Search Results (1,083)

It is found that the speckle structure of the photoinduced light scattering indicatrix of the LiNbO₃:Y³⁺(0.46 wt%) crystal and its behavior with the time of crystal irradiation with a laser undergo an atypical behavior caused by the features of the dissipation processes of laser-induced defects in the crystal. In the frequency range of 100–4000 cm⁻¹, the Raman spectra of the LiNbO₃:Y³⁺(0.46 wt%) single crystal were recorded upon excitation by visible (532 nm) and near-IR (785 nm) laser radiation. Five second-order Raman scattering lines were detected in the frequency range of 1000–2100 cm⁻¹, with the frequencies of two of them (of about 1790 and 1940 cm⁻¹) somewhat exceeding the doubled value of the frequencies of fundamental vibrations of the 4A₁(z)LO (longitudinal optical) and 9E(x,y) symmetry types, which allows us to attribute these lines to the overtones of the fundamental vibrations of 4A₁(z)LO and 9E(x,y). It is found that only one Raman scattering line is observed in the region of stretching vibrations of OH-groups (3200–3800 cm⁻¹). The frequency of this line is found to depend on the scattering geometry, varied within 3431–3438 cm⁻¹, and to be shifted to the low-frequency region by about 30–50 cm⁻¹ relative to the frequencies in the IR absorption spectrum. This finding may be due to the alternative prohibition rule due to the presence of the center of symmetry of the oxygen octahedra O₆ of the crystal structure. Full article

At present, most of the research methods for vibration fatigue of welded structures mainly focus on uniaxial stress, ignoring the influence of shear stress. To this end, by combining the ASME structural stress method with the random and vibration analysis theory outlined in the IEC 61373 standard, a new method for evaluating the fatigue life of multi-axis random vibration problems in the frequency domain has been proposed. This method extends the structural stress method to multi-axis scenarios to accurately extract the local multi-axis structural stress state at the weld toe. Its advantage lies in the fact that it not only accounts for the influence of load frequency distribution and structural modal vibrations on fatigue life, but also incorporates the effect of local multiaxial stress conditions in the weld on fatigue life. Additionally, it includes corrections for non-proportional multiaxial stress conditions, resulting in fatigue assessment results that more closely reflect actual conditions. It was validated by comparing the local multiaxial stress, phase difference between shear and normal stress, and equivalent structural stress power spectrum of 0° and 30° fillet welded specimens with test results. Subsequently, it was applied to a multiaxial random vibration fatigue assessment of a vehicle-mounted electrical cabinet with experimental verification. The results indicate that fatigue life estimates based on a multi-axis stress state are lower than those obtained using a uniaxial method. Compared to traditional uniaxial methods, the multi-axis fatigue life estimates show a significant reduction ranging from 4.20% to 88.35%, effectively accounting for damage caused by shear stress. The fatigue assessment results are more closely aligned with experimental data, thereby validating the effectiveness of the proposed new method. The frequency-domain multiaxial random vibration fatigue assessment method proposed in this article provides a new technology for the design and evaluation of welded structures of vehicle-mounted equipment in rail vehicles. This method reduces costs during the design phase of rail vehicles, offering positive economic implications. Full article

Metallic Shear Plate Dampers (MSPDs) are essential components in passive vibration control systems and require rapid post-earthquake inspection to assess damage and determine replacement needs. Traditional visual inspection methods suffer from low efficiency and limited ability to detect concealed damage. This study proposes a novel MSPD damage detection method based on active sensing and the k-nearest neighbor (KNN) algorithm, featuring high accuracy, efficiency, and low cost. Quasi-static tests were conducted to simulate various damage states. Sweep-frequency excitation was applied using a charge amplifier, and piezoelectric sensors were employed to generate and receive stress wave signals corresponding to different damage conditions. The acquired signals were processed using wavelet packet transform (WPT) and energy spectrum analysis to extract discriminative time–frequency features, which were used to train and validate the KNN model. Results show that the model achieved a validation accuracy of 98.9% using all valid data and 98.1% using a single excitation-sensing channel. When tested on an MSPD with a similar overall structure but lacking stiffeners, the model achieved an accuracy of 92.6% in distinguishing between healthy and damaged states. This indicates that the proposed method has good robustness and practical potential for MSPDs with similar damage evolution and failure modes despite certain structural variations. Full article

Early fault vibration signals from rolling bearings are typically nonlinear, non-stationary, and heavily obscured by background noise, which severely impedes the accurate extraction of fault features. To overcome the limitations of traditional stochastic resonance (SR)—specifically the small-parameter restriction for high-frequency signals and the subjectivity in parameter selection—this paper proposes an adaptive SR envelope spectroscopy method based on particle swarm optimization (PSO) and local mean decomposition (LMD). First, a variable-scale transformation is introduced to compress the high-frequency fault signals into the effective frequency band required by the adiabatic approximation theory. Second, utilizing the global search capability of PSO, the potential well parameters of the bistable system are adaptively optimized by maximizing the output signal-to-noise ratio (SNR), thereby achieving optimal matching between the nonlinear system and the input signal. Finally, the enhanced signal is decomposed by LMD, and the sensitive components are selected for envelope spectrum analysis to identify fault characteristics. Experimental validation using the Case Western Reserve University bearing dataset demonstrates that the proposed method effectively amplifies weak fault signals under strong noise conditions, exhibiting superior feature extraction accuracy and noise robustness compared to traditional methods. Full article

This paper proposes a novel integrated spectral kurtosis (ISK) technology, which is a new conceptualization for fault diagnosis, and compares it with conventional spectral kurtosis technology. The vibration signals from a gearbox are processed by time synchronous averaging (TSA) and analysed using the spectral kurtosis (SK). The ISK feature is estimated across the entire frequency domain, while the envelope is obtained through SK-based filtering and a Hilbert demodulation. The ISK technology demonstrates the ability to distinguish between healthy and defected gearbox cases, achieving a total probability of correct diagnosis (TPCD) of 91.5% for pinions and 96.1% for gears, whereas the SK-based squared envelope technology provides a limited diagnosis effectiveness, with a maximum TPCD of 80%. The motor current signals are also analysed through harmonic amplitude tracking within the current spectrum. A comparison of the ISK and motor current technologies is also made, showing that the motor current technology reaches a maximum of 90% TPCD for gears, which remains lower than the TPCD for the ISK technology. Full article

In industrial robots, harmonic drive flexible bearings are prone to faults, and fault diagnosis is essential for preventing unexpected downtime. However, vibration signals acquired from robot joints are often non-stationary and contaminated by strong multi-source interference, including motion-induced interference and vibrations induced by the deformation of flexible components. Such interference severely masks the subtle signatures of faults. To address this issue, this paper proposes a fault diagnosis framework that leverages multi-channel vibration signals to enhance fault-related features. First, angular resampling is applied to eliminate speed-induced non-stationarity. Second, envelope extraction is utilized to obtain demodulated signals suitable for independent component analysis (ICA). Subsequently, ICA is employed to extract fault-related components from the multi-channel signals. Finally, the fault-related independent component is identified and analyzed via envelope order spectrum analysis. Experimental validation on an industrial robot under both single-joint and multi-joint operating conditions demonstrates the effectiveness of the proposed framework. The method suppresses multi-source interference and achieves accurate fault diagnosis for flexible bearings under complex operating conditions, with quantitative validation confirming the diagnostic performance of the proposed framework. Full article

In this study, we consider a potential expressed as a hyperbolic-sine function aiming to achieve the energy eigenvalues in a closed form, that is, as an analytical expression. Based on this, the Schrödinger equation is constructed within the framework of non-relativistic quantum mechanics and is tackled by using the Asymptotic Iteration Method. The potential in question was previously addressed in the literature. As an alternative, we obtain the complete energy spectrum in a closed form for the single-well regime of the potential function, by way of the quasi-exact solvability where the system has analytical energy eigenvalues once a certain condition is met, or a relation between the potential parameters is satisfied. This is provided by the applicability of the Asymptotic Iteration Method to both quasi-exact and numerical solutions. Thus, the effects of the potential parameters on the energy spectrum can be seen separately. We conclude that the accuracy of the obtained closed-form energy spectrum is quite high as evidenced by the strong agreement with the numerically obtained ones. Furthermore, it is seen that this consistency improves as the energy level increases. The obtained analytical expression can also be used as a simple analytical model for vibrational spectrum of molecular systems described by anharmonic single-well potentials. Full article

Incipient fault signatures in rolling bearings are often compromised by intense background noise and stochastic impulses. Conventional resonance demodulation frequently relies on rigid frequency partitioning, which tends to disrupt the physical continuity of resonance bands and results in the incomplete capture of essential diagnostic information. Furthermore, the robustness of prevailing optimal demodulation frequency band (ODFB) selection indicators remains limited under heavy noise interference. This study develops the WLERgram framework, which utilizes regularized Fourier series to capture the global morphology of the vibration spectrum. By anchoring filter boundaries at natural energy troughs, the method mitigates spectral truncation based on inherent signal characteristics. The framework integrates an Adaptive Morphological Consensus (AMC) strategy, employing multi-scale operators to extract rotation-correlated components and enhance resistance to incoherent interference. By incorporating a Weighted Logarithmic Energy Ratio (WLER) metric, the method utilizes a nonlinear operator to implement differential mapping between coherent fault harmonics and stochastic noise, enabling autonomous optimization of the demodulation band. Validations using synthetic simulations and experimental benchmarks (CWRU and UORED) suggest that WLERgram offers reliable feature extraction performance and diagnostic robustness under harsh noise environments. Full article

Rotor-flexible casing rubbing can induce strong nonlinear dynamics in rotor systems. This study investigates the harmonic frequency characteristics of a rubbing rotor system with a flexible casing constraint. A nonlinear rub-impact model combined with a finite element-based rotor–casing coupling framework is developed to evaluate system responses under concentric and eccentric configurations. The harmonic components of rotor and casing vibrations are analyzed over a range of rotational speeds. Results show that, under concentric conditions, harmonic frequencies originate from rubbing-induced asynchronous motion. The harmonic sub-frequencies observed in the spectrum correspond to lobed rotor orbits formed during the transition from synchronous to asynchronous motion under continuous rubbing forces. Under eccentric rotor–casing alignment, the vibration spectrum becomes more complex and exhibits frequency clustering. The results provide insight into harmonic generation mechanisms and highlight the role of casing flexibility in rubbing-induced asynchronous motion. Full article

The utilization and chemical transformation of carbon dioxide remains a pressing problem in modern chemistry. Numerous experimental and theoretical studies have focused on the interaction of CO₂ with amines. In this work, quantum chemical density functional theory (DFT) calculations of equilibrium geometries, energies, electronic and vibrational characteristics of CO₂-sensitive mono-, di-, tris-hydroxyamidines and their associates were carried out by the B3LYP/6-31G(d, p) method. The harmonic vibrational frequencies were scaled and compared with the experimental FTIR spectra for supporting wavenumber assignments. Natural bond orbital (NBO) analysis of the atomic charges and charge delocalization was employed to investigate the nature of hydrogen bonding in hydroxyamidine associates. We also used the intrinsically polarizable continuum model (IEFPCM), and the DFT-D3 method was applied to account for dispersion effects during associate formation. Using the 6-311+G(2d, p) basis set for tris-hydroxyamidine, and its adducts, a comparative analysis of the experimental and calculated ¹H NMR spectra was performed. Here, we considered non-trivial sites of carbon dioxide absorption and hydroxyamidine protonation, which, to our knowledge, have hardly been considered by other authors. Present DFT results agree rather well with the experimental data and support new insight into the formation of the PIL structure. Full article

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article

Measurement results of train-induced vibrations are evaluated for characteristic frequencies, amplitudes and spectra, leading to a prediction which is based on transfer functions of the vehicle–track–soil system, the soil, and the building–soil system. The characteristic frequencies of train-induced vibrations are discussed following the propagation of vibrations from the source to the receiver: out-of-roundness frequencies of the wheels, the sleeper passage frequency, the vehicle–track eigenfrequency, the car-length frequency and multiples, axle-distance frequencies, bridge eigenfrequencies, the building–soil eigenfrequency, and floor eigenfrequencies. Amplitudes and spectra are compared for different train and track types, for different train speeds, and for different soft and stiff soils, where high frequencies are typically found for stiff soil and low frequencies for soft soil. The ground vibration is between the cut-on frequency due to the layering and the cut-off frequency due to the material damping of the soil, but the dominant frequency range also changes with distance from the track. The frequency band of the axle impulses due to the passing static loads obtains a signature from the axle sequence. The high amplitudes between the zeros of the axle-sequence spectrum are measured at the track, the bridge, and also in the ground vibrations, which are even dominant in the far field. A prediction software is presented, which includes all three parts: the excitation by the vehicle–track interaction, the wave transmission through the soil, and the transfer into a building. Full article

Under the combined effects of vibrations from train operations and wind loads, the dynamic response monitoring data of masonry partition walls in subway stations are often contaminated with high-frequency noise, which hinders the accurate identification of the structure’s true dynamic characteristics. To tackle this problem, this paper proposes employing a Butterworth low-pass filter to process the on-site monitoring data. The paper initially elaborates on the monitoring theory grounded in the pulsation method, followed by a detailed explanation of the rationale for selecting the Butterworth filter, as well as data processing techniques such as Fast Fourier Transform (FFT) and self-power spectrum analysis. By incorporating a field monitoring case from a subway station in Guangzhou, the paper compares and analyzes the acceleration time-history curves before and after filtering. Additionally, finite element analysis is performed to assess the mechanical response of the masonry wall under wind loads, train-induced vibrations, and their combined effects. The results demonstrate that after applying a 4th-order Butterworth low-pass filter with a 46 Hz cutoff frequency, the high-frequency noise in the data is effectively suppressed, thereby accentuating the main trend and low-frequency vibration characteristics of the signal. This provides a reliable data foundation for subsequent precise analysis of the dynamic response and fatigue performance of the masonry walls. Full article

Cracks in planetary gearbox casings generate additional vibration responses, which may be used for monitoring structural degradations. This paper provides a signal processing framework to effectively track casing crack-related features in planetary gearboxes using the carrier synchronous signal average (C-SSA). The proposed algorithm is based on processing the hunting-tooth synchronous signal average (H-SSA) to extract the C-SSA which contains the cyclic interaction between the gear loadings and the corresponding casing response. The root mean square (RMS) of the C-SSA signal can then serve as a health condition indicator (CI) to track crack propagation. Further enhancement can be achieved by applying the Hilbert transform (HT) on the C-SSA using the full bandwidth to derive squared envelope signal, which enhances the trending capability. To remove cyclic temperature influences observed in the trends, singular spectrum analysis technique (SSAT) has been used to ensure that the trend reflects the changes purely due to the damage progression. Experiments using three casing-mounted sensors show good capability to track crack progression. Tests under 100%, 125%, and 150% load levels show consistent performance across these operating conditions, with better results seen at higher loads. The results demonstrate that C-SSA and its squared envelope signal effectively enhance the sensitivity and reliability of vibration-based casing crack detection, providing a practical tool for long-term structural health monitoring of planetary gearboxes. Full article

In the optical fiber Fabry–Perot (FP) multi-dimensional acceleration sensing system, multi-dimensional acceleration measurement is realized based on a single optical path, resulting in the existence of multi-channel interference signals in the spectrum, and the traditional cavity length demodulation algorithm cannot achieve efficient separation of aliasing signals and high-precision demodulation of FP cavity length. To solve this problem, an adaptive filtering–multiple peaks–cooperative least squares algorithm (AF-MP-LS) is proposed for cavity length demodulation of optical fiber FP multi-dimensional accelerometer. The adaptive Gaussian filter is used to dynamically adjust the parameters according to the frequency difference in the aliasing optical signal, and the interference spectra of each channel are efficiently separated. The multiple peaks–least squares method is used to demodulate the separated signals, improve the demodulation resolution, and solve the problem of limited dynamic range of spectral signals. Furthermore, based on the multiplexing structure, a complementary correction method utilizing ‘triple-interferometric’ information—derived from the FP cavities and the auxiliary Michelson interference component—is proposed to improve the demodulation accuracy and stability of the system. The performance of the proposed method was verified through simulations, multi-angle vibration experiments and comparative algorithm analysis. The experimental results show that this algorithm can accurately demodulate multi-dimensional signals under different tilt angles of vibration excitation. Particularly, after compensating for the triple interference information, the mean square error (MSE) of the demodulated acceleration decreased by 0.0044 g, and the accuracy increased by 70.9% compared to before correction. Full article