Journal of Mechanical Engineering, Automation and Control Systems
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A vision-based deep learning approach for non-contact vibration measurement using (2+1)D CNN and optical flow
Get PDFThis paper introduces a proof-of-concept vision-based deep learning approach for vibration measurement, proposing a factorized (2+1)D Convolutional Neural Network (CNN) model to predict four vibration metrics: acceleration, velocity, displacement, and frequency, with a focus on rigid body motion. Unlike conventional neural network models that primarily focus on frequency prediction alone, this approach uniquely enables the simultaneous estimation of four critical vibration metrics, offering a comprehensive and cost-effective alternative to traditional contact-based sensors such as accelerometers. The framework relies on the visibility of a training fiducial marker, eliminates the need for calibration in controlled settings, enhancing scalability across specific environments. A curated dataset was generated using a controlled experimental setup comprising a single object in a lab-scale environment, augmented synthetically to enhance frequency diversity. An optical flow-based preprocessing algorithm synchronized motion features in recorded video inputs with measured vibration labels, improving measurement accuracy. The proposed model achieved an average Mean Absolute Percentage Error (MAPE) of 7.51 %, with acceleration predictions exhibiting the lowest error at 4.84 % and displacement the highest at 8.80 % across varying brightness levels and object-camera distances. Techniques such as Region of Interest (ROI) cropping and multi-section frame extraction were implemented to reduce computational complexity while further enhancing accuracy. These results highlight the framework’s potential for non-invasive vibration analysis, though its generalizability is limited by the single-object dataset. Future work will expand the dataset, integrate multi-sensor inputs, explore marker-less tracking methods, and enable real-time deployment for predictive maintenance and structural health monitoring
Finite element analysis and vibration simulation of electromagnetic imaging sensor housing based on ANSYS
Get PDFMining sensors work in harsh environments and are subject to complex vibrations. Its internal structure is prone to strength failure or fatigue damage. This paper focuses on the structural design of the front discharge and receiver housing inside the electromagnetic imaging sensor for coal-rock demarcation detection. Static analysis, modal analysis, and random vibration simulation were performed using ANSYS Workbench software to verify its reliability and strength in mining. In the static analysis, the thickness of the designed housing is 2 mm. The maximum equivalent elastic strain after applying a pressure of 0.5 MPa to the housing is 0.133 %, much less than the criterion of material fracture strain. This proves that it has excellent strength properties and will not experience strength failure. Modal analysis shows that the first-order intrinsic frequency of the housing is 3298.7 Hz. It is much higher than the vibration frequency in the actual working environment, which can effectively avoid resonance and improve the reliability of the structure. Random vibration simulation results show that the housing's maximum equivalent force and displacement are within the safe range, and the impact on the structural performance is negligible. These results provide a theoretical basis for the optimal design of the sensor housing and its application in complex vibration environments
Innovative design of a gear belt transmission for technological machines
Get PDFThe article presents the types of belt transmission designs, as well as the advantages of their use in mechanical engineering. Belt drives create loads as a result of excessive vibrations due to a flexible element (belt). A new design of an innovative toothed belt drive is proposed, which contains two paired driving and driven gear pulleys with different diameters and two belts with teeth covering them, while the gear ratios of each pair of gears are equal to each other. The simulation demonstrates a 25-38 % reduction in velocity fluctuation compared to conventional drives, confirming the effectiveness of the proposed design
Design peculiarities and kinematic analysis of a shaking conveyor with multiple transporting and screening trays
Get PDFThe paper focuses on the design peculiarities and kinematic analysis of a novel shaking conveyor equipped with three interconnected transporting and screening trays. The goal is to develop a comprehensive mathematical model to describe the system’s motion and analyze the interplay between the trays, providing a basis for improved design and optimization. The scientific novelty lies in the detailed kinematic study of this specific multi-tray configuration, particularly the interaction of the dual beam systems actuating the intermediate tray, leading to complex coupled motion profiles. The practical value of the research is substantial for designing and optimizing such multi-functional vibratory equipment, as the kinematic data (displacements, velocities, accelerations) provide critical insights into material-tray interaction, aiding in predicting and enhancing material processing efficiency, estimating inertial loads for robust structural design, and informing vibration isolation strategies. The methods employed include the development of a kinematic diagram and corresponding motion equations for the multi-loop linkage mechanism, followed by numerical modeling of the system’s motion using Wolfram Mathematica software. The main results characterize the complex motion profiles for a steady-state operational frequency of 10 Hz, revealing distinct amplitudes and near-linear inclined trajectories for key hinges representing each tray. Notably, the upper tray exhibited the most significant displacements and accelerations, with horizontal accelerations reaching approximately 3 g and vertical accelerations around 1.3 g, indicating a motion profile conducive to effective material lifting, “throwing”, and bed stratification. Scopes of further research include a complete dynamic analysis incorporating mass properties and driving forces, experimental validation of the models, optimization of geometric and operational parameters, integration with Discrete Element Method (DEM) simulations for detailed material flow analysis, and investigations into wear, fatigue life, and advanced control strategies
Multi-stage quantitative risk assessment of a critical system in mining industry
Get PDFEngineering Asset Management (EAM) is a strategic approach focused on the optimal management of physical assets throughout their lifecycle. By integrating engineering principles with financial and operational strategies, EAM aims to enhance asset performance, reliability, and longevity while minimizing risks and costs. This holistic methodology ensures that machinery, equipment, and infrastructure operate efficiently, thereby reducing failures and maximizing productivity. A critical component of EAM is understanding the criticality of each asset within a system. Criticality analysis evaluates the potential impact of different failure modes, considering factors such as failure likelihood, consequences, system interdependencies, cost implications, and associated risks. This analysis is essential for prioritizing maintenance efforts and allocating resources effectively. Risk assessment plays a pivotal role in this context, involving the systematic identification, analysis, evaluation, and management of potential risks associated with asset failures. However, traditional risk assessment methods often face challenges due to subjectivity and variability in evaluations, which can lead to inconsistencies in maintenance decision-making. To address these challenges, this paper proposes a novel multi-stage quantitative Failure Modes, Effects, and Criticality Analysis (FMECA) framework. This approach systematically analyses failure rates, downtime, and cost implications, providing a comprehensive understanding of each failure mode's impact. By integrating these quantitative parameters, the framework enhances objectivity in risk assessment and supports more informed decision-making. It enables organisations to systematically prioritize maintenance activities and optimize resource allocation. This approach not only mitigates operational risks but also aligns asset management practices with overarching business objectives, leading to improved efficiency and reduced costs. The proposed methodology is particularly beneficial in industries such as mining, manufacturing, and aerospace, where unplanned downtime and maintenance costs can have significant operational and financial repercussions. By adopting this multi-dimensional approach, organizations can improve asset performance, enhance safety, and achieve more sustainable operations
Design and simulation verification of differential spiral bevel gear transmission based on baja off-road vehicle
Get PDFThe differential spiral bevel gear of Baja off-road vehicle is designed and verified. Based on the competition rules and vehicle transmission parameters, the key geometric parameters of the gear pair are determined, and the three-dimensional model and finite element analysis software are established by UG for static contact analysis and modal analysis. The results show that the maximum contact stress of the tooth surface is 411.4 MPa, and the natural frequency of the gear pair is much higher than the excitation frequency of the system, which can effectively avoid the resonance risk. Through analysis and verification, it meets its application conditions
Feature extraction of rolling bearing based on adaptive variational multi-harmonic mode extraction
Get PDFVariable multi-harmonic mode extraction (VMHME) not only has the advantages of high computational efficiency and extraction accuracy similar to variational mode extraction (VME), but also could extract the multi-harmonic components of periodic narrowband impulse signals in frequency band as wide as possible, making it very suitable for feature extraction in the event of rolling bearing failure. VMHME needs to accurately estimate the fault characteristic frequency of rolling bearing as its prior parameter, and small errors in estimating the fault characteristic frequency will cause significant deviations in the target extraction components. At present, the theoretical fault characteristic frequency of rolling bearings is commonly used as the estimated fault characteristic frequency. However, due to the installation deformation of rolling bearings and the random sliding between the rolling elements and the raceway during operation, it can cause a deviation between the actual fault characteristic frequency and the theoretical fault characteristic frequency. The most scientific and effective method is to enable VMHME to adaptively obtain the fault characteristic frequency based on the characteristics of the analyzed signal itself. Therefore, this paper introduces the envelope harmonic product spectrum (EHPS) theory into VMHME and proposes an adaptive VMHME (AVMHME) method to effectively extract the multi harmonic components of the periodic narrowband impulse signal when rolling bearings fail. Feasibility of the proposed method is verified through simulation and rolling bearing’ early weak fault experiment, and its superiority is also verified through comparative analysis
Use of fragility curves to assess the seismic vulnerability of soft rock tunnels: a review
Get PDFDue to their distinct geotechnical and structural features, soft rock tunnels pose serious issues because of their seismic sensitivity. These tunnels, often constructed in formations with lower shear strength and higher deformability, are particularly susceptible to damage during earthquakes. Fragility curves, which graphically represent the probability that a structure may sustain damage up to or beyond a particular threshold as a function of seismic intensity, are essential tools for evaluating the seismic resilience of these infrastructures. This research looks closely at the use of fragility curves to assess the seismic vulnerability of soft rock tunnels. Exploring the fundamental concepts and methodologies involved in constructing fragility curves, including seismic hazard analysis, structural modeling, damage state definition, data collection and statistical analysis is looked at first. The review highlighted the integration of soft rock characteristics such as strength and deformation properties into the fragility assessment process. Key developments in the topic are covered such as how machine learning and Bayesian inference might improve the precision and usefulness of fragility curves. The paper identified key findings such as the high sensitivity of fragility curves to geotechnical properties and seismic intensity levels and emphasized the importance of accurate data collection and model calibration. Important gaps in seismic risk evaluations are filled by integrating cutting-edge methodologies, such as Bayesian inference and real-time machine learning models that clarify the seismic behaviour of soft rock tunnels in the real world. For the purpose of strengthening earthquake-resistant infrastructure in earthquake-prone areas, engineers, scholars and policymakers are given practical insights
Dynamic behaviors and double-frequency synchronization analysis of a dynamic vibration absorption system driven by three co-rotating exciters
Get PDFThe recovery efficiency of drilling fluid is directly affected by working performance of the vibration screen. Therefore, a newly dynamic vibration absorption system driven by different excitation frequencies is designed through double-frequency synchronization theory to improve the mechanical performance of screening equipment. Firstly, the differential equations of motion of vibration system are deduced by Lagrange method. Then, the theoretical conditions of the system implementing double-frequency synchronization are obtained based on asymptotic method, and stability criterion of the synchronization is revealed according to Routh-Hurwitz criterion. Subsequently, the effects of structure parameters on vibration isolation ability, synchronous state, and stability of synchronization are numerically discussed. Finally, the feasibility of the theoretical method and the obtained results is further verified by simulation and experiment. It is found that the vibration isolation and synchronization performance of the system is influenced by the motor parameters and system structure. The system has the best vibration isolation ability when ωm0= 157 rad/s, which is considered as the best operating frequency of the present vibration system. Meanwhile, when the mass ratio κ between the high-frequency co-rotating rotor and the low-frequency co-rotating rotor is smaller, the absolute value of the stability coefficient Si is larger, and the stability phase difference is smaller, and the system is more stable. The present work can provide theoretical direction for the design of new screening equipment
Research on fault diagnosis of rolling bearings based on multi-method fusion
No full textTo address the limitation that Variational Mode Decomposition (VMD) relies on empirical settings for the mode decomposition number K and penalty factor α, this paper proposed the RIME-VMD-KNN method for bearing fault diagnosis. Specifically, the RIME algorithm was used to intelligently optimize K and α of VMD, breaking the reliance on experience; Pearson Correlation Coefficient (PCC) was adopted to screen Intrinsic Mode Functions (IMFs) with high fault correlation for signal reconstruction, preserving key features; and the sample entropy of the reconstructed signal was input into KNN for fault identification. Experiments show that the optimization performance of RIME is superior to that of GA, GWO and AOA; the generalization ability is verified by supplementary tests on the XJTU-SY dataset; KNN is simpler and more efficient than SVM, proving the rationality of its selection; the confusion matrix and multiple random cross-validation confirm stability; and computing time and resource data are provided to verify the feasibility of embedded deployment. This method improves the reliability and real-time performance of diagnosis and has engineering value