Vibration Sensor Research Articles

Investigation of Laser micro-textured triboelectric nanogenerator based self-powered vibration sensor for industry 4.0 application

Self-powered sensors detect a variety of crucial parameters for industrial processes without any external power source for Industry 4.0. It has capabilities to acquire, analyze, and make decisions on real-time data, these sensors are essential for the advancement of automated production systems and smart factories. In this regard, Triboelectric nanogenerators (TENGs) have potential to provide effective solution as self-powered vibration sensor for continuous monitoring of machine tools. Different techniques have been used to enhance the performance of TENG such as material doping, chemical etching, ion injection, etc. However, laser surface texturing is a low-cost, contact less, efficient technique which can be used for improving the response of TENG-based self-powered vibration sensors. In this context, Laser Induced Backside Texturing is a laser micro texturing approach to improve the electrical performance. It induces waviness and surface roughness, which leads to increased surface charge density and triboelectrification capabilities. In this work, laser micro texturing was performed using with 405 nm wavelength semiconductor diode laser to generate line pattern texture on the Fluorinated Ethylene Propylene (FEP). Laser textures TENG (LT-TENG) was developed with laser textured FEP as tribonegative material and pristine Aluminium as tribopositive material, the open-circuit voltage, short-circuit current and power density achieved 23 %, 41 %, and 47 % enhancement respectively, generating 794 V, 44 µA, and 2371.6 µW/cm2. This increase in output leads to an increased sensitivity of the sensor. When mounted on the machines, LT-TENG was able to detect their working state, vibration frequency, and harmonics i.e. 46, 92, 184 Hz for the vacuum pump and 140, 280, 410 Hz for the heat gun. These detected vibration frequencies, and their harmonics were similar to the frequencies detected by the commercial accelerometer (CTC AC115), confirming potential use of LT-TENG as a low-frequency vibration sensor. The developed LT-TENGs was integrated with the Internet of Things (IoT) supported microcontroller, which collects real-time data for continuous monitoring and analysis for machine health monitoring in smart devices.

Evaluation of the Diagnostic Sensitivity of Digital Vibration Sensors Based on Capacitive MEMS Accelerometers.

In recent years, there has been an increasing use of digital vibration sensors that are based on capacitive MEMS accelerometers for machine vibration monitoring and diagnostics. These sensors simplify the design of monitoring and diagnostic systems, thus reducing implementation costs. However, it is important to understand how effective these digital sensors are in detecting rolling bearing faults. This article describes a method for determining the diagnostic sensitivity of diagnostic parameters provided by commercially available vibration sensors based on MEMS accelerometers. Experimental tests were conducted in laboratory conditions, during which vibrations from 11 healthy and faulty rolling bearings were measured using two commercial vibration sensors based on MEMS accelerometers and a piezoelectric accelerometer as a reference sensor. The results showed that the diagnostic sensitivity of the parameters depends on the upper-frequency band limit of the sensors, and the parameters most sensitive to the typical fatigue faults of rolling bearings are the peak and peak-to-peak amplitudes of vibration acceleration. Despite having a lower upper-frequency range compared to the piezoelectric accelerometer, the commercial vibration sensors were found to be sensitive to rolling bearing faults and can be successfully used in continuous monitoring and diagnostics systems for machines.

A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions

Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time.Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters.This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence.The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.

Dynamic Data-Driven degradation method for monitoring remaining useful life of cutting tools

Monitoring tool condition and remaining useful life (RUL) are vital in preventing the occurrence of excessive tool wear. This paper develops a novel dynamic data-driven degradation method for monitoring the RUL of cutting tools. In sensor-data collection, vibration, sound, and power external sensors and built-in data are gathered from the machine tool. In multi-feature selection and dynamic model updating, a decision-level fusion method of spanning multi-domain features is designed to dynamically utilize a global prediction error for selecting and fusion degradation features, which are associated with the cutting tool life in the degradation model. A rolling HI-RUL mapping is established in RUL prediction by employing a historical health indicator curve, which estimates the RUL of cutting tool with a given threshold. The effectiveness of proposed method was assessed through two run-to-failure experiments of cutting tools, showing that an average global prediction error is reduced to 4.07%.

Microwave sintering of CaBi2Nb2O9 ceramics for improved piezoelectric response and electrical resistivity

AbstractCaBi2Nb2O9 (CBN) ceramic is a promising sensing element to convert vibration to electrical signal at temperatures higher than 600°C. However, conventionally sintered ceramics suffer from poor piezoelectric coefficient (d33) and low electrical resistivity (ρdc). Here, we report that CBN ceramics can be prepared by microwave sintering (MS) to mitigate volatilization issue of Bi2O3 and thus suppress the generation of oxygen vacancies usually seen in conventional sintering (CS) as demonstrated by X‐ray photoelectron spectroscopy (XPS) analysis and mass‐loss measurement. As compared to the CS, the MS is more favorable for reducing the sintering time, and obtaining a dense, fine, and uniform grain morphology as revealed by scanning electron microscopy (SEM) characterizations of both surface and interior of the CBN ceramics, leading to an enhancement of 86% and 75% in d33 and ρdc, respectively. Piezoresponse force microscopy (PFM) as combined with Rayleigh law analysis clearly revealed that the enhancement of piezoelectric properties was attributed to the thinner domains, the higher domain wall density, and the enhanced domain wall motion in the MS‐940 samples. This study paves an important road to simultaneously improve both d33 and ρdc in CBN ceramics for developing high‐temperature vibration sensors.

Novel Leak Detector Based on DWT an Experimental Study

We always face water leakage problems in underground distribution water networks (DWNs). Existing leak detectors suffer from false alarms due to poor leak signal quality affected by external noise, often collected by acoustic or vibratory sensors. This paper introduces a novel Discrete Wavelet Transform Detector (DWTD) that leverages precise pressure signals non-influenced by environmental noise. Using a prototype of a 100m PEHD pipeline and a diameter of 40mm, Data from two pressure transmitters were collected using a dSPACE MicroLabBox unit. The main idea is to apply the Discrete Wavelet Transform (DWT) with a DONOHO threshold law to cancel noises due to water turbulence fluctuations, ensuring high-quality signals for accurate leak detection and localization. As benchmarks to assess the quality of denoising signals three parameters were calculated, Signal to Noise Ratio (SNR 26.6763 dB), Normalized Cross-Correlation (NCC≈1), and Mean Square Error (0.20573 MSE 48.4761). The denoised temporal signals are obtained from the Inverse Discrete Wavelet Transform (IDWT). A Cross-correlation is employed to these signals to determine the leak’s location. The experimental validation involves positioning the first and second transmitters at specific distances on both sides of the leak position. This allows for comparison between the actual leak position in advance known and calculated positions at various points and leak sizes. With only a few exceptions where the maximum error rate reached 5 meters from the actual leak position, the detector's effectiveness was proven across tests involving four different leak sizes.

Single-Sided Deafness at a Tertiary Care Rehabilitation Centre: A Cross-Sectional Retrospective Study.

To estimate the Single-sided deafness (SSD) among sensorineural hearing loss (SNHL) in Tertiary Care Rehabilitation Centre. To determine the audiological characteristics, associated and medical problems, and rehabilitative approaches among SSD. A retrospective study was conducted to report on demographic details, audiological evaluation, and hearing aid management among SSD clients. A total of 11,534 cases of SNHL were reported to the Audiology department at the tertiary care rehabilitative center at Mysuru between January 2014 and December 2017. A total of 225 were diagnosed as having SSD, accounting for approximately two cases of SSD in a cohort of 100 SNHL cases (225/11,534*100). In addition, males are affected more than females. SSD is found to have more in the right ear than the left ear. Their major complaints were tinnitus and vertigo, apart from reduced hearing sensitivity from the affected ear. In the side of the ear with profound hearing loss among SSD clients showed normal middle ear status with absent acoustic reflexes, otoacoustic emissions, and auditory brainstem response. Only 54 individuals (24%) out of 225 SSD clients underwent a hearing aid trial. Twenty-one (9%) SSD clients purchased a hearing aid, and a minuscule seven (3%) of 225 SSD clients are using a hearing aid. The preference for hearing aid uptake was low because of the experience of vibratory sensation and having near-normal hearing in one ear. Two cases of SSD were observed among SNHL and they are less likely to get benefits from the hearing aid.

DetachSense rubber bearing for convenient axial load prediction using active sensing and Bayesian-optimized deep neural network

Rubber bearings serve as key components in seismic isolation structures, rendering accurate monitoring of their axial loads essential. In previous research efforts, a smart rubber bearing (SRB) was introduced based on an active sensing method to achieve axial load monitoring. However, maintaining the wave generator and vibration sensor in the interlayer of the rubber bearing and structure presents significant challenges, making replacement of the generator and sensor difficult. Therefore, this study presents the DetachSense Rubber Bearing (DSRB) as a solution. By placing transducers on the non-loaded surface of a rubber bearing, convenient replacement and substitution of the transducers can be achieved without compromising the isolation performance of the bearing. A deep-learning framework optimized using Bayesian algorithms was employed to predict the axial load of the rubber bearing. This study examined the effects of various connection methods, feature extraction, and reconstruction techniques. Experimental tests were conducted on full-scale LNR600 DSRB bearings, and the data obtained were subjected to a prediction analysis. The results demonstrated that the combination of bolted connections and wavelet packet decomposition for feature extraction yielded the most favorable detection outcomes, with a prediction error of 0.34 MPa.

A self-powered spring-based triboelectric vibration sensor

Abstract Self-powered vibration sensors have gained attention due to their versatility. However, a limitation of many existing self-powered sensors is their single-direction functionality, which hinders their effectiveness in capturing multidirectional human movement’s swinging motions. To address this, this study introduces an innovative self-powered vibration sensor based on the triboelectrification effect of an inverted pendulum metal ball. This novel sensor excels at detecting micro-vibrations through the freestanding sliding electrification of a metal ball using Kapton tape. The generated charge is transferred through interdigital electrodes arranged in a spiral pattern. To ensure adaptability to various motion types, the metal ball is affixed to a spring and configured as an inverted pendulum. This setup allows the sensor to detect both linear and rotary motions across a range of acceleration levels. The fabricated sensor exhibits remarkable sensitivity, measuring 0.203 V/mm. It was affixed to the human body to detect low-frequency vibrations, particularly those below 20 Hz. Impressively, it can detect millimeter-scale vibrations, even up to 3 mm, at different rotational angles (0°, 30°, 60°, and 90°). This outcome highlights the promising performance of our vibration sensor in the field of human motion monitoring, making it a significant advancement in the realm of self-powered vibration sensors.

Monitoring of ground and building vibrations caused by train wheel defects

This paper describes a study performed on train wheel defects and their effect on rail vibrations. The paper describes a test setup allowing the researchers to capture ground vibrations at the desired level of detail to prove to what extend wheel quality influences ground vibration levels and associated nuisance to people. The analysis of the measurement signals shows that the individual axles of a passing train can be detected excellently with the signal from the axle detection and the vibration sensors. The signals from various sensors in the measurement setup are synchronized with high time accuracy, making it possible to overlay the time interval of an axis passage over the signal from the vibration measurements. This makes it possible to track vibrations at bogie and axle level. The distance of measurement points from the track is ultimately the determining factor for the extent to which vibrations are dominated by an axle, a bogie, a wagon or the entire train. In addition, direct wheel roundness measurements have been performed for trains that appear to have defect wheels based on ground vibration data. With the track based measurements, the ground vibration measurements and the direct wheel measurements an unique data set has been generated, allowing for an evaluation of the importance of defect train wheel from the perspective of vibrations nuisance to the environment. Relations of measured vibrations to other parameters, both rolling stock related and environmental parameters were investigated based on the generated set of data. The analysis on the data reveals interesting relations between train wheel parameters and ground vibrations, which are discussed in this paper.

A piezoelectric vibration sensor with excellent performance based on Bi12SiO20 crystal for high-temperature applications

High-temperature piezoelectric vibration sensors play a crucial role in the accurate monitoring of the dynamic mechanical conditions in aerospace, automotive, and energy generation systems. However, the use of conventional piezoelectric materials in high-temperature environments is restricted owing to their limited Curie temperatures. In this study, we grew a piezoelectric crystal Bi12SiO20 (BSO) with the crystal cut optimized for high longitudinal piezoelectric coefficient and low piezoelectric crosstalk behaviors. Subsequently, a compression-type piezoelectric vibration sensor utilizing the BSO bulk crystal was developed and fabricated for structural health monitoring under high temperatures. The impact of pre-tightening torques on the sensor performance was investigated. Moreover, the sensor performance was analyzed under temperatures up to 650 °C. The BSO-based sensor exhibited an average sensitivity of ∼3.89 pC/g between 25 and 650 °C under 160 Hz frequency, with a variation of 5.5%. Additionally, the BSO-based sensor demonstrated ultra-stable sensitivity at 600 °C, highlighting its strong sensing capabilities and reliability under high temperatures. Thus, the BSO-based vibration sensor is a promising option for structural health monitoring applications under high temperatures.

Extending the operational range of Francis turbines: A case study of a 200 MW prototype

Francis turbines are now widely used to support the integration of renewable and intermittent energy sources such as solar and wind power. Consequently, these turbines often operate away from their best efficiency point (BEP). Such operations cause detrimental pressure fluctuations in the runner and draft tube, leading to early fatigue failures. To address these harmful flow conditions and extend the operating range of Francis turbines, a mitigation system was developed and tested on a large-scale, high-head 200 MW Francis turbine. The system consists of four circular rods placed in the draft tube with variable radial protrusion lengths, adjustable using linear actuators. Pressure, accelerometer, and vibration sensors installed on the turbine allowed quantification of the rod system performance. The results demonstrate the system’s capability to reduce pressure pulsations by up to 80 % in terms of maximum pressure amplitude and 100 % in terms of fatigue cycle in both low and high-frequency ranges, up to ten times the runner frequency, based on pressure analysis. The optimal rod protrusion ranges from 5 to 20 % of the runner outlet diameter function of the operating load. The impact of the rods’ protrusion on the turbine structure appears negligible from the accelerometer measurements performed on the draft tube and spiral casing. The hydraulic efficiency is reduced by up to 1 %. These findings are significant across a wide range of part-load operations, from 40 % to 60 % load, indicating the potential to extend the operational range of existing Francis turbines. The research presented here is a novel attempt to enhance the existing Francis turbines with a new degree of freedom using protruding rods.

A living lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics

The Nibelungen Bridge in Worms, Germany has been selected as a national demonstration structure for advanced non-destructive testing (NDT) and structural health monitoring concepts to extend the lifetime of civil structures and to optimize O&M actions. Parts of the research that involves this bridge as a demonstrator belong to the focus area program SPP100+. In this program, the bridges SHM System has been extended and combined with an additional setup of vibration sensors. The used digital smart sensor with pre-processing functions, the arrangement of the sensors at the structure and additional edge computing capability allows the investigation of transfer learning and other methods directly into the real structure. The living lab with seven triaxial accelerometers can be reconfigured in real-time and adjusted to the needs of AI models for classification. The comparison with the existing conventional SHM sensors has been made possible by hardware synchronization to the existing SHM System and collocating sensors at similar positions, so that a hardware exchange can be an investigated use-case for the transfer learning. During idle times, the system collects vibration data like a conventional SHM system. Keywords: Structural Health Monitoring (SHM), Data acquisition, Sensor technology.

X-Structure of Torque Adjustable Formed by 3-D Printing With Embedded Optical Fiber Bending for Vibration Sensing

X-Structure of Torque Adjustable Formed by 3-D Printing With Embedded Optical Fiber Bending for Vibration Sensing

Vectorial Distributed Vibration Sensing Based on Two-Beam $\phi$-OTDR and Four-Core Fiber

Vectorial Distributed Vibration Sensing Based on Two-Beam $\phi$-OTDR and Four-Core Fiber

Unsupervised Transfer Learning Across Different Data Modalities for Bearing's Speed Identification

Recent advancements in transfer learning have revolutionized predictive maintenance, enabling cross-domain generalization for components with varying characteristics and operating under different conditions. While traditional transfer learning approaches require labeled data in both source and target domains, unsupervised transfer learning strives for a more cost-effective alternative for which only labels are available in the source domain. This study investigates adversarial transfer learning between two different sensor modalities: vibration and acoustic. The goal is to enable bearing monitoring using microphones, which are, in general terms, cheaper and easier to deploy than vibration sensors; and without the need to label data in the target domain. The research goal is to identify the operating speed of a bearing testbed. The source domain data correspond to vibration measurements taken from an attached sensor, while the target domain uses a microphone array at distance. Artificial Neural Networks are used as the base architecture. Transferability is assessed with two unsupervised adversarial learning techniques: gradient reversal and deep correlation alignment. Their performance is compared to traditional supervised transfer learning via fine-tuning. Experimental results demonstrate that gradient reversal outperforms deep correlation alignment and is able to achieve results similar to those obtained with supervised transfer learning. These findings highlight the feasibility of speed identification using a microphone array and establish a baseline for future condition monitoring research with such sensors. Conclusion. These findings highlight the feasibility of speed identification using a microphone array and establish a baseline for future condition monitoring research with such sensors.

Applications of vibration sensors in medicine: Enhancing healthcare through innovative monitoring

Recently, vibration sensors, initially confined to industrial use, have emerged as pivotal tools in medical practice. This article delves into their myriad applications within healthcare, underscoring their potential to reshape patient care paradigms. From wearable gadgets to cutting-edge medical equipment, the incorporation of vibration sensors holds promises to redefine patient monitoring, diagnostics, and therapeutic strategies. By integrating these sensors, healthcare professionals gain novel insights into physiological dynamics, ultimately improving patient outcomes. The integration of vibration sensors into medical practice not only enhances the accuracy and efficiency of health monitoring and diagnostics but also opens up new avenues for personalized medicine. As these technologies continue to evolve, they hold the promise of transforming healthcare delivery, making it more responsive, proactive, and patient-centric.

Rotation Error Prediction of CNC Spindle Based on Short-Time Fourier Transform of Vibration Sensor Signals and Improved Weighted Residual Network.

The spindle rotation error of computer numerical control (CNC) equipment directly reflects the machining quality of the workpiece and is a key indicator reflecting the performance and reliability of CNC equipment. Existing rotation error prediction methods do not consider the importance of different sensor data. This study developed an adaptive weighted deep residual network (ResNet) for predicting spindle rotation errors, thereby establishing accurate mapping between easily obtainable vibration information and difficult-to-obtain rotation errors. Firstly, multi-sensor data are collected by a vibration sensor, and Short-time Fourier Transform (STFT) is adopted to extract the feature information in the original data. Then, an adaptive feature recalibration unit with residual connection is constructed based on the attention weighting operation. By stacking multiple residual blocks and attention weighting units, the data of different channels are adaptively weighted to highlight important information and suppress redundancy information. The weight visualization results indicate that the adaptive weighted ResNet (AWResNet) can learn a set of weights for channel recalibration. The comparison results indicate that AWResNet has higher prediction accuracy than other deep learning models and can be used for spindle rotation error prediction.

CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis

When the machinery device operates abnormally, it is not sufficient for fault detection only via extracting fault features from a single sensor due to the latent fault information may be scattered across multiple sensors. Multi-sensory fusion techniques with deep learning framework have attracted increasing attention from researchers due to the exploiting and integration of fault information between multiple sensors. Nevertheless, there are two remaining shortcomings in most existing multi-sensory fusion technologies. (1) Most existing fusion methods merely concentrate on conducting multi-sensory information fusion from time-domain or frequency-domain to achieve fault diagnosis, which are often unsatisfactory in the face of strong noise environments. (2) The collaborative fusion between several vibration sensors is generally considered in the past works, whereas the complementary information fusion between multi-sensory vibro-acoustic heterogeneous data are rarely studied. To address these deficiencies, this paper proposes a novel coarse-to-fine dual-scale time-frequency attention fusion network (CDTFAFN) for machinery fault diagnosis, which not only adequately considers the complementary information fusion of vibro-acoustic signal, but also has robust feature learning capabilities in a noisy scenario. Firstly, the signal-to-image encoding unit (SIEU) containing the improved constant-Q non-stationary Gabor transform (ICQ-NSGT) is introduced to convert the collected raw vibro-acoustic heterogeneous signal into time-frequency representation (TFR) and achieve the coarse-grained feature fusion. Secondly, the time-frequency attention feature fusion unit (TFA-FFU) is designed to concurrently learn the fine-grained features at two scales from the low-level fused features which are meaningful for fault diagnosis. Finally, the coarse-to-fine features are sequentially concatenated and fed into softmax classifier to preferably promote the network learning performance and automatically implement fault classification. The performance of the proposed approach is validated against those state-of-the-art results on two groups of multi-sensory vibro-acoustic data in different experimental platforms. Experiment results show that the proposed method with the diagnosis accuracy of 99 % above outperforms other several representative fusion technologies (i.e., 2MNet, MFF-GBFD, MSCNN-BiLSTM, MFAN-VAF, 1D-CNN-VAF, MI-CNN-TFT and TFFN-VAF) in the raw noise-free addition scenario. Moreover, the average testing accuracy of the proposed method can still reach 97 % above in the noisy scenarios with Gaussian white noises, which shows its competitive superiority and strong robustness against noises in machinery fault diagnosis. According to the five ensemble macro-average performance evaluation metrics (i.e., accuracy, precision, sensitivity, specificity and F1-score) and the receiver operator characteristic (ROC) analysis, our findings also emphasize the superiority of applying our method for machinery fault diagnosis under the colored noises compared with other fusion technologies (i.e., 2MNet, MFF-GBFD, MSCNN-BiLSTM, MFAN-VAF, 1D-CNN-VAF, MI-CNN-TFT and TFFN-VAF) reported in this paper.

Tactile versus motor imagery: differences in corticospinal excitability assessed with single-pulse TMS

Tactile Imagery (TI) remains a fairly understudied phenomenon despite growing attention to this topic in recent years. Here, we investigated the effects of TI on corticospinal excitability by measuring motor evoked potentials (MEPs) induced by single-pulse transcranial magnetic stimulation (TMS). The effects of TI were compared with those of tactile stimulation (TS) and kinesthetic motor imagery (kMI). Twenty-two participants performed three tasks in randomly assigned order: imagine finger tapping (kMI); experience vibratory sensations in the middle finger (TS); and mentally reproduce the sensation of vibration (TI). MEPs increased during both kMI and TI, with a stronger increase for kMI. No statistically significant change in MEP was observed during TS. The demonstrated differential effects of kMI, TI and TS on corticospinal excitability have practical implications for devising the imagery-based and TS-based brain–computer interfaces (BCIs), particularly the ones intended to improve neurorehabilitation by evoking plasticity changes in sensorimotor circuitry.