Vibration Information Research Articles

Research on the Sensing Properties and Vibration Reduction Performance of Self-Sensing Self-Tuning Magnetic Fluid Damper

Abstract The semi-active control damping system has gained popularity due to its quick response time and versatility. However, external sensors are susceptible to environmental interference, affecting system reliability and increasing complexity and maintenance costs, restricting their use. To address this, a self-sensing self-tuning magnetic fluid damper is proposed. The vibration-measuring induction coil is wound on the damper to sense the magnetic fluid vibration information in real time, and the vibration signal is communicated to the self-tuning control circuit. The control circuit calculates and determines the dominant frequency of structural vibration, then outputs the relevant current signal to set the damper's natural frequency to track the excitation frequency, resulting in self-tuning vibration reduction. First, the self-sensing unit's output induced electromotive force model is created, followed by an expression of the damper's natural frequency, indicating that the self-sensing unit can achieve self-tuning vibration reduction by tracking the excitation frequency. The multi-field coupling simulation model of the magnetic fluid damper is generated, and the induction coil coupling mode and damper excitation angle are defined to obtain the maximum induced voltage.
Finally, an experimental platform was developed to assess the damper's self-sensing and self-tuning vibration reduction performance. The experimental results demonstrate that the suggested self-tuning magnetic fluid damper exhibits excellent self-sensing capabilities and effectively reduces vibrations. This offers an innovative research concept for using magnetic fluid in self-sensing technology and vibration reduction domains.

Acoustic Signal Reconstruction Across Water–Air Interface Through Millimeter-Wave Radar Micro-Vibration Detection

Water surface micro-amplitude waves (WSMWs) of identical frequency are elicited as acoustic waves propagating through water. This displacement can be translated into an intermediate frequency (IF) phase shift through transmitting a frequency modulated continuous wave (FMCW) towards the water surface by a millimeter-wave radar, and information transmission across the water–air interface is achieved via the signal reconstruction method. In this paper, a novel mathematical model based on energy conversion from underwater acoustic to vibration (ECUAV) is presented. This method was able to obtain WSMW vibration information directly by measuring the sound source level (SL). An acoustic electromagnetic wave-based information transmission (AEIT) system was integrated within the water tank environment. The measured distribution of SL within the frequency range of 100 Hz to 300 Hz exhibited the same amplitude variation trend as predicted by the ECUAV model. Thus, the WSMW formation process at 135 Hz was simulated, and the phase information was extracted. The initial vibration information was retrieved through a combination of phase unwinding and Butterworth digital filtering. Fourier transform was applied to the vibrational data to accurately reproduce the acoustic frequency of underwater nodes. Finally, the dual-band binary frequency shift keying (BFSK) modulated underwater encoding acoustic signal was effectively recognized and reconstructed by the AEIT system.

Computer vision-based substructure isolation method for localized damage identification

In recent years, vision-based structural damage identification techniques have garnered significant attention due to their simplicity and cost-effectiveness. Nevertheless, dynamic response-based vision methods face challenges related to the limitations of the field of view (FOV), i.e., the extent of the FOV is often sacrificed in order to ensure sufficient monitoring accuracy, which reduces the amount of data available for structural health monitoring (SHM). This study presents a solution to this problem by employing the substructure isolation method (SIM), which focuses on local vibrations of the structure and enables local damage identification by isolating the area of interest. Additionally, a method combining kernelized correlation filter (KCF) with sub-pixel template matching is used to extract vibration information recorded by visual sensor. This strategy balances both the efficiency and accuracy of displacement extraction. In numerical simulations, the performance of the SIM based on acceleration response and displacement response is compared, demonstrating the potential compatibility of visual sensors with the SIM. Finally, the effectiveness of the proposed vision-based local damage identification method is validated through vibration testing of a shear frame model.

Load recognition of connecting-shaft rotor system under complex working conditions

A method for qualitatively recognizing the load of the rolling equipment's connecting-shaft rotor system is proposed in this paper due to the complexity of rolling production conditions and the limitations of single source response signals. The method is oriented towards fusing the vibration and motor's current information. First, singular value decomposition and wavelet packet analysis are used to preprocess the two types of response signals. Then, the Bayesian estimation method in feature-level fusion achieves qualitative recognition and analysis of rotor system load types. Corresponding load experiments are completed on a load recognition test platform based on vibration and the motor's current signals. The research results show that the load recognition method based on fusion information can recognize the type of load excitation with a recognition accuracy of 91.7 %, higher than other single-source response signal methods. Therefore, the feasibility of the aforementioned theoretical methods is verified.

Deep learning-based automated identification on vortex-induced vibration of long suspenders for the suspension bridge

Large amplitude vortex-induced vibration (VIV) of long suspenders, caused by vortex-shedding and wake flow, will decrease the service life of high-strength wires and its anchorage systems, hence listed as an essential monitoring indicator of the structural health monitoring system in the suspension bridges. Traditionally, a static vibration amplitude limitation is usually predefined and used to identify VIV from continuously monitored acceleration data of bridge suspenders. Due to the complexity of the starting vibration of the suspenders, it may not be able to flexibly adapt to the suspenders VIV under different conditions. In addition, transient but significant vibration events may not be captured since it relies only on static vibration amplitude limits without considering the temporal information of vibration. To address the above-mentioned issues, a novel framework to autonomously identify the suspenders VIV is proposed using the multimodal fusion techniques with deep neural networks. The main contributions of the methodology are: i) by calculating the wind field and vibration characteristic, two vibration response parameters are identified as indexes for the identification of suspenders VIV based on the significant difference method of big data analysis; ii) Two different modal information of time history images and power spectral density (PSD) sequences are used as inputs to the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to extract the suspenders vibration response features from the time and frequency domain perspectives, respectively; and iii) The multimodal information is concentrated and enhanced by the multi-head attention (MHA) mechanism to achieve automated identification of the suspenders VIV. The proposed framework is validated with data collected from a full-scale long-span suspension bridge in comparison with the base model. The results show that the proposed framework can efficiently identify the full-cycle development process of the suspenders VIV. This study provides a convenient strategy for suspenders VIV identification, which can be used for bridge management and vibration mitigation device design.

Examining the “unearthly archives”: Spiritualism, Psychical Research, and Psychometric Material Culture in the US and Britain

Abstract This article examines the intersection of material culture and the occult by exploring the practice of psychometry, drawing parallels between this esoteric mode of object interpretation and more conventional approaches. Coined by physician Joseph Rodes Buchanan in the 1840s, psychometry—meaning “soul or mind measurement”—is the ability of “sensitive” individuals, often women, to read an object’s electrical vibration and glean information about its history, as well as that of its owner, living or deceased. Adopted by spiritualists and psychical researchers in the United States and Britain, psychometry mediated between humans and objects, and humans and their own mortality, in the decades spanning the turn of the twentieth century. Drawing on material culture studies, consumer studies, and histories of spiritualism and psychical research, I contextualize and compare firsthand accounts of psychometric readings with more traditional approaches to object-based inquiry to contend that psychometry is a mode of affective design historical practice, a speculative design history that relies on senses other than vision, and knowledge more intuitive than rational.

Element-Specific Ultrafast Lattice Dynamics in Monolayer WSe2.

We study monolayer WSe2 using ultrafast electron diffraction. We introduce an approach to quantitatively extract atomic-site-specific information, providing an element-specific view of incoherent atomic vibrations following femtosecond excitation. Via differences between W and Se vibrations, we identify stages in the nonthermal evolution of the phonon population. Combined with a calculated phonon dispersion, this element specificity enables us to identify a long-lasting overpopulation of specific optical phonons and to interpret the stages as energy transfer processes between specific phonon groups. These results demonstrate the appeal of resolving element-specific vibrational information in the ultrafast time domain.

Vortex-Induced Vibration Performance Analysis of Long-Span Sea-Crossing Bridges Using Unsupervised Clustering

Vortex-induced vibration is a type of wind-induced vibration occurring frequently in large-span sea-crossing bridges under relatively low wind speeds, posing a threat to the structural fatigue performance and driving comfort. Identifying the instantaneous occurrence moments of vortex-induced vibration is a prerequisite for establishing a data-driven prediction model for vortex-induced vibration, and it is of great significance for the monitoring and early warning of vortex-induced vibration performance in bridges. To automatically detect the occurrence moments of vortex-induced vibration and establish a correlation model between vortex-induced vibration amplitude and environmental factors, this study proposes a fuzzy C-means clustering-based classification method. In order to detect the occurrence moments of vortex-induced vibration more finely, only short-term or even instantaneous structural vibration indicators were selected and transformed for distribution as clustering features. The entire detection process could be carried out unsupervised, reducing the manual cost of obtaining vortex-induced vibration information from massive monitoring data. Finally, actual vortex-induced vibration test data from a certain overseas bridge was utilized to verify the feasibility of this method. Based on the classification results, the correlation between vortex-induced vibration amplitude and environmental variables was determined, providing valuable guidance for predicting vortex-induced vibration amplitudes.

Reconfigurable topological gradient metamaterials and potential applications

Elastic topological metamaterials are innovative fields of the fusion of physics and mechanics. Through novel microstructural designs, elastic topological metamaterials provide a pioneering approach to precisely manipulate elastic waves, maintaining robustness and efficiency in wave propagation even within complex environments. Recent advancements in reconfigurability and gradient features have significantly enriched the mechanical phenomena of wave manipulation, broadening the application potential of elastic topological metamaterials. In this study, we design the local resonant phononic crystal, employing a spiral support structure to ensure low-frequency characteristics and adjustable mass for flexible configuration. By utilizing the designed lattices with distinct topological phases, the topological valley edge states are constructed. The frequency and group velocity variation of the topological edge states under different stiffness and mass parameters are analyzed. Furthermore, we further constructed the mass and stiffness gradient metamaterial to analyze the transmission law of elastic waves in topological metamaterials. Additionally, the fluctuation features of the wave transmittance under disordered and impurity defects were discussed, confirming the robustness of waveguides within low-frequency topological metamaterials. Finally, we explored the potential applications of the designed metamaterials in energy localization and low-frequency reconfigurable waveguides. Numerical analysis showed that the topological gradient metamaterials can enhance the vibration energy at a specific interface, and low-frequency bend waveguide paths can be adjusted flexibly by configurable mass. In summary, this paper focuses on the low-frequency and gradient features of elastic topological metamaterials, aiming to unlock their application potential in vibrational energy harvesting, vibration suppression, and information transmission, which accelerate the practical implementation of topological metamaterials.

Asymmetric-Based Residual Shrinkage Encoder Bearing Health Index Construction and Remaining Life Prediction.

Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time-frequency domain features from raw data using a priori knowledge and setting artificial thresholds; this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an Asymmetric Residual Shrinkage Convolutional Autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods.

A spectrum correlation matrix-based rapid damage identification method for joints in hinged slab bridges by sparse measurement

Hinged slab bridges are widely used in the medium and small-span bridges on highways and the joints are highly susceptible to damage under the action of the increasing traffic. However, current methods for damage identification of hinged joints, whether rely on static or dynamic testing, typically require substantial manpower, resources and time, which presents challenges for the rapid evaluation of numerous bridges. In view of this, a novel damage index named spectrum correlation matrix (SCM) is proposed and its feature extraction technology is established as well as the potential for sparse measurement is theoretically explained. Furthermore, a rapid, robust, and convenient damage identification methodology using SCM and Levenberg-Marquardt algorithm is proposed. An optimal strategy for the layout of sparse measuring points has been explored, the sensitivity and the effectiveness of the proposed method is validated through both numerical simulations and the in-situ test of a bridge in service. The results indicate that the SCM contains abundant vibration information and shows a high sensitivity to damage. The strategy for the layout of measuring points is highly efficient and the proposed methodology greatly reduces the number of measuring points compared to previous vibration-based methods, which eases rapid identification of damage for joints in hinged slab bridges.

Integrating, Validating, and Expanding Information Space in Single-Molecule Surface-Enhanced Raman Spectroscopy for Biomolecules.

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) is an ultrahigh-resolution spectroscopic method for directly obtaining the complex vibrational mode information on individual molecules. SM-SERS offers a wide range of submolecular information on the hidden heterogeneity in its functional groups and varying structures, dynamics of conformational changes, binding and reaction kinetics, and interactions with the neighboring molecule and environment. Despite the richness in information on individual molecules and potential of SM-SERS in various detection targets, including large and complex biomolecules, several issues and practical considerations remain to be addressed, such as the requirement of long integration time, challenges in forming reliable and controllable interfaces between nanostructures and biomolecules, difficulty in determining hotspot size and shape, and most importantly, insufficient signal reproducibility and stability. Moreover, utilizing and interpreting SERS spectra is challenging, mainly because of the complexity and dynamic nature of molecular fingerprint Raman spectra, and this leads to fragmentary analysis and incomplete understanding of the spectra. In this Perspective, we discuss the current challenges and future opportunities of SM-SERS in views of system approaches by integrating molecules of interest, Raman dyes, plasmonic nanostructures, and artificial intelligence, particularly for detecting and analyzing biomolecules to realize the validation and expansion of information space in SM-SERS.

Ultrasensitive detection of remote acoustic vibrations at 300 m distance by optical feedback enhancement

Sensitive detection of remote vibrations at nanometer scale owns promising potential applications such as geological exploration, architecture, and public security. Nevertheless, how to detect remote vibration information with high sensitivity and anti-disturbance has become a major challenge. Reported current non-contact measurement methods are difficult to simultaneously possess characteristics of high light intensity sensitivity, long working distance, high vibration response sensitivity, and anti-disturbance of ambient light. Here, we propose a polarization-modulated laser frequency-shifted feedback interferometry method with the above characteristics, to obtain remote vibration information. The method can directly measure non-cooperative targets without the need for any cooperative markers. In each interference cycle, the energy as low as 2.3 photons can be effectively responded to, and the vibration amplitude sensitivity at 300 m can reach 0.72 nm/Hz1/2 at 1 kHz. This approach provides a strategy for the ultrasensitive detection of remote vibration that is immune to electromagnetic interference.

Polarized and Evanescent Guided Wave Surface-Enhanced Raman Spectroscopy of Ligand Interactions on a Plasmonic Nanoparticle Optical Chemical Bench.

This study examined applications of polarized evanescent guided wave surface-enhanced Raman spectroscopy to determine the binding and orientation of small molecules and ligand-modified nanoparticles, and the relevance of this technique to lab-on-a-chip, surface plasmon polariton and other types of field enhancement techniques relevant to Raman biosensing. A simplified tutorial on guided-wave Raman spectroscopy is provided that introduces the notion of plasmonic nanoparticle field enhancements to magnify the otherwise weak TE- and TM-polarized evanescent fields for Raman scattering on a simple plasmonic nanoparticle slab waveguide substrate. The waveguide construct is called an optical chemical bench (OCB) to emphasize its adaptability to different kinds of surface chemistries that can be envisaged to prepare optical biosensors. The OCB forms a complete spectroscopy platform when integrated into a custom-built Raman spectrograph. Plasmonic enhancement of the evanescent field is achieved by attaching porous carpets of Au@Ag core shell nanoparticles to the surface of a multi-mode glass waveguide substrate. We calibrated the OCB by establishing the dependence of SER spectra of adsorbed 4-mercaptopyridine and 4-aminobenzoic acid on the TE/TM polarization state of the evanescent field. We contrasted the OCB construct with more elaborate photonic chip devices that also benefit from enhanced evanescent fields, but without the use of plasmonics. We assemble hierarchies of matter to show that the OCB can resolve the binding of Fe2+ ions from water at the nanoscale interface of the OCB by following the changes in the SER spectra of 4MPy as it coordinates the cation. A brief introduction to magnetoplasmonics sets the stage for a study that resolves the 4ABA ligand interface between guest magnetite nanoparticles adsorbed onto host plasmonic Au@Ag nanoparticles bound to the OCB. In some cases, the evanescent wave TM polarization was strongly attenuated, most likely due to damping by inertial charge carriers that favor optical loss for this polarization state in the presence of dense assemblies of plasmonic nanoparticles. The OCB offers an approach that provides vibrational and orientational information for (bio)sensing at interfaces that may supplement the information content of evanescent wave methods that rely on perturbations in the refractive index in the region of the evanescent wave.

Statistical blade tip timing measurement, Part II: High-order cumulant architecture

Blade tip timing (BTT) is a potential non-contact vibration measurement technique for rotating blades owing to its high efficiency and long service life. However, probe layout design and vibration parameter identification are critical challenges in BTT due to its inherent undersampling. To address this issue, we have proposed the concept of statistical BTT measurement which promotes efficient layouts and parameter identification methods by shifting the recovery target from the signal to itself to its statistics. In the first paper of series papers, we have developed covariance architecture for BTT measurement and signal post-processing. In this second part of series papers, we set out to develop BTT measurement from a higher-order statistic perspective. Specifically, we find that the 2qth-order cumulant retains the vibration information (frequency and amplitude) of the signal itself and it can be recovered at lower sampling rates than what is prescribed by the covariance architecture. On this basis, we propose a higher-order cumulant architecture for BTT measurement, which contains two aspects: higher-order difference layout (HODL) and higher-order cumulant-based parameter identifications. HODL is a special family of layouts that physically guarantee the availability of consecutive higher-order cumulant estimations and thus leads to a satisfactory parameter identification. To facilitate application, a series of concrete HODLs are provided for users, including a four-level nested layout and its enhanced version, and optimal fourth-order difference layout. The quantitative comparison in the simulations and experiments shows the effectiveness of the proposed higher-order cumulant architecture. Compared with the architectures developed from covariance and signal itself perspective, the proposed higher-order cumulant architecture is not only more robust to noise but also further reduces the number of required probes.

Surface Enhanced Nonlinear Raman Processes for Advanced Vibrational Probing.

Surface enhanced Raman scattering (SERS) is not restricted to the well-known one-photon excited spontaneous Raman process that gives information on molecular composition, structure, and interaction through vibrational probing with high sensitivity. The enhancement mainly originates in high local fields, specifically those provided by localized surface plasmon resonances of metal nanostructures. High local fields can particularly support nonlinear Raman scattering, as it depends on the fields to higher powers. By revealing plasmon-molecule interactions, nonlinear Raman processes provide a very sensitive access to the properties of metal nanomaterials and their interfaces with molecules and other materials. This Perspective discusses plasmon-enhanced spontaneous and coherent nonlinear Raman scattering with the aim of identifying advantages that lead to an advanced vibrational characterization of such systems. The discussion will highlight the aspects of vibrational information that can be gained based on specific advantages of different incoherent and coherent Raman scattering and their surface enhancement. While the incoherent process of surface enhanced hyper Raman scattering (SEHRS) gives highly selective and spectral information complementary to SERS, the incoherent process of surface enhanced pumped anti-Stokes Raman scattering (SEPARS) can help to infer effective nonresonant SERS cross sections and allows to see "hot" vibrational transitions. Surface enhanced coherent anti-Stokes Raman scattering (SECARS) and surface enhanced stimulated Raman scattering (SESRS) combine the advantages of high local fields and coherence, which gives rise to high detection sensitivity and offers possibilities to explore molecule-plasmon interactions for a comprehensive characterization of composite and hybrid structures in materials research, catalysis, and nanobiophotonics.

G band enhancement in ABt-twisted trilayer graphene

G band, originating from the in-plane vibrations of carbon atoms, is the main signature in Raman spectroscopy of graphene-based systems. It is often used to characterize the sample quality and obtain molecular vibration information. Here we investigate the Raman spectroscopy of ABt-twisted trilayer graphene (ABt-TTG) and observe two enhancement centers for the G band across samples with different twist angles. To understand the origin of these two enhancement centers, we theoretically calculate the G band intensity of ABt-TTG based on the continuum model. We find that the theoretical calculations exhibit two prominent peaks corresponding to the experimental observations after Fermi velocity corrections. We also investigated the real and imaginary parts of Raman resonances, respectively, and explained the origins of two enhancements of ABt-TTG. By using Raman spectroscopy, evolutions of band structures of ABt-TTG with respect to the twist angles can be characterized, which extends the potential applications of the Raman method in the investigation of electronic structures of graphene-based systems.

Unsupervised and interpretable discrimination of lithium-bearing minerals with Raman spectroscopy imaging

As lithium-bearing minerals become critical raw materials for the field of energy storage and advanced technologies, the development of tools to accurately identify and differentiate these minerals is becoming essential for efficient resource exploration, mining, and processing. Conventional methods for identifying ore minerals often depend on the subjective observation skills of experts, which can lead to errors, or on expensive and time-consuming techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Optical Emission Spectroscopy (ICP-OES). More recently, Raman Spectroscopy (RS) has emerged as a powerful tool for characterizing and identifying minerals due to its ability to provide detailed molecular information. This technique excels in scenarios where minerals have similar elemental content, such as petalite and spodumene, by offering distinct vibrational information that allows for clear differentiation between such minerals. Considering this case study and its particular relevance to the lithium-mining industry, this manuscript reports the development of an unsupervised methodology for lithium-mineral identification based on Raman Imaging. The deployed machine-learning solution provides accurate and interpretable results using the specific bands expected for each mineral. Furthermore, its robustness is tested with additional blind samples, providing insights into the unique spectral signatures and analytical features that enable reliable mineral identification.

Phase unwrapping error identification and suppression method in φ-OTDR systems based on PELT-VMD-ARIMA

In phase-sensitive optical time-domain reflectometer (φ-OTDR) systems, phase unwrapping errors can distort vibration information. To address this issue, a phase unwrapping error identification and suppression method combining pruned exact linear time (PELT) changepoint detection, variational mode decomposition (VMD), and autoregressive integrated moving average (ARIMA) models, termed PELT-VMD-ARIMA, is proposed. Firstly, the principle of the proposed method is introduced, and its effectiveness is verified through a series of numerical simulation experiments. Next, piezoelectric transducers (PZTs) are employed as seismic sources in experiments involving single-frequency and chirp signals. Compared to the mean-shift method, the proposed method reduces the average root mean square error (RMSE) by 70.36% within 2δ range around the changepoints. Finally, the proposed method was validated through an active source seismic application. The results demonstrate the efficacy of the proposed method in identifying and suppressing phase unwrapping errors, thereby enhancing signal quality. This method enhances the vibration recognition capability of φ-OTDR systems, which facilitates precise distributed acoustic sensing applications.

SERS microscopy as a tool for comprehensive biochemical characterization in complex samples.

Surface enhanced Raman scattering (SERS) spectra of biomaterials such as cells or tissues can be used to obtain biochemical information from nanoscopic volumes in these heterogeneous samples. This tutorial review discusses the factors that determine the outcome of a SERS experiment in complex bioorganic samples. They are related to the SERS process itself, the possibility to selectively probe certain regions or constituents of a sample, and the retrieval of the vibrational information in order to identify molecules and their interaction. After introducing basic aspects of SERS experiments in the context of biocompatible environments, spectroscopy in typical microscopic settings is exemplified, including the possibilities to combine SERS with other linear and non-linear microscopic tools, and to exploit approaches that improve lateral and temporal resolution. In particular the great variation of data in a SERS experiment calls for robust data analysis tools. Approaches will be introduced that have been originally developed in the field of bioinformatics for the application to omics data and that show specific potential in the analysis of SERS data. They include the use of simulated data and machine learning tools that can yield chemical information beyond achieving spectral classification.