Vibration Information Research Articles

An adaptive and space-energy efficiency vibration absorber system using a self-sensing and tunable magnetorheological elastomer

Vibration monitoring and buffering is an important technical task in many industrial fields, such as bridges, machine tools and other specific applications. Recently, a magneto-rheological elastomer (MRE) has been utilized in an intelligent vibration absorber system with tunable stiffness, which can reversibly regulate its resonant frequency in real time to effectively prevent the resonance of the mechanical system. However, this MRE must be integrated with a vibrational sensor and equipped with an external magnetic field (e.g., electromagnetic coils or permanent magnets). Thus, the overall system is usually bulky and energy-intensive, which severely limits its broad application potential. In this work, we present an MRE-based tunable vibration absorber (MRE-TVA) with an elaborate 3-layered architecture integrated with a self-sensing triboelectric nanogenerator (TENG) sensor. The inner two-layer MRE structure exhibits variable stiffness, while the outer layer of the negative Poisson's ratio structure can confine the deformation of the inner MRE layers and form a contact-separation TENG sensor with the MRE, where the sensor can recognize external vibration information in a timely manner. In particular, the TENG sensor enables sensitive identification of the abnormal state of resonant excitation and other mechanical disturbances through dual logic judgment control based on vibration frequency and amplitude. For energy-space efficiency, a bistable permanent magnet controlled by a pulse current is integrated to generate a desirable magnetic field for modulation of the MRE stiffness. Compared with traditional electromagnetic coils, the energy and space utilization rates are increased by 94.25% and 75%, respectively. This combination of self-sensing and vibration damping systems achieves high energy-space efficiency, which will enable the intelligent health monitoring of bridges and other autonomous, smart, and long-term engineering tasks.

Multifocal Raman Spectrophotometer for Examining Drug-Induced and Chemical-Induced Cellular Changes in 3D Cell Spheroids.

Cell spheroids offer alternative in vitro cell models to monolayer cultured cells because they express complexities similar to those of in vivo tissues, such as cellular responses to drugs and chemicals. Raman spectroscopy emerged as a powerful analytical tool for detecting chemical changes in living cells because it nondestructively provides vibrational information regarding a target. Although multiple iterations are required in drug screening to determine drugs to treat cell spheroids and assess the inter-spheroid heterogeneity, current Raman applications used in spheroids analysis allow the observation of only a few spheroids owing to the low throughput of Raman spectroscopy. In this study, we developed a multifocal Raman spectrophotometer that enables simultaneous analysis of multiple spheroids in separate wells of a regular 96-well plate. By utilizing 96 focal spots excitation and parallel signal collection, our system can improve the throughput by approximately 2 orders of magnitude compared to a conventional single-focus Raman microscope. The Raman spectra of HeLa cell spheroids treated with anticancer drugs and HepG2 cell spheroids treated with free fatty acids were measured simultaneously, and concentration-dependent cellular responses were observed in both studies. Using the multifocal Raman spectrophotometer, we rapidly observed chemical changes in spheroids, and thus, this system can facilitate the application of Raman spectroscopy in analyzing the cellular responses of spheroids.

A Polarization-Modulated Laser Frequency-Shifted Feedback System Reducing the Parasitic Noise Intensity by About Three Orders of Magnitude

We propose a parasitic noise suppression laser frequency-shifted feedback system based on polarization modulation, which can reduce the parasitic noise intensity by about 30 dB without affecting the signal of interest by applying different polarization modulations to the parasitic noise and the signal to make them converted to system-insensitive and sensitive states, respectively. Therefore, the proposed system can still get correct information which is originally drowned in parasitic noise. Based on the proposed principle, the displacement of the aluminum block 2 m away from the system is successfully recovered. Furthermore, the vibration of an ordinary carton which is 80 m away from the system is demodulated, and the voice signal near the carton is restored according to the vibration information with much better performance than there is no parasitic noise suppression. During the vibration recovery process, the effective response energy as low as 5.5 photons is achieved in each feedback modulation cycle. The successful implementation of the proposed parasitic noise suppression laser frequency-shifted feedback system based on polarization modulation provides a practical anti-interference and sensitive coherent detection method in weak light signals detection.

A novel method for noise reduction of blade tip timing signals based on sparse representation and dictionary learning

Blade Tip Timing Technology (BTT) is a new type of blade vibration measurement method with non-contact measurement capabilities, which is associated with high efficiency and convenience. However, due to under-sampling feature of the BTT signal, the method can only measure part of the vibration information. Therefore, various BTT spectral reconstruction algorithms have been developed based on the under-sampling feature and sparsity of the blade vibration spectrum. Because of the harsh working environment of the blades, BTT signal typically contains high-level Gaussian noise in actual measurement, which significantly reduces frequency-domain sparsity and even aliases the vibration modals, resulting in a decrease in the performance of sparse reconstruction algorithms. In this paper, a multi-sequences BTT signal noise reduction method is proposed, which is based on sparse representation to suppress Gaussian noise. Through appropriately modifying the constraint in the ℓ0 optimization problem, the method filters out the noise elements in the sparse vector of the BTT signal. Then, denoising is completed by sparse inverse transformation. Finally, a global average-based singular value decomposition dictionary learning algorithm (GA-K-SVD) is proposed to generate an over-complete sparse dictionary which is adaptable to the original signal, to increase the effectiveness of GA-K-SVD. At last, simulation and experience are carried out to verify the performance optimization effect for sparse reconstruction algorithm and the effectiveness of proposed noise reduction method.

A fault diagnosis method for nuclear power plant rotating machinery based on adaptive deep feature extraction and multiple support vector machines

Rotating machinery is the essential component in nuclear power plants (NPPs). Effective fault detection and diagnosis is a main challenge in the operation and maintenance of NPPs rotating machinery. The performance of traditional fault diagnosis methods mainly depends on complex manual feature extraction and sufficient expert prior knowledge. This study proposes a fault diagnosis method based on adaptive feature extraction and multiple support vector machines (ResNet-SVMs) to overcome the limitations of the traditional intelligent fault diagnostics. First, the vibration information from different locations of the rotating machinery is collected and used as input data for the algorithm model. Then, the deep residual neural network adaptively extracts fault features from input data to obtain feature data of different depths. Finally, multiple support vector machines identify the feature data to realize the fault diagnosis. The effectiveness verification is carried out based on the experimental cases of induction motor and rolling bearing fault diagnosis. Compared with other advanced intelligent fault diagnosis methods, ResNet-SVMs model provides better diagnostic performance, which demonstrates its potential value for NPPs rotating machinery fault diagnosis.

Long-range distributed vibration sensing using phase-sensitive forward optical transmission.

Long-range vibration sensing is an important tool for real-time structural health monitoring. A new, to the best of our knowledge, design of a distributed fiber-optic vibration sensor is introduced and experimentally demonstrated in this study. The proposed system utilizes the transmission of light in the forward direction for sensing, and a self-interference method for laser source simplification. To extract vibration information from phase modulation of light, two Mach-Zehnder interferometers (MZIs) are employed with a 3 × 3 coupler-based differential cross multiplication algorithm for phase calculation. A folded double-ended detection configuration allows the time-of-flight difference via cross correlation (CC) to provide vibration positioning. Experimental results demonstrate a sensing range of up to ∼80 km without optical amplification, accompanied by a position accuracy of 336 m.

Cryptos Control of Humanoid Intelligent Digital Twin Based on Spectral and Holographic Approaches

A person perceives his environment through the influence of various complexes of conjugated vibrations on the eyes, ears and other sensitive components of the body. The psyche and neural systems of a person form the impression of vibration impact. The mind creates a language equivalent and connects it with the impression that has formed. Communication links are formed between impressions and language equivalents. Live vibrational information involves a person in a communicative creative process. In the creative communicative process, human intelligence develops. The combination of modern interdisciplinary technologies can contribute to the creation of an intelligent digital twin, similar to humans on spectral and holographic principles, relying on achievements in the field of artificial intelligence. The digital twin fixes the effects of the environment in the form of a spectrogram, and stores the effect result in the form of a hologram. Multilayer neural network systems with additional training work with spectra and holograms and their communications. Language communications are associated with spectra, holograms and their communications. A humanoid digital twin with an ensemble of intellectual agents will be able to form and develop intelligence in interaction with similar digital twins and people.

When Phenyl and Cyclopropyl Rings Meet: Spectroscopic Shifts and Conformational Questions.

The electronic and vibrational spectra of cyclopropylbenzene (CPB) and 1,3-bromocyclopropylbenzene (BrCPB) in the gas phase were investigated using quantum chemical calculations in combination with resonance-enhanced multi-photon ionization REMPI techniques including 1c-R2PI, UV-UV holeburning, and IR-UV ion depletion in the CH stretch region. The electronic spectra revealed the presence of a single conformer for both species, with the absence of any perpendicular conformer attributed to low computed barriers to conformer interconversion. Assignment of CPB to the bisected conformer was made through interpreting distinctive CH stretch bands in the IR-UV spectrum in conjunction with quantum chemical calculations. A local anharmonic model based on DFT calculations was adapted to reproduce the cyclopropyl CH stretch spectrum successfully. It was not feasible to definitively assign which bisected conformer of BrCPB was observed using vibrational information alone due to the close similarity of their predicted IR spectra. However, conformational sensitivity of the S1 ← S0 transition dipole moment (TDM) alignments leads to simulated rotational contours that display stark differences, which prompted assignment to the "B1" bisecting conformer with the cyclopropyl ring directed away from the bromine atom. The absence of the energetically comparable "B2" conformer is unexpected. The analysis of the convolution of aromatic and aliphatic modes serves as a basis for assignment in constrained aliphatic systems.

Nanostructured cauliflowers patterning in Zr doped tungsten oxide thin films grown by AACVD

The effort is made to grow the cellular segmentation patterning of undoped and Zr doped Tungsten Oxide (ZWO) coatings engineered by aerosol assisted Chemical Vapor Deposition (AACVD) onto silica glass at 400 °C. Nanostructured cauliflowers were successfully grown without using any particular template via the chemical route of AACVD for undoped and Zr doped Tungsten Oxide. The tailored coatings of nano cauliflowers were explored by employing FE-Scanning electron microscopy/Energy Dispersive X-ray spectroscopy (FE-SEM/EDX), X-ray photoelectron spectroscopy (XPS), X-ray diffractometry (XRD), Fourier transformation Infrared spectroscopy (FTIR), UV-VIS spectroscopy (UV-VIS) and four-point probe method to study surface morphological, chemical composition, optical and electrical properties systematically. Morphological features present fractal-type cauliflower patterning because of Zr incorporation. XRD spectra revealed a monoclinic (WO3) phase in undoped Tungsten Oxide with preferential orientation (0 2 0) whereas Zr doping triggered the growth of Magneli suboxide W5O14 with tetragonal (0 0 1) preferred reflection. Adding Zr dopant caused a slight expansion of lattice with a decrease in crystallite size (54–18) nm. Vibrational information exhibits shoulder at around 854.30 cm−1, attributed to O–W–O bond shifting. Undoped and Zr doped Tungsten oxide thin film signatures positioned at 446.65 cm-1, 692 cm-1, and 854.30 cm-1 are witnessed with a red and blue shift in all rest of the Zr doped thin films. The mismatch of W and Zr ions in ionic radii, electronegativity, and thermal coefficients promote stresses leading to the variation in lattice parameters, bond length, and crystallite size with stretching of IR modes attributed to the incorporation of Zr into the host lattice sites of Tungsten oxide. XPS deconvoluted peaks yield the analysis for bond energy of O1s electrons located around 505.19 eV and 510.85 eV characterized as metallic oxide of Tungsten (WO3). UV-VIS spectroscopy presented a highly absorptive nature of coatings in the spectral range between (300–900) nm with the widening of both direct (1.9–3.2) eV and indirect (1.9–2.6) eV band gap which is of absolute interest for both fundamental and applied aspects. Transport properties unveiled the decrease of electrical resistivity of order from 106 Ω-cm (undoped) to 105 Ω-cm (Zr doped), attributed to grown high angle nanoparticles boundaries of tungsten oxide contribute to governing of nano cauliflowers and, as a result, the volume of the grain boundaries increases. The interplay between crystalline quality, tuned architecture, and oxygen vacancies can contribute to the understanding of morphological, optical, and transport properties.

Self-Powered Wireless Temperature and Vibration Monitoring System by Weak Vibrational Energy for Industrial Internet of Things.

Developing self-powered smart wireless sensor networks by harvesting industrial environmental weak vibration energy remains a challenge and an impending need for enabling the widespread rollout of the industrial internet of things (IIoT). This work reports a self-powered wireless temperature and vibration monitoring system (WTVMS) based on a vibrational triboelectric nanogenerator (V-TENG) and a piezoelectric nanogenerator (PENG) for weak vibration energy collection and information sensing. Therein, the V-TENG can scavenge weak vibration energy down to 80 μm to power the system through a power management module, while the PENG is able to supply the frequency signal to the system by a comparison circuit. In an industrial vibration environment where the vibration frequency and amplitude are 20 Hz and 100 μm, respectively, the WTVMS can upload temperature and frequency information on the equipment to the cloud in combination with the narrowband IoT technology to realize real-time information monitoring. Furthermore, the WTVMS can work continuously for more than 2 months, during which the V-TENG can operate up to 100 million cycles, achieving ultrahigh stability and durability. By integrating weak vibration energy harvesting and active sensing technology, the WTVMS can be used for real-time online monitoring and early fault diagnosis of vibration equipment, which has great application prospects in industrial production, machinery manufacturing, traffic transportation, and intelligent IIoT.

Recent Advances in Rational Design and Engineering of Signal-Amplifying Substrates for Surface-Enhanced Raman Scattering-Based Bioassays

In recent decades, surface-enhanced Raman spectroscopy (SERS) has become a powerful detection scheme for many applications, particularly bioassays, due to its unique strengths, such as its ultrasensitive performance. Due to the development of various SERS substrates, more SERS-based bioassays with improved sensitivity and reproducibility have been designed and manufactured. SERS is able to provide the intrinsic vibration information of molecules through the unique Raman fingerprint to enable direct detection and quantitation. Meanwhile, with the assistance of Raman-active labels, biomolecules, like proteins and nucleic acids, can be detected by the immunosandwich assay. In this review, we focus on the rational design and engineering of signal-enhancing substrates for SERS-based bioassays. Those substrates are classified into two categories, i.e., nanoparticles in colloidal suspension and nanostructures on a solid support. Each category is discussed in detail with stress on their biomedical application potential. Afterward, we summarize the SERS-based assays of proteins, nucleic acids, and viruses, for which both label-free and labeled approaches play important roles. Finally, we present the remaining challenges in the field of SERS-based bioassays and sketch out promising directions for future development.

Vibration and Image Texture Data Fusion-Based Terrain Classification Using WKNN for Tracked Robots

For terrain recognition needs during vehicle driving, this paper carries out terrain classification research based on vibration and image information. Twenty time-domain features and eight frequency-domain features of vibration signals that are highly correlated with terrain are selected, and principal component analysis (PCA) is used to reduce the dimensionality of the time-domain and frequency-domain features and retain the main information. Meanwhile, the texture features of the terrain images are extracted using the gray-level co-occurrence matrix (GLCM) technique, and the feature information of the vibration and images are fused in the feature layer. Then, the improved weighted K-nearest neighbor (WKNN) algorithm is used to achieve the terrain classification during the travel process of tracked robots. Finally, the experimental results verify that the proposed method improves the terrain classification accuracy of the tracked robot and provides a basis for improving the stable autonomous driving of tracked vehicles.

Overview of System Identification Tool Advancements at Systems Technology, Incorporated

For nearly two decades, Systems Technology, Inc. has been investigating novel system identification methods and practical applications. Two such methods will be highlighted herein: 1) the use of wavelet-based scalograms to explore the nature of dynamic systems in both the time and frequency domains simultaneously; and 2) a method to estimate frequency, damping, and mode shapes for lightly damped aeroelastic modes exhibited across a variety of air vehicles. From past work, two scalogram-based metrics have evolved, which are the power frequency and inceptor peak power phase, that will be one focus of this paper. The power frequency provides a metric that considers not only the dominant frequencies of the signal of interest, such as the command input from a pilot, but also the power at those frequencies. For the inceptor peak power phase, scalograms are used to identify the peak input power of a pilot’s inceptor command signal that when compared against a pitch or roll rate phase angle at the peak frequency produces a new method to detect the onset of pilot-induced oscillations. This paper will further describe system identification tools that extract modal information of aeroelastic vibrations observed in flight. Because it is often difficult to obtain input excitation data, particularly in real time, Systems Technology, Inc. developed a modal identification software toolbox that provides an innovative approach based on curve-fitting frequency domain decomposition, which relies only on output sensor data to identify the aeroelastic modes. Flight-test data are used to demonstrate the effectiveness and efficacy of the system identification approaches.

Non-Contact Global Measurement of the Engine in Working Condition

Vibration measurements are essential for studying the dynamic properties of structures, as they can better capture the global characteristics of the structure. This paper proposes a three-dimensional measurement method based on projected structured light to address the challenge of measuring the vibration information of engine pipes during operation. By using the data obtained from this method and applying the time domain analysis method for modal analysis, the modal parameters of the engine under operating conditions can be obtained. The feasibility of this method is demonstrated by comparing it with different measurement methods, and it provides an effective solution for modal identification of engines under operating conditions.

Periodic-Filtering Method for Low-SNR Vibration Radar Signal

Radar is a non-contact, high-precision vibration measurement device and an important tool for bridge vibration monitoring. Vibration information needs to be extracted from the radar phase, but the radar phase information is sensitive to noise. Under low signal-to-noise ratio (SNR) data acquisition conditions, such as low radar transmission power or a long observation distance, differential phase jump errors occur and clutter estimation becomes difficult, which leads to inaccurate inversion of vibration deformation. Traditional low-pass filtering methods can filter out noise to improve SNR, but they require oversampling. The sampling rate needs to be several times higher than the Doppler bandwidth, which is several times higher than the vibration frequency. This puts high data acquisition requirements on radar systems and causes large data volumes. Therefore, this paper proposes a novel vibration signal filtering method called the periodic filtering method. The method uses the periodicity feature of vibration signals for filtering without oversampling. This paper derives the time-domain and frequency-domain expressions for the periodic filter and presents a deformation inversion process based on them. The process involves extracting the vibration frequency in the Doppler domain, suppressing noise through periodic filtering, estimating clutter using circle fitting on the data complex plane, and inverting final deformation with differential phase. The method is verified through simulation experiments, calibration experiments, and bridge vibration experiments. The results show that the new periodic filtering method can improve the SNR by five times, resolve differential phase jumps, and accurately estimate clutter, thus getting submillimeter-level vibration deformation at low SNR.

Heartbeat information prediction based on transformer model using millimetre‐wave radar

AbstractMillimetre‐wave radar offers high ranging accuracy and can capture subtle vibration information of the human heart. This study proposes a heartbeat prediction method based on the transformer model using millimetre‐wave radar. Firstly, the millimetre‐wave radar was used to collect the heartbeat data and conduct normalisation processing. Secondly, a position coding was introduced to assign sine or cosine variables to input data and extract their relative position relationship. Subsequently, the transformer encoder was adopted to allocate attention to input data through the multi‐head attention mechanism, using a mask layer before the decoding layer to prevent the leakage of future information. Finally, we employ the fully connected layer was employed in the linear decoder for regression and output the predicted results. Our experimental results demonstrate that the proposed transformer model achieves nearly 30% higher prediction accuracy than traditional long short‐term memory models while improving both the prediction accuracy and convergence rate. The proposed method has great potential in predicting the heartbeat state of elderly and sick patients.

Bond-selective fluorescence imaging with single-molecule sensitivity.

Bioimaging harnessing optical contrasts and chemical specificity is of vital importance in probing complex biology. Vibrational spectroscopy based on mid-infrared (mid-IR) excitation can reveal rich chemical information about molecular distributions. However, its full potential for bioimaging is hindered by the achievable sensitivity. Here, we report bond selective fluorescence-detected infrared-excited (BonFIRE) spectral microscopy. BonFIRE employs two-photon excitation in the mid-IR and near-IR to upconvert vibrational excitations to electronic states for fluorescence detection, thus encoding vibrational information into fluorescence. The system utilizes tuneable narrowband picosecond pulses to ensure high sensitivity, biocompatibility, and robustness for bond-selective biological interrogations over a wide spectrum of reporter molecules. We demonstrate BonFIRE spectral imaging in both fingerprint and cell-silent spectroscopic windows with single-molecule sensitivity for common fluorescent dyes. We then demonstrate BonFIRE imaging on various intracellular targets in fixed and live cells, neurons, and tissues, with promises for further vibrational multiplexing. For dynamic bioanalysis in living systems, we implement a high-frequency modulation scheme and demonstrate time-lapse BonFIRE microscopy of live HeLa cells. We expect BonFIRE to expand the bioimaging toolbox by providing a new level of bond-specific vibrational information and facilitate functional imaging and sensing for biological investigations.

Metamaterial-Based Analog Recurrent Neural Network Toward Machine Intelligence

Wave-based analog computation platforms hold the promise of realizing intelligent information processing with high speed and low computational cost, but current design principles face intractable problems in processing low-frequency wave information end to end at the physical layer. Here we theoretically demonstrate a concept of a metamaterial-based analog recurrent neural network (Meta-RNN) for intelligent classification of physical information carried by mechanical vibrations. With the spatiotemporal recurrence relation induced by coupled local resonators of the simulated metamaterial model, the Meta-RNN is capable of memorizing the internal hidden state in the dynamic displacement field. The trained coupling and synergy of local resonators lead to desirable vibration energy localization with selective frequency extraction capability, which can distinguish the intrinsic characteristics of vibration information. The proposed Meta-RNN provides a foundation for implementing a promising mechanical analog processor for intelligent vibration information processing and classification, and paves the way to efficient machine learning platforms for machine intelligence.

Statistical topology optimization scheme for structural damage identification

Statistical topology optimization (TO) scheme, which statistically records and analyzes local optima at random frequencies of the excitation force, is developed for material damage and open crack. To identify the alternations of stiffness, mass, and strength due to the damages, the structural responses of healthy and suspicious systems can be compared and optimized by TO for fault identification. Owing to the local optima problem in TO, slight modifications in responses caused by relatively minimal damage, and inadequate comparison of insufficient vibration information, the application of TO to a damage identification problem is often complicated. To overcome this complication, this study establishes a new approach that records local optima (layouts) and performs a statistical process (clustering and averaging of layouts). To illustrate the concept of the present scheme, several examples with material damage and open crack are solved.

Vaporthermal-driven rapid transformation of commercial gibbsite to rhombic boehmite flakes

The large-scale and controllable production of boehmite has attached much attention due to its extensive application in advanced energy and electronics industries. In this study, a facile vaporthermal (VPH) method was developed for the rapid fabrication of rhombic boehmite microflakes using low-cost commercial gibbsite particles. The evolution behaviors of gibbsite to boehmite under VPH and conventional hydrothermal (LPH) conditions were ex-situ monitored by XRD, Raman, and FT-IR techniques. The boehmite powders from VPH (V-boehmite) and LPH (L-boehmite) have similar crystal structure and bonding vibration information, whereas V-boehmite exhibits rhombic morphology and L-boehmite presents cubic morphology. Moreover, the formation time of boehmite can be shortened from 16 h of LPH to 6 h of VPH at 180 °C, which significantly contributes to its scalable production. These differences of V-boehmite are attributed to the direct contact between high-temperature saturated water steam and gibbsite, which can induce the rapid formation of rhombic boehmite microflakes through dissolution-recrystallization mechanism. Therefore, the vaporthermal technology has great potential for efficient, sustainable, and large-scale production of boehmite powders.