Vibration Measurements Research Articles

Исследования математических моделей процессов деформаций зданий и сооружений

Purpose. The article is aimed at analyzing changes in the dynamic characteristics of a building and establishing the dependence of shear deformation accelerations on the building's storey to predict the effect of seismic vibrations on the technical condition of the building, taking into account the engineering and geological properties of the foundation. Methodology. A number of instrumental observations using geodetic devices, automated monitoring systems and engineering seismic stations were made to study the issues of assessing changes in the dynamic characteristics of high-rise buildings and structures. To create mathematical models of deformation processes of buildings and structures, the study of interaction of structural elements of the building with the engineering-geological structure of the ground in the upper and lower parts of the megapolis was proposed. Findings. The results on ground shaking intensity acceleration, effective duration, spectral coefficients and periods of spectrum maximum are obtained. The results of measurements of vibrations on different floors of a high-rise building based on instrumental records of seismic station of local and distant earthquake have been obtained. Dependences of accelerations of shear deformations on the floor of the building with regard to engineering-geological properties of the base have been established. Predictive models of deformation processes of the building taking into account dynamic characteristics of ground vibrations and energy class of earth crust movements have been proposed. Originality. The analytical technique of correlation analysis of random variables is proposed, by means of which normally distributed linear and nonlinear processes are studied to create a mathematical model of deformation processes of buildings of different storeys. Practical value. The obtained results of the study of deformation processes of buildings and their structural elements allow to predict the dynamic model of the process of displacements of points of the structure taking into account the dynamic characteristics of ground vibrations and energy class movements of the Earth's crust

Enhanced ship engine vibration reduction using magnetorheological dampers and Adaptive Neuro-Fuzzy Control System

ABSTRACT In this paper, an innovative approach is proposed to mitigate ship engine vibrations by integrating a semi-active control system with magnetorheological (MR) dampers and an Adaptive Neuro-Fuzzy Control System (ANFIS). The ANFIS controller adjusts MR damper damping force to effectively adapt to engine vibrations. A comprehensive mathematical model simulates the complex dynamics of the ship engine vibration system, validated with real-world data. The model incorporates engine vibration measurements and MR damper properties to train the ANFIS controller. Compared to passive control systems, the proposed semi-active system reduces torsional vibrations by 22.606% and longitudinal vibrations by 29.840%. The ANFIS controller dynamically adjusts damping force in response to engine conditions, ensuring optimal performance. This system effectively mitigates ship engine vibrations, improving overall ship performance and passenger comfort. Consistency between modeling and experimental results confirms the system's viability and efficacy.

A PGC demodulation scheme based on the Lissajous ellipse fitting for homodyne interferometer without using second harmonic carrier signal

This paper proposes a phase generated carrier (PGC) demodulation scheme for sinusoidal phase modulation interferometer (SPMI) that does not require a second harmonic carrier signal. In this scheme, an in-phase interference signal component with direct current (DC) bias is obtained by low-pass filtering the original interference signal, and a quadrature interference signal component is obtained by conventional means. The Lissajous figure formed by these two quadrature interference signal components is an ellipse whose center deviates from the origin of the I-Q coordinates. By fitting this ellipse, the major and minor axes and the center coordinates can be obtained. These elliptic parameters are then used to eliminate DC bias and normalize the amplitude of the quadrature signal components. Finally, the phase demodulation of the interference signal can be realized by using arctangent algorithm or differential cross multiplication (DCM) algorithm. The results of simulation and experiment have verified the validity and accuracy of the proposed PGC demodulation scheme, and show that it is insensitive to the phase modulation depth C. Compared with conventional PGC demodulation, this scheme requires lower depth of carrier phase modulation. The vibration measurement results indicate that the average signal-to-noise and distortion Ratio (SINAD) and total harmonic distortion (THD) values of demodulation results in the frequency range of 130 Hz to 250 Hz are 34 dB and -33 dB respectively. The proposed PGC demodulation scheme shows good performance in displacement measurement using SPMI.

Building structure-borne noise measurements and estimation due to train operations in tunnel

The perception of structure-borne noise is particularly salient when train passes through the tunnel under the buildings, which has a negative impact on human health. In the process of constructing buildings along metro lines, it is crucial to estimate indoor structure-borne noise levels in order to enhance design and prevent any negative impact on human comfort. This study conducted measurements of structure-borne noise, reverberation time, and train-induced vibrations in Guangzhou, China to investigate the generation, propagation, and dissipation mechanisms of structure-borne noise. An approach based on Short-Time Fourier Transform and Schroeder integral was proposed for obtaining frequency-dependent reverberation time. Additionally, a deep learning-based approach incorporating indoor vibrations, frequency-dependent reverberation time, and room parameters as inputs was proposed based on Genetic Algorithm-Artificial Neural Network. The estimated structure-borne noise levels demonstrated good agreement with measured values, indicating the feasibility of the approach. The finding of this research facilitates a clear comprehension of the generation, distribution, and dissipation mechanisms of indoor structure-borne noise for engineers while also enabling convenient acquisition of indoor structure-borne noise. The estimated noise levels can be effectively utilized during building design processes along metro lines to mitigate adverse impacts on human comfort.

Requirements for vibroacoustic methods of the quality assessment of vehicles traction electric motors

The progression and refinement of traction electric motors play a pivotal role in the enhancement of vehicles. With constant advancements in power efficiency, diagnostic tools, and reliability, electric traction motors are becoming more economical in terms of operational costs. This article underscores the significance of methods used in assessing the technical proficiency of traction electric motors, coupled with their testing techniques. The vibration characteristics of these motors serve as a prime criterion in assessing their quality throughout their lifecycle – from design and manufacturing to operation. A groundbreaking proposition made in this article is the evaluation of electric traction motors’ health based on their vibration acceleration within an expansive frequency range of 5 Hz to 10 kHz. This contrasts the conventional approach which focuses on vibration speed in a restricted frequency spectrum up to 1000 Hz, as outlined by current standards. Such permissible vibration classes are essential in ensuring the high-quality design, technical finesse, and dependability of traction electric motors. Exceeding these vibration thresholds can result in rapid wear and tear, diminishing the motor’s reliability and lifespan. Hence, motors that surpass these permissible vibrations are not advised for vehicular use. Additionally, the article sheds light on requisite equipment controls for accurately gauging vibrations and noise levels in electric motors. Delving deeper into the nuances, the methods for mounting vibration transducers and electric motors on testing platforms are elucidated. These methods are pivotal in obliterating any hindrances that could impede the precise measurement of vibrations induced by an electric motor.

Enhancing Cable Vibration Measurement at Long Distances Through Super-Resolution Reconstruction and Target Foreground Segmentation

This paper presents a comprehensive non-contact computer vision-based system for monitoring cable vibrations in cable-supported bridges, addressing challenges related to low image resolution and feature extraction difficulties at long distances. The proposed system utilizes deep learning techniques to enhance cable vibration recognition accuracy and offers a practical solution for cable monitoring without the need for target assistance. The core of the system is a novel two-stage model, which combines a super-resolution (SR) video reconstruction algorithm with state-of-the-art Resnet-34 and Swin-B models for precise target foreground segmentation. This approach significantly improves the recognition of target details and enhances the accuracy of cable vibration data in monitoring videos. Furthermore, a phase-based motion estimation (PME) algorithm is employed for precise cable vibration measurement. Field tests conducted on two cable-supported bridges validate the effectiveness of the system. The results demonstrate superior accuracy and noise immunity compared to traditional methods, achieving sub-pixel level precision with a maximum error rate below 2%. This system represents a significant advancement in non-contact structural health monitoring for long-distance cable vibration monitoring in cable-supported bridges.

Augmented-Resolution Digital image correlation algorithm for vibration measurements

Digital Image Correlation (DIC) is a widespread technique, and many different implementations were developed in the literature to optimize the algorithm performance. In this paper, a new DIC algorithm is optimized for vibration measurements, where the displacement amplitude drastically decreases with the increase in frequency. Even if high-resolution cameras are used, it is mandatory to use a DIC algorithm with reliable sub-pixel performance. DIC algorithms are generally optimized for large complex displacement fields, and they solve the sub-pixel problem by fitting correlation data through a second-order polynomial function. An innovative solution is proposed to push the sub-pixel accuracy and increase the signal-to-noise ratio of the measurement, by exploiting image resizing to achieve virtual higher resolutions and by adopting a quartic polynomial function to interpolate the displacement map with an increased number of interpolation points. The proposed algorithm is validated through synthetic and experimental data and compared to literature DIC algorithms.

Incorporation mechanisms and infrared absorption coefficients of water in MgSiO3 orthoenstatite clarified via comprehensive NMR and vibrational spectroscopic measurements, and first-principles calculations

We performed 1H and 29Si NMR and infrared measurements, and first-principles calculations to clarify the nature of OH defects in MgSiO3 orthoenstatite. An orthoenstatite sample synthesized at 7 GPa and 1200 °C from a composition of MgSiO3 + 0.1 wt% H2O yielded two 1H MAS NMR peaks near 5.9 and 7.6 ppm that are correlated in 2D NMR spectra, and two infrared bands near 3361 and 3066 cm− 1 that correspond to the previously reported A3 and A4 bands. The first-principles calculations confirmed that they are due to a pair of protons in a Mg (M2) vacancy. The previously reported A1 and A2 infrared bands near 3687 and 3592 cm− 1 for orthoenstatite synthesized at low silica activities were confirmed to arise from four protons in a SiB vacancy. The latter is predicted to give two additional OH stretching bands associated with two strongly hydrogen-bonded O3b-H bonds with frequencies below the spectral range reported thus far. The previously reported infrared absorption coefficients were thus revised to account for the undetected bands. 1H NMR may be used to quantitatively detect all four protons (expected at 1–12 ppm). Other mantle minerals should also be examined for potentially overlooked OH defects with strong hydrogen bonding.

Real-Time Measurement and Uncertainty Evaluation of Optical Path Difference in Fiber Optic Interferometer Based on Auxiliary Interferometer.

Optical interferometers are the main elements of interferometric sensing and measurement systems. Measuring their optical path difference (OPD) in real time and evaluating the measurement uncertainty are key to optimizing system noise and ensuring system consistency. With the continuous sinusoidal wavelength modulation of the laser, real-time OPD measurement of the main interferometer is achieved through phase comparison of the main and auxiliary interferometers. The measurement uncertainty of the main interferometer OPD is evaluated. It is the first evaluation of the impact of different auxiliary interferometer calibration methods on OPD measurements. A homodyne quadrature laser interferometer (HQLI) is used as the main interferometer, and a 3 × 3 interferometer is used as the auxiliary interferometer. The calibration of the auxiliary interferometer using optical spectrum analyzer scanning and ruler measurement is compared. The evaluation shows that the auxiliary interferometer is the most significant source of uncertainty and causes the total uncertainty to increase linearly with increasing OPD. It is proven that a high-precision calibration and large-OPD auxiliary interferometer can improve the real-time accuracy of OPD measurements based on the auxiliary interferometer. The scheme can determine the minimum uncertainty to optimize the system noise and consistency for vibration, hydroacoustic, and magnetic field measurements with OPDs of the ~m level.

A Review of Recent Advancements in Knock Detection in Spark Ignition Engines

In gasoline engines, the combustion process involves a flame’s propagation from the spark plug to the cylinder walls, resulting in the localized heating and pressurization of the cylinder content ahead of the flame, which can lead to the autoignition of the gasoline and air. The energy release from the autoignition event causes the engine cylinder to resonate, causing an unpleasant noise and eventual engine damage. This process is termed as knock. Avoiding knock has resulted in limiting the maximum engine pressures, and hence limiting the maximum efficiencies of the engine. Modern engines employ knock sensors to detect resonances, adjusting the spark plug timing to reduce pressures and temperatures, albeit at the expense of engine performance. This paper sets out to review the different signals that can be measured from an engine to detect the start of knock. These signals traditionally consist of the in-cylinder pressure, the vibrations of the engine block, and acoustic noise. This paper reviews each of these techniques, with a focus on recent advances. A number of novel methods are also presented, including identifying perturbations in the engine speed or exhaust temperature; measuring the ion charge across the spark plug leads; and using artificial intelligence to build models based on engine conditions. Each of these approaches is also reviewed and compared to the more traditional approaches. This review finds that in-cylinder pressure measurements remain as the most accurate for detecting knock in modern engines; however, their usage is limited to research settings. Meanwhile, new sensors and processing techniques for vibration measurements will more accurately detect knock in modern vehicles in the short term. Acoustic measurements and other novel approaches are showing promise in the long term.

Structural health monitoring on offshore jacket platforms using a novel ensemble deep learning model

Monitoring health condition of offshore jacket platforms is crucial to prevent unexpected structural damages, where a prevailing challenge involves translating available feature information into structural damage patterns. Although the artificial neural network (ANN) models are popular in addressing this challenge, they often fail to capture the temporal correlations between the feature information and the damage patterns, which reduce their capability for discovering the laws governing the structural damage detection. To bridge this research gap, this study proposes a novel ensemble deep learning model to enhance the temporal feature extraction to improve the damage pattern identification. In this approach, a one-dimensional Convolutional Neural Network (CNN) extracts the spatiotemporal features from the structural vibration measurements. Simultaneously, a SENet attention mechanism is introduced to select the most informatic features. Subsequently, a bidirectional long short-term memory network (BiLSTM) is employed to learn the mapping between the extracted features and the structural damage patterns. Furthermore, the particle swarm optimization (PSO) algorithm is used to optimize the BiLSTM hyperparameters to enhance its stability and reliability. Both simulations and experiments are carried out to collect the vibration responses of the offshore jacket structure in different damage scenarios. The analysis results demonstrate that the proposed method produces remarkable improvement with respect to the accuracy and robustness in identifying the structural damages when compared with the ANNs. The overall detection accuracy of the proposed CNN-BiLSTM-Attention ensemble model is beyond 95%, which provides strong applicability to practical structural health monitoring of offshore platforms.

Dynamic characteristics of stone masonry walls before and after damage: Experimental and numerical investigations

Seismic behavior of masonry walls has been heavily investigated, especially by means of laboratory experiments employing cyclic tests to determine the mechanical parameters and seismic capacity. Nevertheless, the dynamic properties of the tested walls often remain unknown, even though the nature of the seismic response is dynamic and profoundly affected by the structure's dynamic properties. This paper presents an investigation on the dynamic properties of three different masonry wall panels in healthy and damaged states, and examines if damage quantification via tracking the changes in dynamic properties is feasible. Ambient vibration and impact measurements are used for the dynamic identification of wall panels, before and after they are tested in reversed-cyclic in-plane shear-compression tests. The natural frequencies, damping ratios, and mode shapes of the walls are determined and compared to each other. Moreover, the damage progression and its effect on the dynamic features of the URM wall panel is investigated using a discrete element model of the benchmark wall that is validated in terms of the force-displacement response and damage pattern of the wall. The results of the study indicate that changes in natural frequencies and mode shapes are traceable, although it is difficult to infer damage quantification relationships from these changes. The outcomes of this study also highlight that numerical models verified with the nonlinear quasi-static behavior do not necessarily match the wall's dynamic behavior, and that more research is required to update nonlinear numerical models. Overall, the results contribute to the knowledge regarding the dynamic characteristics of masonry walls in healthy and damaged conditions, and to quantify the damage in masonry walls as well as the changes in their dynamic properties.

Application of Multiresolution Analysis Wavelet Decomposition of Vibration Signals in the Condition Monitoring of Car Suspension

The article addresses the issue of increasing the diagnostic capabilities of the car's suspension in the EUSAMA test. A new, quantitative approach was proposed to enable the assessment of the degree of wear and clearance of the lower suspension mount. An active diagnostic experiment was performed to model the clearance in the lower suspension mounting. During the research, bolts with different diameters were used. In the signal analysis, wavelet decomposition into 12 levels was performed using the Db4 wavelet. The resonance area of the system was extracted from an approximate signal, which contained 43.5% of the relative energy. From these signals, a number of point vibration measures were calculated. Finally, the maximum value was selected due to its sensitivity to the condition, which was 48% more than the original EUSAMA results. Based on the selected diagnostic parameter, a clearance model allowing for an assessment of the clearance with statistically significant coefficients was developed.

Experiment Study of Deformable Honeycomb Triboelectric Nanogenerator for Energy Collection and Vibration Measurement in Downhole

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.

Vibration Measurement and Analysis of Rotary Tools Using Millimeter-Wave Sensor

Vibration Measurement and Analysis of Rotary Tools Using Millimeter-Wave Sensor

Interfacial debonding detection for steel-concrete composite structures part I: Benchmark test and signal calibration of contact and non-contact measurement

Interfacial bonding state is one of the crucial factors affecting the bearing capacity and durability of steel-concrete composite structures (SCCS). To achieve efficient interfacial debonding detection for SCCS, contact and non-contact method-based novel non-destructive test (NDT) is performed in this study. In order to figure out the dominant frequency and vibration mode of steel plate above interfacial debonding defects in SCCS, systematic theoretical analysis and finite element simulation are carried out. Then, the contact NDT experimental study is carried out utilizing piezoelectric lead zirconate titanate (PZT) sensors and acceleration meters. Conventional and high-frequency microphones, as well as high-precision scanning laser Doppler vibrometer (SLDV), are introduced for non-contact measurement. According to benchmark test on steel plate-bonded reinforced concrete cube with preset interfacial debonding defects in different dimensions, the signal calibration with Pearson correlation coefficient is further performed to quantitatively assess the variation and difference between contact and non-contact measurements. Research findings indicate that the synthetic experimental testing platform integrating contact vibration measurement, non-contact SLDV, and acoustic vibration measurement established in this study is capable of performing non-destructive tests using impact-response method, impact-echo technique, impact-acoustic vibration approach, and modal test simultaneously. Moreover, SLDV can precisely obtain the vibration mode of steel plates above interfacial debondings. Aided by high-precision scanning laser-based vibration measurement, the accuracy and efficiency of in-situ non-contact tests can be improved. The research findings in this study can provide practical guidance on non-destructive evaluation of interfacial damages in SCCS.

Assessment of the Correlation between the Monitored Operating Parameters and Bearing Life in the Milling Head of a CNC Computer Numerical Control Machining Center

The present paper describes a study conducted at the request of the operator of machining center equipment. The operator observed undesirable indicators in terms of increased backlash and vibration of the milling head and poor quality of the machined surfaces. Vibration measurements and vibrodiagnostics were carried out before disassembling the milling head in the idle state. The bearings, lubricant, and friction regime were analyzed in the next step. The vibrodiagnostic methods used included VEL, ACC, EN2, EN3, and EN4, with recommended limits conforming to STN ISO 10816-3. The vibration values obtained indicated a problem with the bearings, exceeding the limit values. After disassembly of the bearings, abrasive wear, corrosion, and improper lubricant conditions were detected. Lubricant analysis showed the presence of abrasive and corrosive particles, indicating an unsatisfactory friction regime. Determining the optimum lubricant temperature and the effect on friction torque constituted other aspects of the study. Inspection of the bearing microgeometry confirmed unsatisfactory roundness. Furthermore, the assembly of tapered roller bearings with axial preload was analyzed with a focus on bearing stiffness, accuracy, and life. The results showed that preload improves shaft guidance accuracy and load distribution, promoting reliable operation and extending bearing life.

Torsional Vibration Analysis Using Rotational Laser Vibrometers.

Torsional vibration is a critical phenomenon in rotor dynamics. It consists of an oscillating movement of the shaft and causes failures in multiple oscillating fields of application. This type of vibration is more difficult to measure than lateral vibration. Torsional vibrometers are generally invasive and require a complicated setup, as well as being inconvenient for field measurements. One of the most reliable, non-invasive, and transportable measuring techniques involves the laser torsional vibrometer. For this research, two laser heads with different measurement capabilities were utilized. An experimental test rig was used to perform a relative calibration of the two laser vibrometers. The frequency of the acting force and the rotation speed of the shaft vary in the same range, which is commonly found in rotating machines. Finally, experimental measurements of torsional vibrations using laser vibrometers were compared with numerical results from a 1D finite element model of the same test rig. The main outcome of this paper is the definition of a reliable measuring procedure to exploit two laser vibrometers for detecting torsional mode-shapes and natural frequencies on real machines. The relative calibration of two different measuring heads is described in detail, and the procedure was fundamental to properly correlate measuring signals in two machine sections. A good correspondence between the numerical and experimental results was found.

Investigation of Vibration Characteristics during Various Building Construction Stages under Train Operations

In response to land use challenges, major urban centers have started implementing over-track building constructions above metro lines as a means of accommodating residents and workers. However, the continuous operation of trains can generate excessive vibrations that may negatively impact the overall living conditions for occupants residing in these structures. In this paper, vibration measurements were conducted on the soil and within a three-story frame structure building. Additionally, a three-dimensional finite element model of the track–soil–building was established. The wheel–rail contact force was incorporated as a dynamic load that varies with time to accurately simulate the vibration response induced by trains. According to the construction process of the over-track building, four construction stages were set up using the finite element model to study the impact of the construction stages on the vibration propagation from the soil to building structure. The results indicate that the presence of existing structures exerts a mitigating influence on soil vibrations. Pile foundation construction can effectively mitigate soil vibration to a significant extent. The findings provide references for the future development and design of over-track buildings.

Metamaterial-based passive analog processor for wireless vibration sensing

Real-time, low-cost, and wireless mechanical vibration monitoring is necessary for industrial applications to track the operation status of equipment, environmental applications to proactively predict natural disasters, as well as day-to-day applications such as vital sign monitoring. Despite this urgent need, existing solutions, such as laser vibrometers, commercial Wi-Fi devices, and cameras, lack wide practical deployment due to their limited sensitivity and functionality. Here we proposed a fully passive, metamaterial-based vibration processing device, fabricated prototypes working at different frequencies ranging from 5 Hz to 285 Hz, and verified that the device can improve the sensitivity of wireless vibration measurement methods by more than ten times when attached to vibrating surfaces. Additionally, the device realizes an analog real-time vibration filtering/labeling effect, and the device also provides a platform for surface editing, which adds more functionalities to the current non-contact sensing systems. Finally, the working frequency of the device is widely adjustable over orders of magnitudes, broadening its applicability to different applications, such as structural health diagnosis, disaster warning, and vital signal monitoring.