Frequency Characteristics Research Articles

A spike is a spike: On the universality of spike features in four epilepsy models.

Frequency properties of the EEG characteristics of different seizure types including absence seizures have been described for various rodent models of epilepsy. However, little attention has been paid to the frequency properties of individual spike-wave complexes (SWCs), the constituting elements characterizing the different generalized seizure types. Knowledge of their properties is not only important for understanding the mechanisms underlying seizure generation but also for the identification of epileptiform activity in various seizure types. Here, we compared the frequency properties of SWCs in different epilepsy models. A software package was designed and used for the extraction and frequency analysis of SWCs from long-term EEG of four spontaneously seizing, chronic epilepsy models: a post-status epilepticus model of temporal lobe epilepsy, a lateral fluid percussion injury model of post-traumatic epilepsy, and two genetic models of absence epilepsy-GAERS and rats of the WAG/Rij strain. The SWCs within the generalized seizures were separated into fast (three-phasic spike) and slow (mostly containing the wave) components. Eight animals from each model were used (32 recordings, 104 510 SWCs in total). A limitation of our study is that the recordings were hardware-filtered (high-pass), which could affect the frequency composition of the EEG. We found that the three-phasic spike component was similar in all animal models both in time and frequency domains, their amplitude spectra showed a single expressed peak at 18-20 Hz. The slow component showed a much larger variability across the rat models. Despite differences in the morphology of the epileptiform activity in different models, the frequency composition of the spike component of single SWCs is identical and does not depend on the particular epilepsy model. This fact may be used for the development of universal algorithms for seizure detection applicable to different rat models of epilepsy. There is a large variety between people with epilepsy regarding the clinical manifestations and the electroencephalographic (EEG) phenomena accompanying the epileptic seizures. Here, we show that one of the EEG signs of epilepsy, an epileptic spike, is universal, since it has the same shape and frequency characteristics in different animal models of generalized epilepsies, despite differences in recording sites and location.

LapUNet: a novel approach to monocular depth estimation using dynamic laplacian residual U-shape networks

Monocular depth estimation is an important but challenging task. Although the performance has been improved by adopting various encoder-decoder architectures, the estimated depth maps lack structure details and clear edges due to simple repeated upsampling. To solve this problem, this paper presents the novel LapUNet (Laplacian U-shape networks), in which the encoder adopts ResNeXt101, and the decoder is constructed with the novel DLRU (dynamic Laplacian residual U-shape) module. The DLRU module based on the U-shape structure can supplement high-frequency features by fusing dynamic Laplacian residual into the process of upsampling, and the residual is dynamically learnable due to the addition of convolutional operation. Also, the ASPP (atrous spatial pyramid pooling) module is introduced to capture image context at multiple scales though multiple parallel atrous convolutional layers, and the depth map fusion module is used for combining high and low frequency features from depth maps with different spatial resolution. Experiments demonstrate that the proposed model with moderate model size is superior to other previous competitors on the KITTI and NYU Depth V2 datasets. Furthermore, 3D reconstruction and target ranging by utilizing the estimated depth maps prove the effectiveness of our proposed method.

On-demand performance optimization of AlGaN/GaN high electron mobility transistors using stoichiometric variation of dielectric alloy AlxTayO

The current research investigates the potential advantages of optimally combining wide bandgap Al2O3 with high dielectric constant (high-κ) Ta2O5 for gate dielectric applications. Various compositions of 10 nm AlxTayO oxide films are grown on the Al0.3Ga0.7N/GaN heterostructure by co-sputtering Al and Ta metals, followed by thermal oxidation at 500 °C. The average root-mean-square roughness of the grown oxide films is ∼1.2–1.4 nm compared to 0.4 nm for as-deposited metal. X-ray photoelectron spectroscopy and transmission electron microscopyconfirm the formation and thickness of the grown oxide films. The bandgap (Eg) of the oxide films calculated from O1s electron energy loss spectra show a linear increase from 4.85 eV for pure Ta2O5 to 6.4 eV for pure Al2O3. The dielectric constant (εox) calculated from capacitance–voltage (CG−VG) measurements decreases linearly from 25.7 for Ta2O5 to 7.9 for Al2O3. The interface trap density (Dit) is estimated from the frequency dispersion of capacitance–voltage (CG−VG) characteristics. The DC and radio frequency (RF) characteristics of AlxTayO/Al0.3Ga0.7N/GaN high electron mobility transistor (HEMT) devices are measured and compared with the Schottky HEMT devices (without any gate oxide). Compared to Schottky HEMT devices, AlxTayO/Al0.3Ga0.7N/GaN HEMT devices show superior DC characteristics, which helps us achieve maximum RF output power. Furthermore, the OFF-state measurements show that the AlxTayO/Al0.3Ga0.7N/GaN HEMT devices can sustain higher source-to-drain voltages, from a minimum of 88 V on pure Ta2O5 metal-oxide-semiconductor (MOS)-HEMTs to a maximum of 138 V on pure Al2O3 MOS-HEMTs before the dielectric breakdown happens, compared to a 57 V breakdown voltage on Schottky HEMT devices. An oxide variation of AlxTayO, with Al composition ratio of 0.34, shows an exceptional ION/IOFF ratio of 4 × 1011, a gate leakage current of 8 × 10−12 A/mm, a near-ideal subthreshold slope of 63.8 mV/dec, and the unity current gain frequency (fT) of 25.6 GHz.

Intraoperative peritoneal cytology for cervical gastric-type adenocarcinoma: Cytopathology and clinical impact.

The objective of this study was to elucidate the frequency and cytologic features of positive peritoneal washing cytology (PWC) in cervical gastric-type adenocarcinoma (GAS) and to clarify the clinical significance of positive PWC. The authors analyzed cases from their institution between 1991 and 2023 in which patients underwent surgery and PWC. The study included 62 patients who had cervical GAS (1991-2023; including seven patients with adenocarcinoma in situ and 26, 15, nine, and five patients with International Federation of Gynecology and Obstetrics 2018 stage I, II, III, and IV disease, respectively) and 100 patients who had usual-type endocervical adenocarcinoma (2007-2023; including 65, 15, and 20 patients with stage I, II, and III disease, respectively). The frequency of positive PWC results and cytologic features was assessed, and correlations between positive PWC results and clinicopathologic factors were examined, including prognosis, in the GAS group. Positive PWC results were significantly more frequent in patients who had GAS at 24% (15 of 62 patients) compared with 7% (seven of 100 patients) in those who had usual-type endocervical adenocarcinoma. The cytologic features of GAS included distinct cellular atypia (enlarged nuclei, nuclear irregularity) and frequent formation of spherical clusters (10 of 15 cases) without the golden-yellowish mucus commonly seen in cervical smears. A positive PWC result in GAS was significantly correlated with larger tumor size, parametrium invasion, lymph node metastasis, and elevated carbohydrate antigen 19-9 levels. In patients with stage I GAS, the PWC-positive group had significantly shorter disease-free survival and overall survival compared with the PWC-negative group. Positive PWC findings are frequent in cervical GAS and are associated with pathologic factors indicative of tumor growth and progression. In patients who have stage I GAS, positive PWC results may indicate a poor prognosis, warranting further investigation.

Learnable dual attention fusion network for borehole distributed acoustic sensing systems data reconstruction

As the demand for nonrenewable oil and gas resources has increased, distributed acoustic sensing systems (DAS) have been widely used in the acquisition of vertical seismic profiles. Complicated subsurface conditions produce many types of noise with strong energy. The effective reflection and refraction signals are absorbed by the inelastic medium during the transmission process, resulting in significant energy loss and aliased frequency characteristics. However, satisfactory processing results and quality cannot be achieved through conventional denoising methods. In recent years, the rapid development of deep-learning methods with attention mechanisms has attracted significant attention. Noise reduction and signal reconstruction technologies with multiple attention mechanisms within the framework of joint-domain features have become important development directions in DAS data processing. An attention mechanism can help the network ignore useless information and pay attention to useful information in the same way that humans can. In this study, we discuss a novel learnable dual attention fusion network that combines local and patch-based nonlocal attention mechanisms based on ResBlock modules, making the extraction process of the network learnable and selective. In addition, the novel network can focus on local and global details. We also use a residual learning strategy to simplify the training process and gradually establish the best mapping based on the L2 loss function. All experimental results of the simulation and actual records show that our novel network has better denoising and effective signal restoration abilities. Moreover, it can recover the up-going and down-going wavefields and attenuate different types of noise with less effective energy loss.

Analysis on stall mechanism of axial flow compressor with rotating inlet distortion by dynamic mode decomposition

To study the impact of rotating inlet distortion on stall mechanism of an axial compressor, the full-annulus unsteady simulation results are analysed using dynamic mode decomposition (DMD). Firstly, the frequency and energy ratio of each mode at near stall condition for the compressor with uniform inflow are obtained. A dominating frequency band 0.3–0.6 BPF regarded as the characteristic frequency of unsteady tip leakage flow is observed. The distribution of eigenvalues further confirms that there are a few critical unstable modes in the flow field. With further throttling, four large-scale disturbance structures are captured by the second-order mode, whose frequency is twice as the rotor frequency. The circumferential propagation velocity of the stall cell is thus deduced to be about 50% of the rotor speed. Further, when the compressor is subjected to rotating inlet distortion, the change of stalling process is related to the relative position of distortion plate and stall cell according to the DMD mode frequency and shape. The addition of rotating distortion does not change the stall type of the compressor, but the unstable modes indicate that the number and shape of the stall cells have been varied.

Analysis of friction vibration characteristics of the end face of a T-slot sealing ring under thermal deformation

Aiming at the friction vibration problem caused by dry friction contact during the start-stop process of a dry gas sealing device, this paper takes the end face of a T-slot dry gas sealing ring as the research object and establishes a numerical calculation model of the end face of the T-slot sealing ring. The thermal coupling method is used to obtain the thermal deformation law of the seal ring end face. The transient dynamics analysis method was applied to study the frequency and vibration mode of the friction vibration instability of the seal ring end face under thermal deformation. A vibration test bench was set up for the friction ring, and the friction vibration signals of the test pieces were collected, and the characteristic frequencies were extracted. The results show that: After thermal deformation, the end face of the sealing ring changes from a parallel surface to an inward converging surface, and the friction system of the sealing ring will become unstable. When considering the influence of thermal deformation, there are more unstable frequencies and lower frequency values that are most prone to frictional vibration. As the thermal deformation of the sealing ring increases, the frictional vibration becomes greater. In areas where the characteristic frequency values of frictional vibration are concentrated during thermal deformation, the occurrence rate of frictional vibration noise is higher.

Eigenmode and eigenpropagation of the electromagnetic waves in Möbius and Klein networks

To explore the distribution of characteristic frequencies and the propagation properties of eigenmodes in topological networks at the zero-energy level, we design optical waveguide networks with two typical topologies: Möbius network and Klein network, inspired by the Möbius strip and Klein bottle, respectively. We investigate the degeneracy at characteristic frequencies and the propagation properties of the eigenmodes of these networks, both theoretically and experimentally. We discovered an intriguing eigenpropagation in the Möbius network and multiple degenerate eigenmodes in the Klein network, analyzing the propagation characteristics and distribution patterns of electromagnetic waves within them. In our experiments, we utilize coaxial cables as one-dimensional waveguides to construct transmission line networks for the two networks. We observe the distinct transmission paths of the Möbius network’s eigenmode and the two degenerate eigenmodes of the Klein network. Our findings provide a theoretical foundation for new optical modal transmission devices and novel nanoarrays, with potential implications for theoretical and experimental research in other quantum systems and topological networks.

Frequency and Genetic Analysis of Porcine Circovirus Type 2, Which Circulated between 2014 and 2021 in Jiangsu, China

Porcine circovirus-associated diseases, caused by porcine circovirus type 2 (PCV2), are widespread and result in significant economic losses to the global swine industry. PCV2 can currently be divided into nine genotypes (PCV2a to PCV2i), with the currently dominant one being the PCV2d genotype. In this study, 2675 samples from 804 pig farms in 13 cities in Jiangsu Province, China, were collected between 2014 and 2021 and subjected to polymerase chain reaction analysis to investigate the frequency and genetic diversity of PCV2. The results showed that 41.42% (1108/2675) of samples tested positive for PCV2. The researchers further analyzed the genetic characteristics of 251 PCV2 strains and found that they belonged to the following four genotypes: PCV2a, PCV2b, PCV2d, and PCV2i. The dominant genotype was PCV2d, with a frequency of 49.80% (125/251). The detection rate of PCV2b was significantly higher than those of PCV2a and PCV2i, at 35.46% (89/251), 7.57% (19/251), and 7.17% (18/251), respectively. The percentage of different genotypes of PCV2 varied irregularly over time. We have further revealed the fingerprint of PCV2i genomic nucleotides for the first time. In conclusion, this study illustrates the high frequency and evolutionary features of PCV2 in Jiangsu Province over the past few years.

Letting losses be lessons: Human-machine cooperation in maritime transport

Navigation safety has been a critical guarantee of global shipping, and it becomes more challenging given the increasing employment of advanced technologies and novel ship design in the era of Maritime Autonomous Surface Ships (MASS). The human-centred risk analysis of human-machine cooperation is scarce in general and emerging in maritime transport in specific. This paper aims to develop a new approach enabling the analysis of significant risk influencing factors (RIFs) in human-machine cooperation through an in-depth investigation of the occurred mistakes and violations in the cooperative operations of seafarers and machines in maritime transport. Its novelties consist of (1) a novel approach to analysing and quantifying the connectivity between humans and machines in safety-critical operations, (2) new integration of the frequency and impact of RIFs in the human-machine cooperation model, and (3) ultilisation of graph theory to generate a network to analyse critical human-machine RIFs and their interactions with the system. The connectivity analysis of RIFs is conducted through a weighted undirected network, showing the features of RIF connectivity accommodating the closed-loop system. The proposed novel approach, which combines the frequency and impact features to identify critical RIFs and analyses graphical features, will aid to realise the human-centred risk analysis for MASS. The findings make contributions for ship designers to rationalise the clustering design of function-based automation and training organisations to improve seafarer skills by rationally considering the identified risk-based human-machine cooperation features, and providing new competence schemes that can fit the demands of MASS in future.

DAFE-MSGAT: Dual-Attention Feature Extraction and Multi-Scale Graph Attention Network for Polyphonic Piano Transcription

Automatic music transcription (AMT) aims to convert raw audio signals into symbolic music. This is a highly challenging task in the fields of signal processing and artificial intelligence, and it holds significant application value in music information retrieval (MIR). Existing methods based on convolutional neural networks (CNNs) often fall short in capturing the time-frequency characteristics of audio signals and tend to overlook the interdependencies between notes when processing polyphonic piano with multiple simultaneous notes. To address these issues, we propose a dual attention feature extraction and multi-scale graph attention network (DAFE-MSGAT). Specifically, we design a dual attention feature extraction module (DAFE) to enhance the frequency and time-domain features of the audio signal, and we utilize a long short-term memory network (LSTM) to capture the temporal features within the audio signal. We introduce a multi-scale graph attention network (MSGAT), which leverages the various implicit relationships between notes to enhance the interaction between different notes. Experimental results demonstrate that our model achieves high accuracy in detecting the onset and offset of notes on public datasets. In both frame-level and note-level metrics, DAFE-MSGAT achieves performance comparable to the state-of-the-art methods, showcasing exceptional transcription capabilities.

PPP time transfer using an adaptive clock constraint model

ABSTRACT In the processing of Precise Point Positioning (PPP) data, the receiver clock is approached with nearly infinite uncertainty, rendering it difficult to fulfill the requirements of high-precision time frequency applications. Therefore, a receiver clock model is essential. In this study, we first analyze the “over-constraint” problem in the existing clock model and subsequently propose a new clock model, called the Adaptive Clock Constraint (ACC) model, which relies on a sliding window to update covariance and frequency characteristics parameters in real-time. To verify the robustness of the ACC model, three experiments were conducted, and the results show that the ACC model not only is suitable for different types of atomic clock stations but also has superior frequency stability and time transfer precision in contrast to the BIPM PPP, the IGS products and White Noise (WN) model results. Using the optical fiber results as a time reference, the STD of the time difference between the ACC model and optical fiber results is 0.13 ns and the frequency stability is 1.28 × 10−16 on average for one week, representing improvements of more than 10% and 15% compared with the BIPM PPP results.

A Novel Approach of the Viscoelasticity of Axially Functional Graded Bar and Application of Harmonic Vibration Analysis of an Isotropic Beam as Support

The use of smart materials and passive controllers in modern technologies has stimulated the study of vibration in elastic systems with viscoelastic damping. It is also possible to create components with precise material distribution coefficients and distinct properties, such as Functionally Graded Materials. This work investigates the resonant frequency characteristics of a beam supported at its ends by Axially Functionally Graded (AFG) viscoelastic bars using the finite element method. The set of equations governing motion is obtained by assuming Euler–Bernoulli beam theory for the beam and bar theory for the bars using Lagrange’s equations. The material properties of the functionally graded bar is assumed to vary through the length according to the power law distribution. The longitudinal loss factor values are used to define the internal damping coefficient, which is also dependent on the Young’s modulus value varying along the bar. The effects of the length-varying material properties and internal damping of the FG support bars on the force transmission TR and frequency parameters λ are examined in detail. No study has been found in the literature on the vibration of viscoelastic FG bar-supported beams subjected to a harmonic force at the centre point. It is shown that using bars formed with combinations of different materials considering material damping will be useful to keep the vibration level and force transmission at a certain value and control the frequency parameters.

Vibration behavior of a microperforated plate within an acoustic nonlinear framework

Microperforated plates (MPP) can add substantial damping in the low-frequency range. MPP are known to dissipate energy through thermo-viscous interactions between shearing adjacent fluid layers near the perforation solid walls. Under linear operating conditions, a previous work carried out by the authors showed that the added damping reaches a maximum at a characteristic frequency which solely depends on the perforation parameters. However, MPP is also suitable in environments subject to high levels of mechanical excitation and, consequently, high fluid velocity within the perforations. Two types of nonlinearities should then be considered: (1) an acoustic nonlinearity induced by high fluid velocity, and (2) a nonlinearity induced by large structural displacements. This work only explores the former. The acoustic nonlinearity is modelled by the Forchheimer resistivity correction, a function of the fluid-solid relative velocity in the perforations, introduced into the equations subsequently solved numerically. Experimental measurements using a laser vibrometer on a perforated cantilever beam validate the proposed model. Results show that, under high excitation levels and at the characteristic frequency, the maximum added damping can reach a maximum, depending on the MPP parameters, at a critical value of the relative fluid-solid velocity.

Road traffic noise prediction model "ASJ RTN-Model 2023" proposed by the Acoustical Society of Japan - Part 2: Structure-borne noise of viaducts

The latest road traffic noise prediction model "ASJ RTN-Model 2023" was published by the Research Committee on Road Traffic Noise of the Acoustical Society of Japan (ASJ) in this April. In this new version, the method for predicting structure-borne noise from viaducts was also updated. Specifically, the calculation method was updated to separate direct sound and ground-reflected sound, and the sound power levels on structure-borne noise from viaducts have revised accordingly based on the latest research results. This paper describes the method for measuring structure-borne noise, the latest values of noise emission and its frequency characteristics, and the method for predicting structure-borne noise.

Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion

BackgroundEpilepsy is a prevalent neurological disorder in which seizures cause recurrent episodes of unconsciousness or muscle convulsions, seriously affecting the patient’s work, quality of life, and health and safety. Timely prediction of seizures is critical for patients to take appropriate therapeutic measures. Accurate prediction of seizures remains a challenge due to the complex and variable nature of EEG signals. The study proposes an epileptic seizure model based on a multidimensional Transformer with recurrent neural network(LSTM-GRU) fusion for seizure classification of EEG signals.MethodologyFirstly, a short-time Fourier transform was employed in the extraction of time-frequency features from EEG signals. Second, the extracted time-frequency features are learned using the Multidimensional Transformer model. Then, LSTM and GRU are then used for further learning of the time and frequency characteristics of the EEG signals. Next, the output features of LSTM and GRU are spliced and categorized using the gating mechanism. Subsequently, seizure prediction is conducted.ResultsThe model was tested on two datasets: the Bonn EEG dataset and the CHB-MIT dataset. On the CHB-MIT dataset, the average sensitivity and average specificity of the model were 98.24% and 97.27%, respectively. On the Bonn dataset, the model obtained about 99% and about 98% accuracy on the binary classification task and the tertiary upper classification task, respectively.ConclusionThe findings of the experimental investigation demonstrate that our model is capable of exploiting the temporal and frequency characteristics present within EEG signals.

ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING

The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteristics and significant noise interference. The proposed method of multilevel recurrent time-frequency segmentation allows for the modification of time segment durations and the use of segments of varying lengths, providing flexibility in signal processing and adaptation to current conditions. This adaptability enables optimal signal processing for each individual case, taking into account short-term impulses, long-term fluctuations, and various types of noise and distortions. This approach effectively separates frequency components and reduces interference between them, which is crucial for maintaining high signal quality and communication stability in cognitive networks. It has been proven that the use of adaptive filters such as LMS (Least Mean Squares) and RLS (Recursive Least Squares), as well as fast Fourier Transform (STFT), wavelet, and Hilbert transforms at different stages of multilevel time-frequency segmentation, significantly enhances signal interference resistance and energy efficiency. Comparative analysis of signal metrics before and after filtering and transformation shows an increase in signal quality by 14,3–24,5% and a reduction in noise levels by 21,7–29,6%. The wavelet transform, in particular, proved to be highly effective, allowing for precise extraction of useful frequency components from the noise background and improving signal parameters through dynamic adjustment to specific radio environment conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its ability to ensure consistently high-quality processing of complex signal ensembles even in dynamic cognitive radio environments.

Acoustic radiation of a fluid-saturated microperforated plate

It is known that fluid-saturated microperforated plates (MPP) dissipate energy and are substantially damped through thermoviscous dissipation taking place in the thermoviscous skin on the solid wall of the perforations. The aim of this work is to explore the acoustic radiation of a microperforated periodic cell to investigate the intercellular coupling and the complexity of near-field radiation, and to derive a criterion related to the near-field far-field transition. A numerical modeling using the finite element method is developed to solve the elastic and thermo-visco-acoustic (TVA) multiphysics problem. The TVA calculates explicitly the thermoviscous losses on the acoustic wave in the periodic cell studied. Numerical results show that the distance of the near-far field transition is significant at low frequencies, passing through a minimum for a frequency depending on perforation parameters (diameter and ratio) before increasing again as a frequency function. The radiated acoustic power and radiation efficiency are compared with those derived from the theoretical MPP vibration model. It is found that the microperforations and resulting added damping, around a characteristic frequency, reduce the acoustic radiation efficiency of the MPP compared to the corresponding non-perforated plate.

Recent investigation on VHF sound exposure sources around us

We have reported recent investigation of sound exposure sources in very-high frequency (VHF) region as the activities for research project of VHF sounds in Japan. Recently, the devices which utilize or generate VHF sounds as like rodent repelling devices, ultrasonic cleaning machines, IH cookers and corner sensors of automobile continue to increase in public space or our living environment. The VHF sounds in high level have physical or/and psychological effects for young people who can hear such sounds. So, in environment around us, we identified VHF sound sources using acoustic camera and measured the sound pressure level for such sources. In this paper, we introduce newly-discovered several VHF sounds: for example, ultrasounds from corner sensors of automobile car, VHF sounds from photovoltaic installation and VHF sounds of interior train, and so on. From both the results measured by acoustic camera and microphones show source positions and frequency characteristics of VHF sounds. However these play important role in our life environment, we need to note that these contain potential hazard for young people who can hear such VHF sounds.

Study on the vibration characteristics of angular contact ball bearing cage under rolling-sliding compound motion

Abstract Cage fracture has become the main form of bearing failure, the study of factors affecting the operating condition of the cage has an important impact on the operating performance and life of bearings. This study takes the angular contact ball bearing H7006C as the object of study and extracts the bearing inner ring and cage speed to analyze the bearing slip rate law. Extract cage vibration data and analyze cage vibration frequency characteristics using the VMD method. The effects of load and speed induced bearing slippage on the vibration characteristics of cages are investigated by varying the induced bearing slippage factors. The effects of load and speed induced bearing slip on the vibration characteristics of cages are investigated separately by varying the induced bearing slip factors. The study found that, at a fixed rotational speed, the bearing slip rate decreases as the axial load increases from 0 to 350N, and then stabilizes beyond 350N. Under fixed load conditions, the bearing slip rate increases with the rotational speed ranging from 1000 to 9000rpm. At a constant speed, the axial load is varied within 50 to 100N, resulting in vibration frequency increases of 72Hz, 43.5Hz, and 24Hz at 1000 rpm, 5000 rpm, and 9000 rpm, respectively. The characteristic frequency of cage vibration rises with increasing load, contributing to smoother cage operation. Nonetheless, as the rotational speed increases, the influence of axial load-induced slippage on the vibration characteristics of the cage diminishes gradually. At a set load of 500N, the amplitudes of the cage vibration eigenfrequencies at 1000rpm, 3000rpm, and 7000rpm are 0.0317, 0.0288, and 0.0174, respectively, decreasing with rotational speed. This indicates that the cage does not operate smoothly. The findings of this study serve as a theoretical foundation for detecting and diagnosing bearing slip and vibration issues in rotating machinery.