Vibration properties of Paulownia wood for Ruan sound quality using machine learning methods

As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

A Comprehensive 3-Steps Methodology for Vibration-Based Fault Detection,Diagnosis and Localization in Rotating Machines

In any industry,it is the requirement to know whether the machine is healthy or not to operate machine further.If the machine is not healthy then what is the fault in the machine and then finally its location.The paper is proposing a 3-Steps methodology for the machine fault diagnosis to meet the industrial requirements to aid the maintenance activity.The Step-1 identifies whether machine is healthy or faulty,then Step-2 detect the type of defect and finally its location in Step-3.This method is extended further from the earlier study on the 2-Steps method for the rotor defects only to the 3-Steps methodology to both rotor and bearing defects.The method uses the optimised vibration parameters and a simple Artificial Neural Network(ANN)-based Machine Learning(ML)model from the earlier studies.The model is initially developed,tested and validated on an experimental rotating rig operating at a speed above 1st critical speed.The proposed method and model are then further validated at 2 different operating speeds,one below 1st critical speed and other above 2nd critical speed.The machine dynamics are expected to be significantly different at these speeds.This highlights the robustness of the proposed 3-Steps method.

Vibrations induced by tunnel boring machine in urban areas: In situ measurements and methodology of analysis

Excavation with tunnel boring machine(TBM)can generate vibrations,causing damages to neighbouring buildings and disturbing the residents or the equipment.This problem is particularly challenging in urban areas,where TBMs are increasingly large in diameter and shallow in depth.In response to this problem,four experimental campaigns were carried out in different geotechnical contexts in France.The vibration measurements were acquired on the surface and inside the TBMs.These measurements are also complemented by few data in the literature.An original methodology of signal processing is pro-posed to characterize the amplitude of the particle velocities,as well as the frequency content of the signals to highlight the most energetic bands.The levels of vibrations are also compared with the thresholds existing in various European regulations concerning the impact on neighbouring structures and the disturbance to local residents.

Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine

A single freedom degree model of drilling bit-rock was established according to the vibration mechanism and its dynamic characteristics. Moreover, a novel identification method of rock and soil parameters for vibration drilling based on the fuzzy least squares(FLS)-support vector machine(SVM) was developed, in which the fuzzy membership function was set by using linear distance, and its parameters, such as penalty factor and kernel parameter, were optimized by using adaptive genetic algorithm. And FLS-SVM identification on rock and soil parameters for vibration drilling was made by changing the input/output data from single freedom degree model of drilling bit-rock. The results of identification simulation and resonance column experiment show that relative error of natural frequency for some hard sand from identification simulation and resonance column experiment is 1.1% and the identification precision based on the fuzzy least squares-support vector machine is high.

Research on Stick-Slip Vibration Suppression Method of Drill String Based on Machine Learning Optimization

During the drilling process,stick-slip vibration of the drill string is mainly caused by the nonlinear friction gen-erated by the contact between the drill bit and the rock.To eliminate the fatigue wear of downhole drilling tools caused by stick-slip vibrations,the Fractional-Order Proportional-Integral-Derivative(FOPID)controller is used to suppress stick-slip vibrations in the drill string.Although the FOPID controller can effectively suppress the drill string stick-slip vibration,its structure isflexible and parameter setting is complicated,so it needs to use the cor-responding machine learning algorithm for parameter optimization.Based on the principle of torsional vibration,a simplified model of multi-degree-of-freedom drill string is established and its block diagram is designed.The continuous nonlinear friction generated by cutting rock is described by the LuGre friction model.The adaptive learning strategy of genetic algorithm(GA),particle swarm optimization(PSO)and particle swarm optimization improved(IPSO)by arithmetic optimization(AOA)is used to optimize and adjust the controller parameters,and the drill string stick-slip vibration is suppressed to the greatest extent.The results show that:When slight drill string stick-slip vibration occurs,the FOPID controller optimized by machine learning algorithm has a good effect on suppressing drill string stick-slip vibration.However,the FOPID controller cannot get the drill string system which has fallen into serious stick-slip vibration(stuck pipe)out of trouble,and the machine learning algorithm is required to mark a large amount of data on adjacent Wells to train the model.Set a reasonable range of drilling parameters(weight on bit/drive torque)in advance to avoid severe stick-slip vibration(stuck pipe)in the drill string system.

Vibration Suppression of Unbalanced Machines UsingLumped Mass Dynamic Vibration Absorber

The vibration of machines due to rotating parts unbalance disturbs the machine functioning and shortens the lifetime of its parts. A dynamic vibration absorber is a favorite solution to suppress the machine vibration since its implementation does not require any modification neither on the machine nor on its installation. The paper considers an unbalanced machine to which a lumped mass dynamic vibration absorber is attached. Firstly, the machine equipped with the absorber is modeled, and the vibration expressions are extracted. Secondly, an original approach to optimize the absorber parameters is presented, and simulation results are advanced, when the absorber is undamped and damped. Thirdly, the absorber optimal parameters allowing the best vibration reduction of the machine are identified, showing bow the absorber should be designed, when the disturbance frequency is stable or unstable. The results are a significant contribution in the vibration control of unbalanced machines.

Vibration Analysis and Direct Control of XY Table Automated Assembly Machines

XY table automated assembly machines ensure time saving and quality improving in the electronics industry. Recently, due to the need of higher operation speeds and lighter machines in PCB （Printed Circuit Board） assembly, a challenging problem has arisen which is the table positioning vibration. The high speed with the machine flexibility, make the positioning vibration inevitable although the inner control. The positioning vibration is to be reduced otherwise the machine becomes useless. Firstly, the machine is modeled, the positioning vibration is formulated, and then analyzed. Secondly, using the analysis, three direct control methods are identified to decrease the positioning vibration, they are based on the kinematics, dynamics, and operation of the machine. Thirdly, the methods are examined numerically to evaluate their efficiency. Lastly, the identified methods are discussed to conclude on their application. The results are a real contribution in the vibration control of XY table automated assembly machines, which is classified as industrial knowhow.

Design and analysis of a two-dimensional vibration control mechanism based on vibro-impact damping

The robotic drilling always generates the axial vibration along the drill bit and the torsional vibration around the drill bit,which will adversely affect the drilling precision.A vibration control mechanism fixed between the end-effector and the robot is proposed,which can suppress the axial and torsional vibrations based on the principle of vibro-impact(VI)damping.The energy dissipation of the system by vibro-impact damping is analyzed.Then,the influence of the structure parameters on the vibration attenuation effect is studied,and a semi-active vibration control method of variable collision clearance is presented.The simulation results show that the control method has effective vibration control performance.

Numerical simulation and analysis of drill rods vibration during roof bolt hole drilling in underground mines

Structural deterioration in the roof in an underground mine can easily cause roof fall, and deterioration is difficult to detect. When drilling holes for roof bolts, there is a relationship between the vibration of the drill rod and the properties of the rock being drilled. This paper analyzes transverse, longitudinal, and torsional vibrations in the drill rod by using vibration theory. Characteristic indexes for three kinds of vibration are determined. Using the finite element analysis software ABAQUS, a model for drill rod vibration during the drilling of roof bolt holes was established based on the geological and mining conditions in the Guyuan Coal Mine, northern China. Results from the model determined that the transverse and the longitudinal vibration decrease as the rock hardness decreases. In descending order, sandstone,sandy mudstone, mudstone, and weak interbeds cause progressively less vibration when being drilled.The ranking for strata that cause decreasing torsional vibration is slightly different, being, in descending order, mudstone, sandstone, sandy mudstone, and weak interbeds. These results provide a theoretical basis for predicting dangerous roof conditions and the presence of weak interbeds to allow for adjusting bolt support schemes.

Investigation of active vibration drilling using acoustic emission and cutting size analysis

This paper describes an investigation of active bit vibration on the penetration mechanisms and bit-rock interaction for drilling with a diamond impregnated coring bit. A series of drill-off tests(DOTs) were conducted where the drilling rate-of-penetration(ROP) was measured at a series of step-wise increasing static bit thrusts or weight-on-bits(WOBs). Two active DOTs were conducted by applying 60 Hz axial vibration at the bit-rock interface using an electromagnetic vibrating table mounted underneath the drilling samples, and a passive DOT was conducted where the bit was allowed to vibrate naturally with lower amplitude due to the compliance of the drilling sample mountings. During drilling, an acoustic emission(AE) system was used to record the AE signals generated by the diamond cutter penetration and the cuttings were collected for grain size analysis. The instrumented drilling system recorded the dynamic motions of the bit-rock interface using a laser displacement sensor, a load cell, and an LVDT(linear variable differential transformer) recorded the dynamic WOB and the ROP, respectively. Calibration with the drilling system showed that rotary speed was approximately the same at any given WOB, facilitating comparison of the results at the same WOB. Analysis of the experimental results shows that the ROP of the bit at any given WOB increased with higher amplitude of axial bit-rock vibration, and the drill cuttings increased in size with a higher ROP. Spectral analysis of the AEs indicated that the higher ROP and larger cutting size were correlated with a higher AE energy and a lower AE frequency. This indicated that larger fractures were being created to generate larger cutting size. Overall, these results indicate that a greater magnitude of axial bit-rock vibration produces larger fractures and generates larger cuttings which, at the same rotary speed, results in a higher ROP.

Evaluation and prediction of blast induced ground vibration using support vector machine

We present the application of Support Vector Machine (SVM) for the prediction of blast induced ground vibration by taking into consideration of maximum charge per delay and distance between blast face to monitoring point. To investigate the suitability of this approach, the predictions by SVM have been compared with conventional predictor equations. Blast vibration study was carried out at Magnesite mine of Pithoragarh, India. Total 170 blast vibrations data sets were recorded at different strate-gic and vulnerable locations in and around to mine. Out of 170 data sets, 150 were used for the training of the SVM network as well as to determine site constants of different conventional predictor equations, whereas, 20 new randomly selected data sets were used to compare the prediction capability of SVM network with conventional predictor equations. Results were compared based on Co-efficient of Determination (CoD) and Mean Absolute Error (MAE) between monitored and predicted values of Peak Particle Veloc-ity (PPV). It was found that SVM gives closer values of predicted PPV as compared to conventional predictor equations. The coef-ficient of determination between measured and predicted PPV by SVM was 0.955, whereas it was 0.262, 0.163, 0.337 and 0.232 by USBM, Langefors-Kihlstrom, Ambraseys-Hendron and Bureau of Indian Standard equations, respectively. The MAE for PPV was 11.13 by SVM, whereas it was 0.973, 1.088, 0.939 and 1.292 by USBM, Langefors-Kihlstrom, Ambraseys-Hendron and Bureau of Indian Standard equations respectively.

Research on Horizontal Vibration of Heavy-Weight Drill Pipes in Directional Drilling

The mode of load and deformation of directional drilling string and the expression of trigonometric series of deflection equation are established by means of elastic deformation energy and of the vertical and horizontal bending. A calculation formula for natural frequency of horizontal resonance and rotational speed is derived based on the calculation method by Ritz, with which analysis is made for the cause and affecting factors of the excessive abrasion of heavy-weight drill pipe in high-angle holes so as to provide reference and basis for rational selection of drilling parameters and drilling tools in the future high-angle directional drilling.

Tunnel boring machine vibration-based deep learning for the ground identification of working faces

Tunnel boring machine(TBM) vibration induced by cutting complex ground contains essential information that can help engineers evaluate the interaction between a cutterhead and the ground itself.In this study,deep recurrent neural networks(RNNs) and convolutional neural networks(CNNs) were used for vibration-based working face ground identification.First,field monitoring was conducted to obtain the TBM vibration data when tunneling in changing geological conditions,including mixed-face,homogeneous,and transmission ground.Next,RNNs and CNNs were utilized to develop vibration-based prediction models,which were then validated using the testing dataset.The accuracy of the long short-term memory(LSTM) and bidirectional LSTM(Bi-LSTM) models was approximately 70% with raw data;however,with instantaneous frequency transmission,the accuracy increased to approximately 80%.Two types of deep CNNs,GoogLeNet and ResNet,were trained and tested with time-frequency scalar diagrams from continuous wavelet transformation.The CNN models,with an accuracy greater than 96%,performed significantly better than the RNN models.The ResNet-18,with an accuracy of 98.28%,performed the best.When the sample length was set as the cutterhead rotation period,the deep CNN and RNN models achieved the highest accuracy while the proposed deep CNN model simultaneously achieved high prediction accuracy and feedback efficiency.The proposed model could promptly identify the ground conditions at the working face without stopping the normal tunneling process,and the TBM working parameters could be adjusted and optimized in a timely manner based on the predicted results.

Vibration test of micro machined gyroscope based on high speed photography and SURF

Based on three kinds of dynamic test of MEMS, a dynamic system for the vibration test of micro machined gyroscope based on high speed photography is introduced. Firstly, the architecture of the system hardware is introduced. Secondly, the image tracking performance is compared by the test using the template matching algorithm, the mean shift algorithm and the SURF algorithm. The vibration curve shows that high speed photograph combined with SURF algorithm is faster, more ac- curate, and more suitable for the vibration test of micro machined gyroscope. After the frequency a- nalysis and related interpolation, more characteristics of micro gyroscope can be obtained.

THE DESIGN AND ANALYSIS OF VIBRATION STRUCTURE OF VERTICAL DYNAMIC BALANCING MACHINE

A new type of vibration structure (i.e. supporting system, called swing frame cus- tomarily) of vertical dynamic balancing machine has been designed, which is based on an analysis for the swing frame of a traditional double-plane vertical dynamic balancing machine. The static unbalance and couple unbalance can be e?ectively separated by using the new dynamic balancing machine with the new swing frame. By building the dynamics model, the advantages of the new structure are discussed in detail. The modal and harmonic response are analyzed by using the ANSYS7.0. By comparing the ?nite element modal analysis with the experimental modal analy- sis, the natural frequencies and vibration modes are found. There are many spring boards in the new swing frame. Their sti?nesses are di?erent and assorted with each other. Furthermore, there are three sensors on the measuring points. Therefore, the new dynamic balancing machine can measure static unbalance and coupling unbalance directly, and the interaction between them is faint. The result shows that the new vertical dynamic balancing machine is suitable for inertial measurement of ?ying objects, and can overcome the shortcomings of traditional double-plane vertical dynamic balancing machines, which the e?ect of plane-separation is inferior. The vertical dynamic balancing machine with the new vibration structure can ?nd wide application in the future. The modelling and analysis of the new vibration structure will provide theoretical basis and practical experience for designing new-type vertical dynamic balancing machines.

Reduction of Cogging Torque and Electromagnetic Vibration Based on Different Combination of Pole Arc Coefficient for Interior Permanent Magnet Synchronous Machine

Cogging torque and electromagnetic vibration are two important factors for evaluating permanent magnet synchronous machine(PMSM)and are key issues that must be considered and resolved in the design and manufacture of high-performance PMSM for electric vehicles.A fast and accurate magnetic field calculation model for interior permanent magnet synchronous machine(IPMSM)is proposed in this article.Based on the traditional magnetic potential permeance method,the stator cogging effect and complex boundary conditions of the IPMSM can be fully considered in this model,so as to realize the rapid calculation of equivalent magnetomotive force(MMF),air gap permeance,and other key electromagnetic properties.In this article,a 6-pole 36-slot IPMSM is taken as an example to establish its equivalent solution model,thereby the cogging torque is accurately calculated.And the validity of this model is verified by a variety of different magnetic pole structures,pole slot combinations machines,and prototype experiments.In addition,the improvement measure of the machine with different combination of pole arc coefficient is also studied based on this model.Cogging torque and electromagnetic vibration can be effectively weakened.Combined with the finite element model and multi-physics coupling model,the electromagnetic characteristics and vibration performance of this machine are comprehensively compared and analyzed.The analysis results have well verified its effectiveness.It can be extended to other structures or types of PMSM and has very important practical value and research significance.

Monitoring and Prediction of the Vibration Intensity of Seismic Waves Induced in Underwater Rock by Underwater Drilling and Blasting

All underwater drilling and blasting operations generate seismic waves.However,due to a lack of suitable vibration sensing instruments,most studies on the propagation of seismic waves have been limited to shorelines near construction areas or wharfs,whereas comparatively few studies have beerconducted on the larger seafloor itself.To address this gap,a seafloor vibration sensor system was developed and applied in this study that consists of an autonomous acquisition storage terminal,soft-ware platform,and hole-plugging device that was designed to record the blasting vibration intensities received through submarine rocks at a given measurement point.Additionally,dimensional analyses were used to derive a predictive equation for the strength of blast vibrations that considered the in fluence of the water depth.By combining reliable vibration data obtained using the sensor system in submarine rock and the developed predictive equation,it was determined that the water depth was ar important factor influencing the measured vibration strength.The results using the newly derivedequation were compared to those determined using the Sadowski equation,which is commonly used on land,and it was found that predictions using the derived equation were closer to the experimental values with an average error of less than 10%,representing a significant improvement.Based on these results the developed sensor system and preliminary theoretical basis was deemed suitable for studying the propagation behavior of submarine seismic waves generated by underwater drilling and blasting operations.

In Situ Determination of the Effect of Drilling Noise and Vibration on Growth of Grass Carp, Myloparyngodon Piceus

The study is carried out on the effect of drilling noise and vibration on growth of grass carp, Myloparyngodon Piceus, by using cut-fin marking method in situ. Compared with other methods, the method is more appropriate, for its operation is simpler and more data may be obtained under the same condition. The results show that drilling noise and vibration have significant effect on the growth of grass carp. Critical equivalent noise and vibration grade ( Nleq and Vleq ) are about 84.4 dB and 90.2 dB, and the affecting radius is about 8.5 m. The effect of drilling noise and vibration could be influenced by some factors, such as duration of pollution and body weight of grass carp, etc. Grass carp’s growth could rapidly recover after removing drilling noise and vibration, indicating that the drilling noise and vibration do not damage the fish organs and the effect is reversible. Therefore, the effect mechanism may be due to the activating response of non-hearing system.

Classifying Machine Learning Features Extracted from Vibration Signal with Logistic Model Tree to Monitor Automobile Tyre Pressure

Tyre pressure monitoring system(TPMS)is compulsory in most countries like the United States and European Union.The existing systems depend on pressure sensors strapped on the tyre or on wheel speed sensor data.A difference in wheel speed would trigger an alarm based on the algorithm implemented.In this paper,machine learning approach is proposed as a new method to monitor tyre pressure by extracting the vertical vibrations from a wheel hub of a moving vehicle using an accelerometer.The obtained signals will be used to compute through statistical features and histogram features for the feature extraction process.The LMT(Logistic Model Tree)was used as the classifier and attained a classification accuracy of 92.5%with 10-fold cross validation for statistical features and 90.5% with 10-fold cross validation for histogram features.The proposed model can be used for monitoring the automobile tyre pressure successfully.