Vibration, Volume 4, Issue 2 (June 2021) – 13 articles

In the field of lightweight construction, the use of natural fibers as reinforcement in composites has been increasingly discussed. Additionally, the damping properties of natural fibers are known from fiber materials such as fiber insulation boards. In the scope of the work presented here, the focus is on identifying the potential of natural fibers for lightweight structures with high vibration damping capacity. For this purpose, test specimens made of flax fiber-reinforced and glass fiber-reinforced thermoplastic composites were manufactured and characterized. Contrary to expectations, the flax fiber-reinforced composite exhibited an almost isotropic damping characteristic. A comparison of the damping and stiffness properties determined by measurement confirms the high potential of natural fiber-reinforced materials for lightweight structures with high damping. Full article

Studies on hand-transmitted vibration exposure, biodynamic responses, and biological effects were conducted by researchers at the Health Effects Laboratory Division (HELD) of the National Institute for Occupational Safety and Health (NIOSH) during the last 20 years. These studies are systematically reviewed in this report, along with the identification of areas where additional research is needed. The majority of the studies cover the following aspects: (i) the methods and techniques for measuring hand-transmitted vibration exposure; (ii) vibration biodynamics of the hand–arm system and the quantification of vibration exposure; (iii) biological effects of hand-transmitted vibration exposure; (iv) measurements of vibration-induced health effects; (iv) quantification of influencing biomechanical effects; and (v) intervention methods and technologies for controlling hand-transmitted vibration exposure. The major findings of the studies are summarized and discussed. Full article

Manual wheelchair (MWC) propulsion can expose the user to significant vibration. Human body exposure to certain vibrations can be detrimental to health, and a source of discomfort and fatigue. Therefore, identifying vibration exposure and key parameters influencing vibration transmissibility during MWC propulsion is crucial to protect MWC users from vibration risks. For that purpose, a systematic review using PRISMA recommendations was realized to synthesize the current knowledge regarding vibration transmissibility during MWC propulsion. The 35 retrieved articles were classified into three groups: Vibration content, parameters influencing vibration transmission, and vibration transmission modeling. The review highlighted that MWC users experience vibration in the frequency range detrimental/uncomfortable for human vibration transmission during MWC propulsion depends on many parameters and is still scarcely studied and understood. A modeling and simulation approach would be an interesting way to assist physicians in selecting the best settings for a specific user, but many works (modeling, properties identification, etc.) must be done before being effective for clinical and industrial purposes. Full article

Railway bridges are an integral part of any railway communication network. As more and more railway bridges are showing signs of deterioration due to various natural and artificial causes, it is becoming increasingly imperative to develop effective health monitoring strategies specifically tailored to railway bridges. This paper presents a new damage detection framework for element level damage identification, for railway truss bridges, that combines the analysis of acceleration and strain responses. For this research, operational acceleration and strain time-history responses are obtained in response to the passage of trains. The acceleration response is analyzed through a sensor-clustering-based time-series analysis method and damage features are investigated in terms of structural nodes from the truss bridge. The strain data is analyzed through principal component analysis and provides information on damage from instrumented truss elements. A new damage index is developed by formulating a strategy to combine the damage features obtained individually from both acceleration and strain analysis. The proposed method is validated through a numerical study by utilizing a finite element model of a railway truss bridge. It is shown that while both methods individually can provide information on damage location, and severity, the new framework helps to provide substantially improved damage localization and can overcome the limitations of individual analysis. Full article

In this paper, the frequency response functions of rubber o-rings are investigated. A growing trend is the application of electric motors in vehicle powertrains. Electric motors exhibit a different kind of structural dynamic excitation, compared to conventional internal combustion engines. Especially the excitation frequency is multiple times higher, as in combustion engines. This increased excitation frequency is a novel requirement on materials and their modelling in simulations. For this case, a novel fixture is introduced, which constrains the rubber o-ring components in their designated conditions and which allows to perform a Transfer-Path-Analysis up to a frequency of 6.5 kHz. The evaluation of the results shows a high coherence throughout the considered frequency range, while using a high-frequency miniature shaker. The structural resonances are clearly visible and the signal noise is low. This proves the suitability of the presented fixture, in combination with the high-frequency miniature shaker. The results presented and the fixture itself serve as a basis for further work and developments. Full article

In a previous paper, it was determined that coolant fluids can have a significant influence on the acoustic emission of an electric motor. There is no appliance that can investigate and compare the transfer path analysis of isolated fluids. For this case, a new fixture is introduced and its suitability is evaluated. Furthermore, the appliance is used to measure and compare different media and their frequency response functions. The results indicate that the fixture and its repeatability are suitable to measure and compare fluids and media. A comparison of different media shows how the air medium has the lowest amplitudes for the transfer path analysis, in relation to incompressible fluids. Furthermore, differences in the transfer path analysis between both considered gearbox oils are marginal. While the utilization of air as a coolant medium is not always possible, due to thermal issues, the presented fixture can help find an acoustically suitable coolant medium for future applications in electric motors. Additionally, it is possible to investigate the influence of temperature and pressure conditions on the transfer path analysis of fluids. Full article

The present study involved performing an experiment to clarify whether vibration measurement values on the tool handle, in accordance with ISO 5349-1, can assess risk from workplace environments. The study investigated the relationship between the vibration magnitude of a hand-held electric drill with different operating postures. The experiment included the determination of the participant’s temporary threshold shift (TTS) of vibrotactile perception threshold (VPT) at the tip of the index finger. The experimental hypothesis was that the vibration measurement values on the tool handle, in accordance with the ISO 5349-1 standard, include the effect of posture on the vibration measurements obtained despite the variation in posture and test participants. The hand-transmitted vibration (HTV) was applied using a hand-held electric drill applied to a pre-cast concrete paving slab substrate (600 × 600 × 50 mm, 55 MPa) using a 10 mm diameter masonry drill bit (without hammer action). The tool was operated using the right hand on twelve male subjects with three working postures (n = 36). Vibration was measured in three orthogonal directions according to the international standard ISO 5349-1 procedure. Vibration magnitudes were expressed as root-mean-square (r.m.s.) acceleration, frequency-weighted using the $W_h$ frequency weighting. Clause 4.3 states that the characterisation of the vibration exposure is assessed from the acceleration of the surface in contact with the hand as the primary quantity. The experimental results indicate that the TTS following vibration exposure is not related to the measured vibration magnitude on the tool handle. Therefore, the automatic inclusion of posture and test participant variation is not proven. The results suggest that the vibration measurement values on the tool handle do not predict the TTS after hand-transmitted vibration in varying posture across the test participants. The research concludes that tool handle vibration measurement, in accordance with ISO 5349-1, does not properly assess the potential hazard from authentic workplace tool usage conditions of varying postures. Full article

The flutter phenomenon is a potentially destructive aeroelastic vibration studied for the design of aircraft structures as it limits the flight envelope of the aircraft. The aim of this work is to propose a heuristic design of a piezoelectric actuator-based controller for flutter vibration suppression in order to extend the allowable speed range of the structure. Based on the numerical model of a three degrees of freedom (3DOF) airfoil and taking into account the FEM model of a V-stack piezoelectric actuator, a filtered PID controller is tuned using the population decline swarm optimizer $P_{D}SO$ algorithm, and gain scheduling (GS) of the controller parameters is used to make the control adaptive in velocity. Numerical simulations are discussed to study the performance of the controller in the presence of external disturbances. Full article

Driving over rising and falling edges on roads and pavements, rails, manhole covers, or transverse joints can influence the driving impression regarding the driver’s perception of vibrations and acoustics. To be able to describe this, objective parameters are used to make the subjective ride comfort measurable and scalable. Previous studies have already contributed to the investigation of the subjective perception regarding the interaction of vibrations and acoustics. However, the results were individual. Aimed at improving the quality of objective analysis methods, driving maneuvers were performed in a real vehicle to investigate the interaction of vibrations and acoustics due to transient road excitations. For this purpose, a sound reproduction system was used, which could provide the acoustic environment for the driver to adapt to while driving. With this method, subjective ratings by varying vibrations and acoustics were collected and with reference to objective parameters statistically evaluated. The results showed that both tactile and audible vibrations under transient influences had no significant interactive effects on the driver’s perception. Full article

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm. Full article

Footbridges are generally slender and lightweight structures with low stiffness, designed to support dynamic loads generated by crowds. Therefore, these structures are exposed to vibration problems related to the resonance of human walking step frequencies and the lower vibration modes. To mitigate these problems, one of the most applied corrective strategies is the installation of tuned mass damper (TMD) systems that aim at the vibration reduction of the footbridge’s dominant mode. A fundamental matter in both the footbridge and the TMD design is the pedestrian load modelling, generally considered as a deterministically moving force or a biodynamic model. However, as human gait is a random process, the deterministic models can lead to non-realistic results, directly affecting the TMD system efficiency. In contrast, the use of probabilistic distributions to simulate the human walk randomness can lead to more reliable time series predictions. In this paper, a random walk (RW) algorithm is developed and applied to simulate different crowd scenarios using a simplified plane model of a coupled human-structure-TMD system. In each scenario, the TMD efficiency in reducing the vibration amplitudes is assessed. Results highlight the importance of considering the walking randomness and pedestrians’ dynamic properties in the TMD design. Full article

The article is devoted to the practical problem of computer simulation of the dynamic behaviour of horizontal axis wind turbine composite rotor blades. This type of wind turbine is the dominant design in modern wind farms, and as such its dynamics and strength characteristics should be carefully studied. For this purpose, in this paper the mechanical model of a rotor blade with a composite skin possessing a stiffener was developed and implemented as a finite element model in ABAQUS. On the basis of this computer model, modal analysis of turbine blade vibrations was performed and benchmark cases for the dynamic response were investigated. The response of the system subjected to a uniform underneath pressure was studied, and the root reaction force and blade tip displacement time histories were obtained from the numerical calculations conducted. Full article

This paper demonstrates the differences between popular transformation-based input representations for vibration-based machine fault diagnosis. This paper highlights the dependency of different input representations on hyperparameter selection with the results of training different configurations of classical convolutional neural networks (CNNs) with three common benchmarking datasets. Raw temporal measurement, Fourier spectrum, envelope spectrum, and spectrogram input types are individually used to train CNNs. Many configurations of CNNs are trained, with variable input sizes, convolutional kernel sizes and stride. The results show that each input type favors different combinations of hyperparameters, and that each of the datasets studied yield different performance characteristics. The input sizes are found to be the most significant determiner of whether overfitting will occur. It is demonstrated that CNNs trained with spectrograms are less dependent on hyperparameter optimization over all three datasets. This paper demonstrates the wide range of performance achieved by CNNs when preprocessing method and hyperparameters are varied as well as their complex interaction, providing researchers with useful background information and a starting place for further optimization. Full article