Vibration Monitoring Research Articles

Blasting safety criterion of existing high speed railway tunnel over tunnel

Drilling and blasting method is still an important method in the current tunnel excavation construction. Controlling the vibration effect of blasting during construction and its influence on the upper span tunnel is the key problem in tunnel construction. Based on Chongqing Science City tunnel excavation blasting project, combined with the tunnel blasting vibration monitoring and testing, this paper analyzes the propagation attenuation law of tunnel blasting vibration along the rock mass, and studies the load characteristics of the explosion stress wave propagating to the existing high-speed railway tunnel. Considering the influence of the buried depth of the blasting source, a mathematical prediction model of the attenuation law of the upper span existing high-speed railway tunnel caused by tunnel blasting is established. Based on the dynamic finite element numerical calculation method, the influence of blasting vibration on the structure of the existing high-speed railway tunnel under construction is analyzed, and the propagation and attenuation law of blasting vibration along the tunnel contour is studied. Based on the ultimate tensile stress criterion, the ultimate shear stress criterion and the Mohr criterion, and compared with the results obtained from the numerical simulation, the blasting safety criterion model of the existing high-speed railway tunnel over the tunnel is established.

Vibration Analysis at Castello Ursino Picture Gallery (Sicily, Italy) for the Implementation of Self-Generating AlN-MEMS Sensors.

This work explores the potential of self-powered MEMS devices for application in the preventive conservation of cultural heritage. The main objective is to evaluate the effectiveness of piezoelectric aluminum nitride MEMS (AlN-MEMS) for monitoring vibrations and to investigate its potential for harvesting energy from vibrations, including those induced by visitors. A preliminary laboratory comparison was conducted between AlN-MEMS and the commercial device Tromino®. The study was then extended to the Picture Gallery of Ursino Castle, where joint measurements with the two devices were carried out. The analysis focused on identifying natural frequencies and vibrational energy levels by key metrics, including spectral peaks and the Power Spectral Density (PSD). The results indicated that the response of the AlN-MEMS aligned well with the data collected by the commercial device, especially observing high vibrational energy around 100 Hz. Such results validate the potential of AlN-MEMS for effective vibration measurement and for converting kinetic energy into electrical power, thereby eliminating the need for external power sources. Additionally, the vibrational analysis highlighted specific locations, such as the measurement point Cu4, as exhibiting the highest vibrational energy levels. These points could be used for placing MEMS sensors to ensure efficient vibration monitoring and energy harvesting.

Power Plant Vibration Monitoring and Optimization Using Functional Design of Experiments

In a typical pressurized water reactor nuclear power plant, the condensate pumps contribute to the majority of the feedwater flow to the steam generators, which is converted into steam that spins the turbine generator to produce electricity. Adequate monitoring of the condensate pump feedwater flow is essential not only for the pump’s reliability but also for efficient plant operations overall. This study applies the functional principal component analysis in characterizing the vibration signals generated under six different levels of flow rates of the pump, an environmental factor. The functional design of experiments is then applied to obtain an optimal flow rate according to the target vibration curve. The obtained flow rate is found comparable to the theoretical pump curve’s best efficiency point and is recommended to be used for optimal pump performance and reliability.

Low-Cost and Paper-Based Micro-Electromechanical Systems Sensor for the Vibration Monitoring of Shield Cutters.

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.

Mechanical-computing metastructure for self-powered vibration sensing

Triboelectric nanogenerator-based vibration sensors (TVSs) present a promising approach for online vibration monitoring, offering early detection of excessive vibrations that pose risks to the functionality of devices and infrastructure. However, the structural design of most current TVSs lacks consideration for information processing capabilities, resulting in limited detection bandwidth and displacement signal fidelity. This study first introduces a quasi-zero-stiffness (QZS) mechanical-computing metastructure aimed at enhancing the structural intelligence of TVSs and extending their vibration signal detection bandwidth. The optimization of the QZS metastructure involved adjusting the length ratio and inclination angle of the zero-order polynomial beam to achieve ultra-low resonant frequencies. The results indicate that the TVS based on the mechanical-computing metastructure (MCM-TVS) is capable of efficiently detecting complex random signals, achieving an average measurement accuracy above 85 % for the spectral components of random vibration signals. Furthermore, the sensor exhibited remarkable linearity (99 %) in measuring vibration amplitude and maintained consistent sensitivity (with less than 25.8 % fluctuation), facilitating the lossless conversion of displacement to electrical signals. Consequently, this novel approach of incorporating an MCM into TVS design offers substantial insights for the advancement of high-performance vibration signal monitoring.

Self-powered system for real-time wireless monitoring and early warning of UAV motor vibration based on triboelectric nanogenerator

Unmanned aerial vehicle (UAV) is widely used in various industries due to their high flexibility and large maneuverable space. Abnormal vibration of UAV motors can prevent it from working properly due to the abnormal state of the motors and the harsh external environment. There is no suitable method to monitor the UAV motors vibration in real time. At the same time, commercial sensors require an additional power supply to power the sensor, which indirectly limited their application in UAVs. A vibration sensor (AV-TENG) with magnetic spring structure was proposed in this paper for monitoring the vibration of UAV motors. The experimental results show that the magnetic spring has excellent stability. AV-TENG can achieve frequency detection in a wide frequency band. It also has excellent linearity with a maximum error of only 0.0062 %. Simultaneously, to verify the performance of AV-TENG in various environments and simulate its loading, the results demonstrate that AV-TENG can adapt well to the working conditions of UAV. Finally, this paper constructs a low-cost (less than $7), easy-to-manufacture signal acquisition system, and conducts a field demonstration, realizing the monitoring of the vibration frequency and acceleration of the UAV motors, and realizing the early warning, which is expected to promote the development of the field of UAV as well as the practical application of the triboelectric nanogenerator.

The Elevation Effect of Vibration Induced by Pneumatic Rock Breaking with Carbon Dioxide Ice Powder

In traditional explosive blasting, the elevation amplification effect is an important aspect that cannot be ignored in the study of vibration disasters. Compared with this type of vibration, the elevation amplification effect of vibration induced by pneumatic rock breaking with carbon dioxide ice pow-der was explored through on-site vibration monitoring experiments. The results showed peak particle velocity increased when the sensor was higher than the vibration source, indicating that vibration induced by pneumatic rock breaking with carbon dioxide ice powder, like explosive blasting, has an elevation amplification effect. However, the elevation effect of vibration induced by pneumatic rock breaking with carbon dioxide ice powder mainly manifests as an amplification of the low-frequency vibration component, suppressing and weakening the high-frequency vibration. Others, the elevation amplification effect is only reflected within a certain distance. As the distance to the vibration source increases, peak particle velocity returns to its standard value and follows the existing attenuation rule. The elevation factor no longer affects the propagation and attenuation of vibration within the rock mass.

An adaptive convolutional neural network based on transmissibility grayscale image for online identification of offshore platform damage pattern

In response to the challenges in offshore platform damage pattern recognition, where traditional methods lack adaptability owing to relying on feature extraction and expert knowledge, a novel method for online recognition of offshore platform damage patterns using an improved convolutional neural network (CNN) based on transmissibility grayscale image is proposed. The original transmissibility function is converted into a two-dimensional grayscale image to capture the essential features in the original signal, which is then used as the input for the deep learning model. Subsequently, an attention mechanism is introduced to enable the CNN to focus on key areas of the image, emphasize important feature channels, dynamically adjust feature representations, and enhance its overall performance. Meanwhile, the model is optimized by virtue of variational Bayesian algorithm and early stopping criteria, and finally, the trained offline model is applied to online damage pattern recognition of offshore platforms. Through on indoor vibration monitoring experiments, seven different damage patterns of the platform model are recognized accurately, which show that the accuracy of the improved CNN model is increased by an average of 8.5 % as compared to traditional intelligent methods. Furthermore, the proposed method is applied to the online damage identification of offshore platforms in the field, and the corresponding prediction accuracy reaches 98.9 %, demonstrating the feasibility and generalizability of the proposed method in adaptive feature extraction and precise identification of damage patterns.

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.

Cooperation mechanism between the vacuum levels and interface dipole energy of triboelectric materials for real-time vibration recognition

Triboelectric Nanogenerator (TENG) device, acting as sensors of energy harvesting and material identification, are attracting intensive attention. Nevertheless, vibration wave recognition cannot be achieved by the developed devices owing to device structure and working mechanism limitations. Here, a cooperation mechanism between the vacuum level and interface dipole energy of triboelectric materials is proposed to explore triboelectric charge generation and migration. Hence, metallized cottons of high specific surface area encapsulated by polydimethylsiloxane (PDMS) of low elastic modulus are developed as TENG-based sensors to realize vibration recognition. The results indicate electrons flow from PDMS to metallized cotton with the generation of an extremely thin interfacial dipole barrier layer because the vacuum level of metallized cotton is higher than that of PDMS, resulting in the accumulation of triboelectric charges on the PDMS surface under vibration, further leading to generating distinct triboelectric signals that can be simply differentiated using signal amplitude or peak shape with 98.3 % recognition accuracy for vibration waveform. Besides, combined with the deep machine learning and triboelectric effect, a vibration perception system integrated with a TENG-based sensor, data processing and display modules is also developed for real-time vibration monitoring. This work contributes to further clarifying the theoretical mechanisms of triboelectric charge excitation under vibration.

Assessment of the intensity of intermodule interaction and the choice of means of its organization in vibration monitoring systems of turbine units

Assessment of the intensity of intermodule interaction and the choice of means of its organization in vibration monitoring systems of turbine units

Bridging the Gap: commodifying infrastructure spatial dynamics with crowdsourced smartphone data

Structural information deficits about aging bridges have led to several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of widespread deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive and has produced structural information, i.e., modal frequencies, in crowdsensing applications. Even so, current methods cannot extract spatial vibration characteristics with uncontrolled datasets that are needed for damage identification. Here we present an extensive real-world study with crowdsourced smartphone-vehicle trips within motor vehicles in which we estimate absolute value mode shapes and simulate damage detection capabilities. Our method analyzes over 800 trips across four road bridges with main spans ranging from 30 to 1300 m in length, representing about one-quarter of bridges in the United States. We demonstrate a bridge health monitoring platform compatible with ride-sourcing data streams that check conditions daily. The result has the potential to commodify data-driven structural assessments globally.

Vector-autoregressive based vibration monitoring of wind energy turbines using laser scanner profile measurements

Structural health monitoring of wind energy turbines (WET) is crucial due to the potential presence of an imbalanced rotor, leading to adverse centrifugal forces. In this research, non-contact vibration monitoring of a WET is conducted using a terrestrial laser scanner (TLS) of the type Zoller+Fröhlich IMAGER 5016 in a two-dimensional (2D) profile mode, and with a rotation speed of 55 revolutions per second. The TLS profile measurements cover the entire pillar up to a height of 100 meter. Therefore, the challenges imposed by conventional measurement systems, such as accelerometers or inductive displacement transducers, are tackled through the high spatial resolution of the TLS measurements and non-contact measurement methods. Additionally, both time and cost are reduced regarding the sensor installation. In general, the WET is measured in two different directions to account for significant movements, both in the direction of the wind and perpendicular to it. To initiate the analysis, time series are generated from the profile measurements for various positions covering the entire pillar. A robust time-domain modal parameter identification approach based on the Vector-autoregressive (VAR) process with multivariate t-distributed random deviations (VAR-RT-MPI) is proposed to estimate modal parameters, including eigenfrequencies, eigenforms, and modal damping. Besides, it allows estimating unknown auto- and cross-correlation coefficients of the VAR process, the cofactor matrix, and the degrees of freedom of the t-distribution. The VAR-RT-MPI algorithm enables to jointly estimate the eigenfrequencies and damping ratio coefficients considering multiple time series. Additionally, the spatio-temporal model of the pillar is characterised based on the estimates of the amplitudes (in a submillimetre range) and phase shifts at different positions. Vibration monitoring was conducted on a WET located in Hannover, Germany, and the results were compared with a well-known covariance driven stochastic subspace identification (SSI-COV) approach.

Vibration measurement technique by using OFDR with in-line interferometers

Due to their compact size, electromagnetic interference immunity, and compatibility with embedding in composite materials, optical fiber sensors have emerged as a versatile and integral solution for structural health monitoring (SHM) in various structures. Among the key contributors to the evolution of this field is the significant progress in optical frequency domain reflectometry (OFDR), a technology that offers unparalleled high resolution and sensitivity in distributed strain sensing, thereby establishing itself as an indispensable tool in the realm of SHM. Through the implementation of OFDR, we devised a distributed fiber optic sensor tailored for the precise measurement of vibration or strain within structures. In previous works, vibration monitoring has been achieved using an OFDR with an array of 20 in-line interferometers, serving as weak reflectors that not only facilitated vibration monitoring but also furnished valuable strain information. In this study takes a step further by introducing an innovative technique wherein in-line interferometers are strategically arranged in a non-uniform fashion along the optical fiber. To validate the practical utility of this technology, extensive simulations and experiments were conducted, utilizing a cantilever beam as a representative structure. The results obtained from these endeavors showcase the tangible potential and reliability of optical fiber sensors in the practical implementation of SHM across various applications. By enhancing the precision and scope of structural monitoring, optical fiber sensors equipped with non-uniform interferometer arrangements pave the way for more effective and comprehensive structural health assessment in diverse settings.

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.

Robust Structural Health Monitoring of CFRP Structures under Varying Loads using NSFD and Machine Learning with Combined Usage of Acceleration and Strain Data

Structural health monitoring (SHM) of objects in situ often requires a suitable combination of different methods and measurement techniques. In the field of aerospace applications, the previous research project "Combined acoustic and modal structure monitoring" dealt with the development of a robust SHM system for damage detection in carbon-fibre-reinforced polymer (CFRP) structures under varying realistic loads. The methods combined within the project were based on guided waves, acoustic emission, different vibration monitoring techniques and strain measurement. The present paper deals with the application of the nullspace-based fault detection algorithm (NSFD) combined with a machine learning approach based on strain measurements to classify different load conditions of the structure. Due to the high sensitivity to changes in the statistical properties of the measurement data, the NSFD algorithm reacts very sensitively to structural changes, but also to changes in the environmental conditions, such as changes in the external loads and the resulting stress state of the structure. In the paper, the impact of varying external loads on the NSFD damage indicator is analysed and shown. To prevent the NSFD algorithm from causing false alarms due to changes in the load conditions, but still being sensitive to small structural changes due to impact damages, the strain sensor data is used to predict the external loads on the structure. For this, machine learning is used to create clusters of similar stress states. For every cluster, a baseline for the NSFD algorithm must be made. The resulting approach is robust against changes in external loads and sensitive to small changes in the structure due to damages at the same time. The theory and the algorithms are successfully tested with measured data sets from a CFRP-airplane structure.

Evaluating Unmanned Aerial Vehicles for Vibrational Analysis: Exploiting Integrated Capabilities of Nano-Drones as Nodes in a WSN

Addressing the aging infrastructure challenge in both civil and industrial sectors has spurred the development of advanced sensor technologies and novel inspection methodologies, significantly contributing to the field of Structural Health Monitoring (SHM). While conventional sensors, such as structure-mounted accelerometers, have demonstrated high effectiveness, their deployment may be difficult or even impossible in high-risk environments like post-seismic areas, where the installation procedure hampers the operators' safety. Hence, this study aims to evaluate the characteristics of a novel sensing platform compatible with autonomous deployment for vibration inspection of infrastructures. In particular, we explore the use of Unmanned Aerial Vehicles (UAVs), with a specific focus on nano-drones, as a viable and promising solution due to their compact size and cost-effectiveness. In particular, Crazyflie 2.1 nano-drones are exploited as reference platforms, given their characteristics in terms of versatility and electrical parameters. The objective is to utilize the on-drone MEMS-based Inertial Measurement and Microcontroller Unit (IMU and MCU) to perform, at the same time, both flight control and navigation, and vibration inspection. This approach eliminates the need for additional hardware, going beyond other successful drone applications in SHM, such as visual inspections, sensors deployment, and distance measurements. The proposed solution is validated through a comparison with commercial sensors for SHM. Multiple drones can be combined to deploy wireless sensor networks in hard-to-reach locations, ready for vibration monitoring with minimal software adjustments, offering a novel approach to SHM. The results indicate that the Crazyflie drone is capable of detecting even small variations in the dynamic response of a beam, with a maximum deviation of 1% compared to commercially available counterparts designed for SHM purposes.

Vibration Monitoring of the Rock Formation Above the Hatshepsut Temple

The Hatshepsut temple in Egypt is a masterpiece of ancient architecture. Just as distinct as the temple itself is the rock formation it is carved into, and recurring rock falls in the vicinity have raised concerns regarding the temple’s safety under progressing erosion and seismic activity. Due to the material characteristics (brittle carbonate rock), high stresses could lead to rapid crack propagation, and vibra-tion-based measures may be suitable precursors for imminent rock falls. This paper describes a two-day measurement campaign on the rock towers as a preliminary study prior to the implementation of a long-term seismic station. Next to operational modal analysis, horizontal-to-vertical spectral ratios, and standard spectral ratios are evaluated to cross-validate the resonance behaviour. To the author’s knowledge, this is the first study where stochastic subspace identification (SSI) is applied for the estimation and automated tracking of natural frequencies, mode shapes, and damping ratios in rock formations. One of the advantages of this method is the inherent uncertainty quantification, meaning for each vibration record, the mean values and the standard deviations are estimated for each modal parameter, giving deep insights into the reliability of the vibration-based monitoring of rock formations.

Performance-enhanced tribovoltaic nanogenerator by regulating carrier transportation with an asymmetric heterostructure

The tribovoltaic nanogenerator (TVNG) is emerging as a promising circuit-integrative energy harvester, with notable advantages like direct current output and large current density. Nevertheless, the research on optimizing charge excitation and carrier transportation remains deficient. In this work, we report an alternative approach for practically promoting the output performance of silicon (Si)-based TVNG. A well-designed asymmetric heterostructure is achieved by inserting an appropriate interlayer between the friction layer and the bottom electrode. Carrier extraction efficiency has been promoted effectively, while carrier recombination has been restrained owing to the built-in electric field excited by p-Si/ZnO heterojunction. The coupling mechanism of the built-in electric field and the interfacial electric field has been revealed explicitly with a comprehensive theoretical model, which is based on the capacitance feature of the PN junction. The designed multilayer TVNG (MTVNG) has shown 20 times higher output compared to normal Si-based TVNGs. Apart from presenting fundamental insights into the tribovoltaic effect, we have developed a dual-mode analysis method for vibration monitoring. This work expands the path to improve TVNG output through multi-electric field coupling and provides new inspiration for miniaturized vibration sensors in real-time deployments.

Graphene-decorated polyurethane nanofiber membrane flexible sensor with different fiber orientation

Electrospun polyurethane (PU) nanofiber membranes offer desirable properties such as large strain, good resilience, light weight, good air permeability, and good adhesion to the skin; as a result, they have a unique advantage in the development of flexible sensors. To explore the application of conductive graphene (Gr)/PU nanofiber membranes in the field of strain sensing and to study the influence of the nanofiber orientation structure on the sensing performance of nanofiber sensors, electrospinning technology was adopted to prepare PU nanofiber membranes with different fiber orientations by adjusting the rotating speed of the receiving drum. Gr/PU nanofiber membranes were prepared as strain-resistance sensors, their strain-resistance sensing performance was analyzed, and the membranes were used to monitor human motion and air vibrations. The results showed that the orientation of the nanofibers in the PU nanofiber membranes could be improved by increasing the rotation speed of the receiving drum. Gr/PU nanofiber sensors have good sensing linearity, sensitivity, and stability; an increase in fiber orientation also increases sensor sensitivity, and the sensitivity factor can reach more than 200 at 50 % strain. Sensors with a low detection threshold and high detection sensitivity, which can be used for large-scale motion monitoring of human joints and small-range motion monitoring of air vibrations, have potential applications in wearable human motion health monitoring and other fields.