Vibrational Motion Research Articles

Analytical Calculation and Low-Order Component Optimization of Axial Electromagnetic Force for an Axial Flux Motor

The low-order high-amplitude component of axial electromagnetic force is the leading cause of electromagnetic vibration and noise of axial flux permanent magnet (AFPM) motors. In this paper, based on the analytical calculation of the axial electromagnetic force for a 20-pole 24-slot AFPM motor, the low-order axial electromagnetic force component is weakened by optimizing the motor’s electromagnetic structure. First, the three-dimensional (3-D) structural model of the motor is equated to a two-dimensional (2-D) analytical model, which is used to calculate the magnetic field generated by each of the armature winding and permanent magnet. Then, combined with the Maxwell tensor method, the axial electromagnetic force waveform is further obtained, and the target low-order high-amplitude component is extracted by 2-D Fourier analysis. Finally, taking this axial electromagnetic force component minimization as the target function of optimization, considering the output torque and ripple as constraints, the genetic algorithm is used to search for the optimal point by varying the structural parameters of the motor. The results show that the target low-order high-amplitude component of the axial electromagnetic force of the optimized motor is effectively suppressed, and the output torque ripple decreases with slightly increased torque amplitude, which provides a practical method for vibration and noise reduction of AFPM motors.

Simulation Methods to Model Chemical Processes at Elevated Pressures and the Theory of Non-ideal Reaction Systems

Literature analysis shows that the main method to model the equilibrium characteristics of reaction systems at elevated pressures, including processes under supercritical conditions, are equations of state describing the non-ideality of the vapor and liquid phases, while the law of mass action is applied to describe the kinetics of the elementary and chemical stages. The mentioned difference in the types of models used to describe the equilibrium and kinetic characteristics of the same experimental system under study violates thesecond law of thermodynamics formulated by Clausius. The only theoretical method consistent with the second law of thermodynamics is the molecular theory based on the lattice gas model. In order to satisfy the second law of thermodynamics, molecular models must provide the self-consistent description of the rates of the chemical process at the equilibrium and elementary stages. This means that the molecular models must providea single mathematical apparatus to calculate the states of the system both outside and inside the equilibrium point. The molecular models can differ in both the effective parameters of the interparticle interaction and the methods of refining these models due to taking into account distinctions in sizes, contributions of the vibrational motions of the components, as well as the accuracy of description of the correlation effects. To ensure the self-consistent description of the equilibrium and kinetics, the models must at least reflect the effects of direct correlations. One-particle approximations (mean field, chaotic, density functional) do not correspond to the self-consistency condition and violate the second law of thermodynamics.

A Deep Learning Approach for Motor Fault Detection using Mobile Accelerometer Data

Electrical machines, which provide many conveniences in our daily life, may experience malfunctions that may adversely affect their performance and the general functioning of the industrial processes in which they are used. These failures often require maintenance or repair work, which can be expensive and time consuming. Therefore, minimizing the risk of malfunctions and failures and ensuring that these machines operate reliably and efficiently play a critical role for the industry. In this study, a one-dimensional convolutional neural network (1D-CNN) based fault diagnosis model is proposed for electric motor fault detection. Motor vibration data was chosen as the input data of the 1D-CNN model. Motor vibration data was obtained from a mobile application developed by using the three-axis accelerometer of the mobile phone. Three-axis data (X-axis, Y-axis and Z-axis) were fed to the model, both separately and together, to perform motor fault detection. The results showed that even a single axis data provides error-free diagnostics. With this fault detection method, which does not require any connection on or inside the motor, the fault condition in an electric motor has been detected with high accuracy.

Intra-protein interacting collective modes in the terahertz frequency region.

Understanding how proteins work requires a thorough understanding of their internal dynamics. Proteins support a wide range of motions, from the femtoseconds to seconds time scale, relevant to crucial biological functions. In this context, the term "protein collective dynamics" refers to the complex patterns of coordinated motions of numerous atoms throughout the protein in the sub-picosecond time scale (terahertz frequency region). It is hypothesized that these dynamics have a substantial impact on the regulation of functional dynamical mechanisms, including ligand binding and allosteric signalling, charge transport direction, and the regulation of thermodynamic and thermal transport properties. Using the theoretical framework of hydrodynamics, the collective dynamics of proteins had previously been described in a manner akin to that of simple liquids, i.e. in terms of a single acoustic-like excitation, related to intra-protein vibrational motions. Here, we employ an interacting-mode model to analyse the results from molecular dynamics simulations and we unveil that the vibrational landscape of proteins is populated by multiple acoustic-like and low-frequency optic-like modes, with mixed symmetry and interfering with each other. We propose an interpretation at the molecular level of the observed scenario that we relate to the side-chains and the hydrogen-bonded networks dynamics. The present insights provide a perspective for understanding the molecular mechanisms underlying the energy redistribution processes in the interior of proteins.

Evaluation of Ride Comfort under Vortex-Induced Vibration of Long-Span Bridge

The increasing number of suspension bridges has led to designs favoring greater length and flexibility, resulting in a common problem of vortex-induced vibration. While vortex-induced vibration typically does not cause structural damage, it diminishes the bridge’s fatigue resistance and has a detrimental impact on ride comfort. Additionally, this study introduces a road–bridge–vehicle vibration model, proposing an evaluation method for assessing ride comfort during vortex-induced vibrations in long-span bridges. This method features simplified modeling and swift calculations, circumventing the need for intricate finite element modeling and iterative solving. Furthermore, it evaluates ride comfort for vehicles crossing a prototype long-span suspension bridge using the Overall Vibration Total Value (OVTV) and Motion Sickness Incidence (MSI) criteria. This study also analyzes the influence of various parameters on OVTV and MSI, including vehicle speed, road grade, vortex-induced vibration frequency, and amplitude. It establishes a reference limit for vortex-induced vibration amplitude based on OVTV and MSI values. Moreover, the study substantiates that, within the context of vortex-induced vibration, the MSI value is more suitable for evaluating driving comfort compared to the OVTV.

The Impact of Vibrating Screen Startup Time on Vibration Amplitude and Energy Consumption in Transient State

The article presents the results of research on the trajectory and amplitude values during the startup of a vibrating screen, depending on the set startup time. The research was conducted using a suspended vibrating screen with a segmental vibration trajectory, where vibration excitation was achieved through two vibratory motors directly attached to the screen frame. The vibratory motors were powered with three-phase voltage and connected to the grid through frequency converters, allowing for current control in steady-state and startup conditions. The analysis focused on the vibration trajectory and the resulting amplitude values for nine selected startup times (ranging from 0 s to 8 s). Two measurement methods were employed: using piezoelectric acceleration sensors and optical recording. Additionally, the momentary power consumption by the screen drive was recorded, and the maximum current flowing in the power supply circuit was determined. These studies enabled the determination of the most favourable startup parameters that reduce the power demand of the drive without significantly affecting the kinematic parameters of the screen during its startup. The results of experimental research presented in the article regarding the startup time of the screening machine and its influence on vibration parameters have shown that the startup time significantly impacts the amplitude values that occur during startup. In the analysed case, the most favourable startup time was 3 s. Power consumption studies during startup revealed that startup time has a notable influence on momentary power consumption. The conducted research demonstrated that for startup times below 0.2 s, the maximum momentary power was the highest (reaching 5 kW, with a current consumption of 4.5 times the rated current). Regarding energy efficiency, the most favourable startup times exceed 2.0 s. Extending these startup times can significantly reduce the power demand of the drive, even up to threefold, thereby enabling the selection of motors with much lower power ratings.

Study of low-frequency spectroscopic characteristics of γ-aminobutyric acid with THz and low-wavenumber Raman spectroscopy

γ-aminobutyric (GABA) is the most important inhibitory neurotransmitier in vertebrate central nervous systems. The content level of GABA is related to the different degree of malignancy gliomas. Thus, it can be considered a promising glioma biomarker. In this paper, the spectroscopic properties of GABA have been characterized by combining the THz spectroscopy with low-wavenumber Raman spectroscopy. The experimental results showed that, GABA exhibited three absorption peaks and three refractive index peaks in the range of 0.6–2.1 THz. The limit of detection can reach up to 0.428 % based on the absorption coefficient at the peak of 2.04 THz. Moreover, the low-wavenumber Raman spectrum of GABA showed seven characteristic peaks at 41.0, 50.8, 58.8, 77.2, 98.8, 115.6, 141.2 cm−1 in 0–150 cm−1 region. Moreover, the THz and low-wavenumber theoretical spectra of GABA were simulated with solid-state density function theory, respectively. The calculated results were in good agreement with the experimental observations. On the basis of calculated result, the vibrational motions of each THz and Raman characteristic modes were quantitatively decomposed by analytical mode-decoupling method, where the contribution percentages of external translation, external librations and intramolecular vibration of each vibration modes were analyzed Furthermore, the low-frequency characteristics of GABA was analyzed by combining the THz and low-wavenumber Raman spectroscopy. It is beneficial for the structural information analysis and quantitative identification of biomarker GABA in early stage diagnosis of glioma.

Hearing loss prevention at loud music events via real-time visuo-haptic feedback

Hearing loss is becoming a global problem, partly as a consequence of exposure to loud music. People may be unaware about the harmful sound levels and consequent damages caused by loud music at venues such as discotheques or festivals. Earplugs are effective in reducing the risk of noise-induced hearing loss but have been shown to be an insufficient prevention strategy. Thus, when it is not possible to lower the volume of the sound source, a viable solution to the problem is to relocate to quieter locations from time to time. In this context, this study introduces a bracelet device with the goal of warning users when the music sound level is too loud in their specific location, via haptic, visual or visuo-haptic feedback. The bracelet embeds a microphone, a microcontroller, an LED strip and four vibration motors. We performed a user study where thirteen participants were asked to react to the three kinds of feedback during a simulated disco club event where the volume of music pieces varied to reach a loud intensity. Results showed that participants never missed the above threshold notification via all types of feedback, but visual feedback led to the slowest reaction times and was deemed the least effective. In line with the findings reported in the hearing loss prevention literature, the perceived usefulness of the proposed device was highly dependent on participants’ subjective approach to the topic of hearing risks at loud music events as well as their willingness to take action regarding its prevention. Ultimately, our study shows how technology, no matter how effective, may not be able to cope with these kinds of cultural issues concerning hearing loss prevention. Educational strategies may represent a more effective solution to the real problem of changing people’s attitudes and motivations to want to protect their hearing.

Experimenting ‘In Vivo’ with Dynamic Glottal Parameters: Methodological Issues, Technical Tips & Tricks and Preventable Pitfalls

‘In vivo’ studies pertaining to dynamics of vocal fold vibration motion, to vocal fold contact and collision, to vocal onset and offset and to mechanical efficiency all need valid, sensitive and precise measurements of the different mechanical parameters involved. This is also true for investigating the physiological correlates of particular acoustic events like register breaks or diplophonia. The main physical parameters involved are: vocal fold movement and shaping, particularly the velocity of tissue displacement, glottal area, tissue distortion, intraglottal pressure, transglottal air flow, vocal fold contact and collision stress, etc. This article presents a critical review of the instruments and techniques involved in the direct measurements of the glottal dimensions and movements, the transglottal airflow, the VF contact changes, the pressures and the sound acoustic pressure. In each case are analyzed the methodological aspects that are critical for validly calibrating and synchronizing these signals, and correcting them for time delays. Moreover, it is shown how new parameters, like vocal fold velocity, intraglottal pressure, vocal fold collision stress, can be inferred from these (raw or after differentiation) signals by combining them. Finally, the discussion focuses on weighing advantages and limitations of techniques for monitoring the glottal area, i.e. photometry and the high-speed imaging, the latter involving the relevance in this scope of future developments in endoscopic and external imaging techniques, and in image processing software. Our aim is to facilitate the work of future researchers by showing how to solve important technical pitfalls, how to apply the necessary corrective measures - and which ones - where needed, and how to get the most out of combinatorial measures.

Efficacy of vibration motion in comparison to reciprocation motion in retreatment of root canals obturated with lateral compaction technique: an in vitro study

Efficacy of vibration motion in comparison to reciprocation motion in retreatment of root canals obturated with lateral compaction technique: an in vitro study

Research on Three-Phase Asynchronous Motor Fault Diagnosis Based on Multiscale Weibull Dispersion Entropy.

Three-phase asynchronous motors have a wide range of applications in the machinery industry and fault diagnosis aids in the healthy operation of a motor. In order to improve the accuracy and generalization of fault diagnosis in three-phase asynchronous motors, this paper proposes a three-phase asynchronous motor fault diagnosis method based on the combination of multiscale Weibull dispersive entropy (WB-MDE) and particle swarm optimization-support vector machine (PSO-SVM). Firstly, the Weibull distribution (WB) is used to linearize and smooth the vibration signals to obtain sharper information about the motor state. Secondly, the quantitative features of the regularity and orderliness of a given sequence are extracted using multiscale dispersion entropy (MDE). Then, a support vector machine (SVM) is used to construct a classifier, the parameters are optimized via the particle swarm optimization (PSO) algorithm, and the extracted feature vectors are fed into the optimized SVM model for classification and recognition. Finally, the accuracy and generalization of the model proposed in this paper are tested by adding raw data with Gaussian white noise with different signal-to-noise ratios and the CHIST-ERA SOON public dataset. This paper builds a three-phase asynchronous motor vibration signal experimental platform, through a piezoelectric acceleration sensor to discern the four states of the motor data, to verify the effectiveness of the proposed method. The accuracy of the collected data using the WB-MDE method proposed in this paper for feature extraction and the extracted features using the optimization of the PSO-SVM method for fault classification and identification is 100%. Additionally, the proposed model is tested for noise resistance and generalization. Finally, the superiority of the present method is verified through experiments as well as noise immunity and generalization tests.

Feasibility and usability of a virtual-reality-based sensorimotor activation apparatus for carpal tunnel syndrome patients.

This study aimed to assess the usability of a virtual reality-assisted sensorimotor activation (VRSMA) apparatus for individual digit rehabilitation. The study had two main objectives: Firstly, to collect preliminary data on the expectations and preferences of patients with carpal tunnel syndrome (CTS) regarding virtual reality (VR) and an apparatus-assisted therapy for their affected digits. Secondly, to evaluate the usability of the VRSMA apparatus that was developed. The VRSMA system consists of an apparatus that provides sensory and motor stimulation via a vibratory motor and pressure sensor attached to a button, and a virtual reality-based visual cue provided by texts overlaid on top of a 3D model of a hand. The study involved 10 CTS patients who completed five blocks of VRSMA with their affected hand, with each block corresponding to the five digits. The patients were asked to complete a user expectations questionnaire before experiencing the VRSMA, and a user evaluation questionnaire after completing the VRSMA. Expectations for VRSMA were obtained from the questionnaire results using a House of Quality (HoQ) analysis. In the survey for expectations, participants rated certain attributes as important for a rehabilitation device for CTS, with mean ratings above 4 for attributes such as ease of use, ease of understanding, motivation, and improvement of hand function based on clinical evidence. The level of immersion and an interesting rehabilitation regime received lower ratings, with mean ratings above 3.5. The survey evaluating VRSMA showed that the current prototype was overall satisfactory with a mean rating of 3.9 out of 5. Based on the HoQ matrix, the highest priority for development of the VRSMA was to enhance device comfort and usage time. This was followed by the need to perform more clinical studies to provide evidence of the efficacy of the VRSMA. Other technical characteristics, such as VRSMA content and device reliability, had lower priority scores. The current study presents a potential for an individual digit sensorimotor rehabilitation device that is well-liked by CTS patients.

Quantum Dynamics of Oblique Vibrational States in the Hénon-Heiles System.

In this paper, we study the quantum time evolution of oblique nonstationary vibrational states in a Hénon-Heiles oscillator system with two dissociation channels, which models the stretching vibrational motions of triatomic molecules. The oblique nonstationary states we are interested in are the eigenfunctions of the anharmonic zero-order Hamiltonian operator resulting from the transformation to oblique coordinates, which are defined as those coming from nonorthogonal coordinate rotations that express the matrix representation of the second-order Hamiltonian in a block diagonal form characterized by the polyadic quantum number n = n1 + n2. The survival probabilities calculated show that the oblique nonstationary states evolve within their polyadic group with a high degree of coherence up to the dissociation limits on the short time scale. The degree of coherence is certainly much higher than that exhibited by the local nonstationary states extracted from the conventional orthogonal rotation of the original normal coordinates. We also show that energy exchange between the oblique vibrational modes occurs in a much more regular way than the exchange between the local modes.

Mating and post-copulation behavior in the tea leafhopper, Empoasca onukii (Hemiptera: Cicadellidae).

The tea leafhopper, Empoasca onukii, relies on substrate-borne vibrations for sexual communication and is mainly controlled with chemical pesticides, which poses risks to the environment and food safety. Based on previous studies, we conducted a series of behavioral assays by simultaneous observation of vibration signals and movement to investigate the mating and post-copulation behavior of tea leafhoppers. During mating, the activity of E. onukii was restricted to dawn and dusk and concentrated on the sixth or seventh mature leaf below the tea bud. By comparing the time spent in locating females among different males, the timely reply of females was the key factor affecting mating success. Empoasca onukii females mated only once in their lives, while males could mate multiple times. Male rivalry behavior involved two distinct strategies. The rivals could send disruptive pulses to overlap the male calling signals, locate the courting males, and drive them away after contact. Some rivals could emit mating disruption signals (MDSs) to interrupt the ongoing identification duet and establish their own mating communication. Both identification and location duets could be interrupted by playback of MDSs, which is essential to create effective synthetic signals to disrupt mating communication of E. onukii. Our study clarified the spatial and temporal distribution of E. onukii in mating and the function of MDSs, which will be essential to develop future vibrational mating disruption techniques for E. onukii and its energy-efficient application in the field.

Nonlinear Structural Vibration of Multi-Megawatt Vertical Axis Wind Turbine Blades-Part 1: Derivation of Motion Equations

Replacing fossil fuels with renewable energy is one of the important means to improve energy scarcity and environmental protection, and is of great significance for achieving sustainable development of human society. Vertical axis wind turbine (VAWT) is one of the key equipment for applying green energy — wind energy. During the operation of VAWT, its blades not only have to withstand periodic aerodynamic loads, but also suffer from centrifugal force effects and the Coriolis force effect caused by coupling with multi-dimensional deformation. The complex stress situation makes the blades susceptible to damage, resulting in structural resonance and instability of aeroelastic stability. This paper applies the Euler beam model and Hamilton principle to establish a nonlinear structural vibration motion equation for large-span vertical axis wind turbine blades with fully coupled four-dimensional deformation (i.e. lateral bending deformation, chord bending deformation, axial torsion deformation, and axial tensile deformation), and retains the nonlinear term of the motion equation to the third-order infinitesimal. Separating the motion equation into equilibrium equation and dynamic equation, it can be seen that the displacement of equilibrium state caused by the centrifugal force of the rigid body exists in the form of a coefficient in the dynamic equation, which will have an impact on the dynamic characteristics. This modeling work can provide a research foundation for exploring the dynamic characteristics and aeroelastic stability of VAWT blades in the future.

Model-inversion control to enforce tunable Duffing-like acoustical response on an Electroacoustic resonator at low excitation levels

The electroacoustic resonator is an efficient electro-active device for noise attenuation in enclosed cavities or acoustic waveguides. It is made of a loudspeaker (the actuator) and one or more microphones (the sensors). So far, the desired acoustic behaviour, expressed in terms of a linear-time-invariant relationship between sound pressure and vibrational motion (the acoustical impedance), has been more efficiently achieved by a model-inversion strategy which is implemented by driving the electrical current in the loudspeaker coil, based upon the measured pressure. The corrector transfer function is defined in the Laplace domain and digitally executed by the classical infinite-impulse-response technique, though a state–space representation could be employed. In this work, we are interested in enforcing a nonlinear behaviour at low sound excitation levels, where the electroacoustic resonator would normally behave as a linear-time-invariant system. Hence, in order to transform its acoustical response from linear to nonlinear, the model-inversion technique must be reformulated in time domain. The state–space representation of the relationship between the input measured pressure and the output electrical current gives the right perspective and the solution to this problem. We provide the conception of this model-inversion control algorithm capable of transforming a linear-time-invariant acoustical response to potentially any causal acoustical response of the electroacoustic resonator. Such control strategy is tested by targeting a Duffing acoustical response with tunable parameters. Both numerical simulations and experimental tests in quasi-open field validate the approach. The results provided in this contribution open the doors for conceiving non-conventional absorbers which can exploit nonlinear phenomena for noise mitigation even at low excitation amplitudes.

Objective tools in pediatric voice therapy: A path to improved outcomes

This is an invited presentation in the Special Session titled, “Voice therapy: objective measurement science and clinical efficacy.” Pediatric dysphonia (hoarseness) is a common condition with prevalence estimates ranging from 1.4% to 23.9%. Dysphonia can be detrimental to children both psychologically and academically; hence, early identification and restoration of optimal vocal health is critical. Direct visualization of vocal fold vibratory patterns is fundamental for appropriate diagnosis and treatment of vocal fold pathology. Thus, evaluation of vocal fold structure and cycle-to-cycle vibratory motion through techniques of direct visualization of the vocal fold is essential for clinical assessment and measurement of treatment outcomes. This session will discuss the most recent advances in objective voice assessment and measurement of voice therapy outcomes in children. The focus will be on discussing an innovative approach that leverages high-speed videoendoscopy and a cutting-edge custom developed laser projection system for quantitative evaluation of lesion size and comprehensive evaluation of opening and closing phase dynamics of the glottal cycle before and after voice therapy in children with vocal fold nodules.

Vocal Fold Vibration of the Whistle Register Observed by High-Speed Digital Imaging

Singers use a whistle register to sing at a fundamental frequency above 1000Hz. In previous studies, vocal fold vibrations with or without complete closure and partial vocal fold vibrations were observed depending on the subject. However, the production mechanism of the whistle register is not yet clearly understood because of the limitations of the imaging device for the glottis and subjects. This study aims to examine vocal fold vibrations in a whistle register. The dynamic behavior of the glottis was recorded for six singers (four females and two males) using a high-speed digital imaging device with a frame rate above 10,000fps. Audio signals were recorded simultaneously. The data were analyzed in the form of topography, glottal area waveforms, spectrograms, and phonovibrography to examine spatiotemporal patterns of glottal motion. The vibratory motion of the vocal folds was classified into six patterns. The first pattern was the entire vocal fold vibration with complete closure during the closed phase. The second to fifth was the entire vocal fold vibration without complete closure, where a gap was observed for the full length of the vocal folds for the second, at the posterior part of the glottis for the third, at the anterior for the fourth, and at both ends for the fifth. In the sixth pattern, the vocal folds vibrated partially. Our results support the previous findings on the vibration of the vocal folds. In addition, we identified novel vibratory patterns in the vocal folds. We conclude that the production of the whistle register is not just an extension of the falsetto register to the higher fundamental-frequency region; rather, the production mechanism of the whistle register appeared to be diverse as a means of vocalization.

MATLAB-Based Vibration Signal Processing for Fault Diagnosis

Traditionally, vibration signal processing has been performed using analog and digital signal analyzers or writing code in intermediate and high-level computer languages. However, the advent of higher-level interpretive-based signal processing software products such as MATLAB has added a new dimension to vibration signal processing. This paper presents a method for analyzing motor vibration data using MATLAB. The method first pre-processes the vibration data to remove noise and baseline wander. Then, the frequency spectrum of the vibration signal is calculated using the Fourier transform. The frequency spectrum is then used to identify the dominant frequencies in the vibration signal. These dominant frequencies can be used to identify potential problems with the motor, such as bearing defects or misalignment. The method was studied on a set of vibration data collected from open source online data of a real motor. The results showed that the method was able to identify the dominant frequencies in the vibration signal accurately. The method was also able to identify the potential problems with the motor. This paper demonstrates the effectiveness of using MATLAB for analyzing motor vibration data. The method presented in this paper can be used to improve the reliability and efficiency of motor maintenance.

Optimizing and Analyzing Performance of Motor Fault Diagnosis Algorithms for Autonomous Vehicles via Cross-Domain Data Fusion

Electric motors play a pivotal role in the functioning of autonomous vehicles, necessitating accurate fault diagnosis to ensure vehicle safety and reliability. In this paper, a novel motor fault diagnosis approach grounded in vibration signals to enhance fault detection performance is presented. The method involves capturing vibration signals from the motor across various operational states and frequencies using vibration sensors. Subsequently, the signals undergo transformation into frequency domain representations through fast Fourier transform. This includes normalizing and concatenating the amplitude frequency and phase frequency signals into comprehensive frequency domain information. Leveraging Gramian image-encoding attributes, cross-domain fusion of time-domain and frequency-domain data is achieved. Finally, the fused Gram angle field map is fed into the ConvMixer deep learning model, augmented by the ECA mechanism to facilitate precise motor fault identification. Experimental outcomes underscore the efficacy of cross-domain data fusion, showcasing improved pattern recognition and recognition rates for the models compared to traditional time-domain methods. Additionally, a comparative analysis of various deep learning models highlights the superior performance of the ECA-ConvMixer model. This study makes significant contributions by introducing a cross-domain data fusion method, merging time-domain and frequency-domain information to enhance motor vibration signal analysis. Additionally, the incorporation of the ECA-ConvMixer deep learning model, equipped with attention mechanisms, effectively captures critical features, thus serving as a robust tool for motor fault diagnosis. These innovations not only enhance diagnostic accuracy but also have broad applications in areas like autonomous vehicles and industry, leading to reduced maintenance expenses and enhanced equipment reliability.