the speedy upgrading, most sensors composed of rigid or even toxic materials cause pollution to the environment and give rise to an increased amount of electronic waste. To meet the requirement of green electronics, biodegradable materials are advocated to be used to develop vibration sensors. Herein, a vibration sensor is reported based on a strategy of pencil-drawing graphite on paper. Specifically, a repeated pencil-drawing process is carried out on paper with a zigzag-shaped framework and parallel microgrooves, to form a graphite coating, thus serving as a functional conductive layer for electromechanical signal conversion. To enhance the sensor's sensitivity to vibration, a mass is loaded in the center of the paper, so that higher oscillation amplitude could happen under vibrational excitation. In so doing, the paper-based sensor can respond to vibrations with a wide frequency range from 5 Hz to 1 kHz, and vibrations with a maximum acceleration of 10 g. The results demonstrate that the sensor can not only be utilized for monitoring vibrations generated by the knuckle-knocking of plastic plates or objects falling down but also can be used to detect vibration in areas such as the shield cut head to assess the working conditions of machinery. The paper-based MEMS vibration sensor exhibits merits like easy fabrication, low cost, and being environmentally friendly, which indicates its great application potential in vibration monitoring fields. [ABSTRACT FROM AUTHOR]

For distributed fiber-optic sensors, slowly varying vibration signals down to 5 mHz are difficult to measure due to low signal-to-noise ratios. We propose and demonstrate a forward transmission-based distributed sensing system, combined with a polarization-generated carrier for detection bandwidth reduction, and cross-correlation for vibration positioning. By applying a higher-frequency carrier signal using a fast polarization controller, the initial phase of the known carrier frequency is monitored and analyzed to demodulate the vibration signal. Only the polarization carrier needs to be analyzed, not the arbitrary-frequency signal, which can lead to hardware issues (reduced detection bandwidth and less noise). The difference in arrival time between the two detection ends obtained through cross-correlation can determine the vibration position. Our experimental results demonstrate a sensitivity of 0.63 mrad/με and a limit of detection (LoD) of 355.6 pε/Hz1/2 at 60 Hz. A lock-in amplifier can be used on the fixed carrier to achieve a minimal LoD. The sensing distance can reach 131.5 km and the positioning accuracy is 725 m (root-mean-square error) while the spatial resolution is 105 m. The tested vibration frequency range is between 0.005 Hz and 160 Hz. A low frequency of 5 mHz for forward transmission-based distributed sensing is highly attractive for seismic monitoring applications. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many people die from not wearing helmets. They also die from delayed treatment. Remote accidents are hard for emergency services to detect. Helmets save lives. Early treatment prevents 60% of accident deaths. This scheme will ensure riders wear helmets and call 911, if they crash. Bike accidents are rising as our country’s bikers. Many deaths occur due to not wearing helmets and not receiving prompt medical attention. The project protects bikers from traffic accidents. The primary aim of this work is to detect smart helmets and report accidents. The system uses sensors, Wi-Fi processors, and cloud computing. The processor checks accelerometer values from the accident detection system for irregularities. Cloud-based services send emergency contacts accident details. GPS locates vehicles. A smart helmet ‘Konnect’ guarantees real-time, verified accident information. Thus, a smart helmet for accident detection uses smart city’s ubiquitous connectivity. [ABSTRACT FROM AUTHOR]

Marine pipeline vibration condition monitoring is a critical and challenging issue, on account of the complex marine environment, while powering the required monitoring sensors remains problematic. This study introduces a vibration sensor based on a ball triboelectric nanogenerator (B-TENG) for marine pipelines condition monitoring. The B-TENG consists of an acrylic cube, polyester rope, aluminum electrodes, and PTFE ball, which converts vibration signals into electrical signals without the need for an external energy supply. The experimental results show that B-TENG can accurately monitor the frequency, amplitude, and direction of vibration in the range of 1–5 Hz with a small error of 0.67%, 4.4%, and 5%, and an accuracy of 0.1 Hz, 0.97 V/mm, and 1.5°, respectively. The hermetically sealed B-TENG can monitor vibration in underwater environments. Therefore, the B-TENG can be used as a cost-effective, self-powered, highly accurate vibration sensor for marine pipeline monitoring. [ABSTRACT FROM AUTHOR]

The vibration parameters during drilling play a critical role in enhancing drilling speed and ensuring safety. However, traditional downhole vibration sensors face limitations in their power supply methods, hindering widespread adoption. To address this challenge, our research introduces a novel solution: a hybrid downhole vibration sensor (HDV-TENG) utilizing triboelectric nanogenerators. This sensor not only enables the measurement of low- to medium–high-frequency vibrations using self-power but also serves to energize other downhole devices. We utilized a self-constructed vibration simulator to replicate downhole drilling tool vibrations and conducted a comprehensive series of sensor tests. The test results indicate that the frequency measurement bandwidth of the HDV-TENG spans from 0 to 200 kHz. Especially, the measurement errors for vibrations within the low-frequency range of 0 to 10 Hz and the high-frequency range of 10 to 200 k Hz are less than 5% and 8%, respectively. Additionally, the experimental findings regarding load matching demonstrate that the HDV-TENG achieves an output power level in the milliwatt range, representing a significant improvement over the output power of traditional triboelectric nanogenerators. Unlike traditional downhole vibration measurement sensors, HDV-TENG operates without requiring any external power supply, thereby conserving downhole space and significantly enhancing drilling efficiency. Furthermore, HDV-TENG not only offers a broad measurement range but also amplifies output power through the synergy of a triboelectric nanogenerator (TENG), piezoelectric nanogenerator (PENG), and electromagnetic power generator (EMG). This capability enables its utilization as an emergency power source for other micropower equipment downhole. The introduction of HDV-TENG also holds considerable implications for the development of self-powered underground sensors with high-frequency measurement capabilities. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background. In the context of sanctions pressure, the issue of production intensification has again become relevant. Increasing production efficiency through the introduction of new technologies is no longer just a global trend, but one of the conditions for survival, while digitalization as one of the main drivers of growth and development plays a crucial role here. The purpose of the study is to substantiate the option of a rational location for the vibration sensor of the diagnostic module of the information and measuring system for monitoring technological systems. Results. Using the example of the BRIDGEPORT milling machine (model Hardinge GX 600) an option for forming a "vibration passport" is proposed based on the recorded vibrodynamic characteristics during machine operation, the analysis of which made it possible to determine the location of the vibration sensor.

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. [ABSTRACT FROM AUTHOR]

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 ( R 2 ), 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Downhole drilling tool vibration measurement is crucial for drilling exploration safety, so real-time monitoring of vibration data is required. In this research, a honeycomb triboelectric nanogenerator (H-TENG) capable of adapting to various downhole environments is proposed. It can measure the frequency of downhole drilling equipment's vibrations and transfer mechanical energy to electrical energy for use in powering other low power downhole meters. In order to preliminarily verify the possibility of sensors used for vibration measurement of downhole drilling tools, we built a simulated vibration platform to test the sensing performance and vibration energy collection performance of H-TENG. According to the testing results, the measurement range of vibration frequency and amplitude are 0 to 11 Hz and 5 to 25 mm, respectively, and the corresponding measurement errors are less than 5% and 6%, respectively. For vibrational energy harvesting, when four sensors are wired in series with a 107 resistance, the maximum power is approximately 1.57 μW. Compared to typical methods for measuring downhole vibration, the honeycomb triboelectric nanogenerator does not need an external power source, it has greater reliability and output power, and it can vary its shape to adapt to the complicated downhole environment. In addition, the H-TENG can be combined freely according to the diameter of the drill string, and even if one sensor unit is damaged, the other units can still be used normally. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Predictive maintenance is required for Induction Motor (IM) to avoid sudden breakdown. In this paper, multimodal sensor data are used for prediction of the rotor slot width variation during runtime of IM. Moreover sensor data are visualized through visual image obtained using Scale Invariant Feature Transform (SIFT) and matching dictionary for various faults in motor such as overload, high speed, Broken Rotor bar (BRB), and unbalance magnetic pull. The multimodal sensor signals data acquired from different parts of three phase induction motor are analysed using various transforms such as Over Complete Rational Dilation Wavelet Transform (ORaDWT), Tuneable Q Wavelet Transform (TQWT), Polynomial Chirplet Transform (PCT) and Dyadic Wavelet Transform (Dyadic WT) for various fault conditions and rotor slot width mentioned during runtime condition of motor. The rotor slot width is predicted using Multiple Linear Regression (MLR), Polynomial Regression (PR), Logistic Regression (LR) and Soft-max Regression (SR) methods through the mean value and energy band of the acquired sensor signal. The Dyadic WT and SR perform better for rotor width prediction. The proposed method such as Dyadic WT and SR provides the prediction accuracy of about 95.2%. From experimental results the rotor slot width expansion more than 2% needs immediate attention to avoid breakdown of motor. [ABSTRACT FROM AUTHOR]

We experimented analyzing motor vibration with aid of Raspberry Pi when, at that time, the engine vibration was abnormal. The Pi signal is transmitted to a relay by the motor supply disconnection. The control unit, nevertheless, monitors and sends the data to the storage system in good form with proper temperature. A FO-PID controller is utilized to analyze the effects of IM due to harmonic current, vibration, and noise. The induction motor's response to harmonic and current fluctuations is stabilized by a FO-PID controller. The findings can be displayed on the mobile. The tests were carried out in a static state of vibration condition, and fast Fourier transformation is used to analyze the measured vibration data signals. The results of this model were based on the convolutional neural network (CNN), which considerably monitors early diagnostics of the vibration. With a maximum delay of around 1 s, the controller can forward cloud vibration data. Using the CNN model train to analyze the performance of the classification accuracy, the stored data are collected. This article offers a novel way of building tools for measuring vibration in real time based on the schematic architecture provided by the Python mode. [ABSTRACT FROM AUTHOR]

Purpose : Induction Motor (IM) is used in various industrial application such as drilling, rolling, paper mills, lathe machine and milling. The faults in IM are avoided through continuous monitoring and control, during running conditions. This research paper presents an innovative method for detecting motor faults such as unbalanced voltage, phase reversal, frequent starting, abnormal speed, low voltage, phase loss, turn-to-turn short circuit, shaft bend and predicting Rotor Slot Size Variation (RSSV) during motor faults. Methods: The RSSV and rotor faults of the IM are due to magnetic and Thermal Stress (TS). Magnetic and thermal stresses are measured through GMR, and temperature sensor. The vibration sensor measures abnormal motor vibration during rotor faults. In this proposed method, Least Square Support Vector Machine (LS-SVM) and Symmetrized Dot Pattern (SDP) are used to match the sensor signals of normal and abnormal motors and identify motor faults. Results: The signals collected from IM are changed into 2D images using the concept of SDP, which shows the difference among various induced faults through dot patterns of signal data points. Energy band values are obtained from the GMR sensor signal using Scale Invariant Feature Transform (SIFT) and Microscopic Camera Image Values (MCIV) are provided to the Multiple Linear Regression (MLR) for predicting the RSSV. Conclusion : The prediction accuracy of rotor slot size through the proposed technique is 94.8%. From the experimental results, if the RSSV is more than 2% of the standard size, leads to heavy damage to the IM. [ABSTRACT FROM AUTHOR]

In order to improve the operation stability of high-speed railway vehicles, an emergency braking control method for high-speed railway vehicles based on the identification of foreign matters on the track surface is proposed. The dynamic frame scanning method is used to build the image acquisition of abnormal objects on the high-speed rail track surface, and the visual sensor and vibration sensor are used to identify and process the features of foreign objects on the high-speed rail track surface. According to the abnormal feature extraction results, the command signal in case of foreign objects is transmitted to the terminal command library of the braking control system. According to the transfer switch of the emergency braking system to display the main emergency disposal output states such as the train emergency disposal information reception, emergency disposal level, emergency disposal type and other information, the joint control method of contact pressure distribution and non-uniform heat flux is adopted, and the joint control method of control system, drive system, disc friction system and signal acquisition system is adopted. The emergency braking control module is established to realize the emergency braking control of high-speed railway vehicles according to the feature recognition results of foreign matters on the rail surface. The test results show that the control module is less affected by the vehicle wheelbase characteristics, track structure characteristics and other factors when using this method for high-speed railway vehicle emergency braking control, and has a strong ability to identify the characteristics of foreign matters on the track surface, which improves the stability and real-time performance of emergency braking. [ABSTRACT FROM AUTHOR]

RELEVANCE. Among the various methods for assessing the technical condition of power equipment, vibration diagnostics occupies an important place. Therefore, improving the quality of hardware for vibration diagnostics is currently extremely important. THE PURPOSE. To develop a vibration sensor with a new principle of operation for systems of technical vibration diagnostics of power equipment, as well as to theoretically substantiate the performance of the proposed sensor. METHODS. When solving the set goal, the main provisions of the theory of vibration diagnostics were applied. RESULTS. A design of the vibration acceleration sensor has been developed, the peculiarity of which is that it is excited by means of constructive coupling capacitors, the first electrode of which is made in the form of a thin-walled metal split cylinder, the role of the second electrodes is performed by the lower row of the winding of each measuring coil, and the function of the inertial element is performed by a screen made in the form of a metal ring fixed on a suspension in the form of a membrane. Based on the electrical equivalent circuit of the sensor, a theoretical substantiation of its operability and efficiency was made. Variants of circuit solutions for the primary and secondary measuring transducers of the measuring device for vibration diagnostics systems have been developed. The primary measuring transducer contains directly a vibration displacement sensor, a sinusoidal voltage source, an input differential measuring amplifier and an analog filter containing a narrow-band filter, a broadband amplifier and a buffer amplifier. The secondary measuring transducer contains a quadrature detector of information signals, an output buffer amplifier and a quadrature reference voltage driver. A block diagram of the hardware solution for the vibration acceleration registration system is also proposed. CONCLUSION. The developed sensor has improved output characteristics and can be used not only for vibration control of power equipment, but also for measuring mechanical vibrations in technical diagnostic systems of various other machines and mechanisms, as well as for tasks of spectral seismic exploration.

The vibration parameters during drilling play a critical role in enhancing drilling speed and ensuring safety. However, traditional downhole vibration sensors face limitations in their power supply methods, hindering widespread adoption. To address this challenge, our research introduces a novel solution: a hybrid downhole vibration sensor (HDV-TENG) utilizing triboelectric nanogenerators. This sensor not only enables the measurement of low- to medium–high-frequency vibrations using self-power but also serves to energize other downhole devices. We utilized a self-constructed vibration simulator to replicate downhole drilling tool vibrations and conducted a comprehensive series of sensor tests. The test results indicate that the frequency measurement bandwidth of the HDV-TENG spans from 0 to 200 kHz. Especially, the measurement errors for vibrations within the low-frequency range of 0 to 10 Hz and the high-frequency range of 10 to 200 k Hz are less than 5% and 8%, respectively. Additionally, the experimental findings regarding load matching demonstrate that the HDV-TENG achieves an output power level in the milliwatt range, representing a significant improvement over the output power of traditional triboelectric nanogenerators. Unlike traditional downhole vibration measurement sensors, HDV-TENG operates without requiring any external power supply, thereby conserving downhole space and significantly enhancing drilling efficiency. Furthermore, HDV-TENG not only offers a broad measurement range but also amplifies output power through the synergy of a triboelectric nanogenerator (TENG), piezoelectric nanogenerator (PENG), and electromagnetic power generator (EMG). This capability enables its utilization as an emergency power source for other micropower equipment downhole. The introduction of HDV-TENG also holds considerable implications for the development of self-powered underground sensors with high-frequency measurement capabilities.

Vibration sensors are prone to bias, drift, and other failures. To avoid misjudgments in state monitoring systems and potential safety accidents caused by vibration sensor failures, it is significant to diagnose the faults of vibration sensors. Existing methods for vibration sensor fault diagnosis are primarily based on Deep Learning, but Extreme Gradient Boosting stands out due to its excellent interpretability, and compared to other ensemble learning algorithms, it boasts superior accuracy and efficiency. Therefore, a vibration sensor fault diagnosis method based on Extreme Gradient Boosting is proposed to diagnose seven common types of faults in vibration sensors. To prevent the model from being overwhelmed by simple negative cases during training, a new loss function named Triage Loss is designed to improve the classifier's performance. The vibration sensor fault diagnosis has confirmed the efficacy and practicality of the suggested approach. The experimental results indicate that the training of the model done using Triage Loss outperforms the training model done using the default loss function, with a maximum improvement of 5.4% accuracy, 5.45% in the F1-score, and 9.87% in the mean Average Precision under different fault rates. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Vibration is a ubiquitous phenomenon that occurs in everyday life and people are exposed to it almost all the time. Most often, vibration is measured using electromechanical devices such as piezoelectric, piezoresistive, or capacitive accelerometers. However, attention should be paid to the limitations of such vibration sensors. They cannot operate in the presence of strong electromagnetic fields. Measurements with electromechanical devices require physical contact between the sensor and the vibrating object, which is not always possible due to the design of the sensor and device. The possibility of a non-contact vibration measurement in harsh environments is provided by the technology of interferometric fibre optic sensors. This paper reports the principle of operation, design aspects, experimentation, and performance of a Mach-Zehnder interferometric setup for the measurement of vibration frequency. There are different sensing arms implemented in the interferometer: single-mode, polarization-maintaining, and tapered optical fibre. The paper emphasises the simplicity of the set-up structure and the detection capabilities based on the interferometric sensing giving the possibility of constructing a commercial vibration sensor for all industry demands. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)