A method for reducing noise radiated from structures by vibration absorbers is presented. Since usual design method for the absorbers is invalid for noise reduction, the peaks of noise power in the frequency domain as...A method for reducing noise radiated from structures by vibration absorbers is presented. Since usual design method for the absorbers is invalid for noise reduction, the peaks of noise power in the frequency domain as cost functions are applied. Hence, the equations for obtaining optimal parameters of the absorbers become nonlinear expressions. To have the parameters, an accelerated neural network procedure has been presented. Numerical calculations have been carried out for a plate type cantilever beam with a large width, and experimental tests have been also performed for the same beam. It is clarified that the present method is valid for reducing noise radiated from structures. As for the usual design method for the absorbers, model analysis has been given, so the number of absorbers should be the same as that of the considered modes. While the nonlinear problem can be dealt with by the present method, there is no restriction on the number of absorbers or the model number.展开更多
To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural netw...To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.展开更多
The work aims to provide a further investigation of the dynamic characteristics of an integral bladed disk（also called ‘blisk＇） with a Parallel Piezoelectric Network（PPN）. The PPN is constructed by parallelly in...The work aims to provide a further investigation of the dynamic characteristics of an integral bladed disk（also called ‘blisk＇） with a Parallel Piezoelectric Network（PPN）. The PPN is constructed by parallelly interconnecting the piezoelectric patches distributed in the blisk. Two kinds of PPN are considered, namely mono-periodic PPN and bi-periodic PPN. The former has a piezoelectric patch in each sector, and the later has one patch every few sectors. The vibration suppression performance of both kinds of PPN has been studied through modal analysis, forced response analysis, and statistical analysis. The research results turn out that the PPN will only affect mechanical frequencies near the electrical frequency clusters slightly, and the bi-periodic PPN will make the nodal diameter spectrum of the modes more complex, but the amplitude corresponding to the new nodal diameter component is much smaller than that of the nodal diameter component corresponding to the mono-periodic system. The mechanical coupling between the blades and the disk plays an important role in the damping effect of the PPN, and it should be paid attention to in applications. The mono-periodic PPN can effectively suppress the amplitude magnification of the forced response induced by the mistuning of the blisk; meanwhile, it can mitigate the vibration localization of the mistuned electromechanical system. If piezoelectric patches are set only in part of the sectors, the bi-periodic PPN still has a vibration suppression ability, but the effect is related to the number and spatial distribution of the piezoelectric patches.展开更多
Vibration monitoring by virtual sensing methods has been well developed for linear timeinvariant structures with limited sensors.However,few methods are proposed for Time-Varying(TV)structures which are inevitable in ...Vibration monitoring by virtual sensing methods has been well developed for linear timeinvariant structures with limited sensors.However,few methods are proposed for Time-Varying(TV)structures which are inevitable in aerospace engineering.The core of vibration monitoring for TV structures is to describe the TV structural dynamic characteristics with accuracy and efficiency.This paper propose a new method using the Long Short-Term Memory(LSTM)networks for Continuously Variable Configuration Structures(CVCSs),which is an important subclass of TV structures.The configuration parameters are used to represent the time-varying dynamic characteristics by the‘‘freezing"method.The relationship between TV dynamic characteristics and vibration responses is established by LSTM,and can be generalized to estimate the responses with unknown TV processes benefiting from the time translation invariance of LSTM.A numerical example and a liquid-filled pipe experiment are used to test the performance of the proposed method.The results demonstrate that the proposed method can accurately estimate the unmeasured responses for CVCSs to reveal the actual characteristics in time-domain and modal-domain.Besides,the average one-step estimation time of responses is less than the sampling interval.Thus,the proposed method is promising to on-line estimate the important responses of TV structures.展开更多
The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This pap...The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This paper develops a vibration isolation system of micro-manufacturing platform. The brains of many kinds of birds can isolate vibrations well, such as woodpecker’s brain. When a woodpecker pecks the wood at the speed as 1.6 times as the velocity of sound, its brain will tolerate the wallop 1 500 times of the weight of itself without any damage. The isolation mechanics and organic texture of woodpecker’s brain that has good isolation characteristics were studied. A structure model of vibration isolation system for the micro-manufacturing platform is established based on the bionics of the bird’s brain vibration isolation mechanism. In order to isolate effectively the high frequency vibrations from the ground, a rubber layer is used to isolate vibrations passively between the micro-manufacturing platform’s pedestal and the ground. This layer corresponds to the cartilage and muscles in the outer meninges of the bird’s brain. The active vibration isolation technique is adopted to isolate vibrations between the micro-manufacturing platform and the pedestal. Air springs are used as elastic components, which correspond to the interspaces between the outer meninges and the encephala of the bird’s brain. Actuators are made of giant magnetostrictive material, and it corresponds to the nerves and neural muscles linking the meninges and the encephala. The actuators and air springs are arranged vertically in parallel to make use of the giant magnetostrictive actuators effectively. The air springs support almost all weight of the micro-manufacturing platform and the giant magnetostrictive actuators support almost no weight. In order to realize high performance to isolate complex micro-vibration, the control method using a three-layer neural network is presented. This vibration control system takes into account the floor disturbance and the direct disturbance acting on the micro-manufacturing platform. The absolute acceleration of the micro-manufacturing platform is used as the performance index of vibration control. The performance of the control system is tested by numerical simulation. Simulation results show that the active vibration isolation system has good isolation performance against the floor disturbance and the direct disturbance acting on the micro-manufacturing platform in all the frequency range.展开更多
This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the d...This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the dynamics model of the system. A back propagation neural network （BPNN） based proportional-derivative （PD） algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.展开更多
Roller bearing failure is one of the most common faults in rotating machines.Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed.But feature extraction from fault signa...Roller bearing failure is one of the most common faults in rotating machines.Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed.But feature extraction from fault signals requires expert prior information and human labour.Recently,deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data.Given its robust performance in image recognition,the convolutional neural network(CNN)architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions.This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis.The first stage in the proposed method is to generate the RGB vibration images(RGBVIs)from the input vibration signals.To begin this process,first,the 1-D vibration signals were converted to 2-D grayscale vibration Images.Once the conversion was completed,the regions of interest(ROI)were found in the converted 2-D grayscale vibration images.Finally,to produce vibration images with more discriminative characteristics,an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images(RGBVIs)with sets of colours and texture features.In the second stage,with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions.Two cases of fault classification of rolling element bearings are used to validate the proposed method.Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy.Moreover,several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy,precision,recall,and F-score.展开更多
Wearable flexible sensors attached on the neck have been developed to measure the vibration of vocal cords during speech.However,highfrequency attenuation caused by the frequency response of the flexible sensors and a...Wearable flexible sensors attached on the neck have been developed to measure the vibration of vocal cords during speech.However,highfrequency attenuation caused by the frequency response of the flexible sensors and absorption of high-frequency sound by the skin are obstacles to the practical application of these sensors in speech capture based on bone conduction.In this paper,speech enhancement techniques for enhancing the intelligibility of sensor signals are developed and compared.Four kinds of speech enhancement algorithms based on a fully connected neural network(FCNN),a long short-term memory(LSTM),a bidirectional long short-term memory(BLSTM),and a convolutional-recurrent neural network(CRNN)are adopted to enhance the sensor signals,and their performance after deployment on four kinds of edge and cloud platforms is also investigated.Experimental results show that the BLSTM performs best in improving speech quality,but is poorest with regard to hardware deployment.It improves short-time objective intelligibility(STOI)by 0.18 to nearly 0.80,which corresponds to a good intelligibility level,but it introduces latency as well as being a large model.The CRNN,which improves STOI to about 0.75,ranks second among the four neural networks.It is also the only model that is able to achieves real-time processing with all four hardware platforms,demonstrating its great potential for deployment on mobile platforms.To the best of our knowledge,this is one of the first trials to systematically and specifically develop processing techniques for bone-conduction speed signals captured by flexible sensors.The results demonstrate the possibility of realizing a wearable lightweight speech collection system based on flexible vibration sensors and real-time speech enhancement to compensate for high-frequency attenuation.展开更多
This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into differe...This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into different bands at different levels and provides multiresolution or multiscale views of a signal which is stationary or nonstationary. Fuzzy mathematics processes uncertain problems in engineering and converts the attributes extracted by wavelet packets to fuzzy membership degree.To achieve self-organizing classification,the MAXNET neural network is employed.WPFCNN integrates the advantages of wavelet packets and fuzzy cluster with MAXNET.The approach is adopted to process and classify vibration signal of a NH_3 compressor in a petrochemical plant.The results indicate that it is a useful and effective intelligence classification in the field of condition monitoring and fault diagnosis.展开更多
Magnetorheological (MR) dampers are one of the most promising new devices for civil infrastructural vibration control applications. However, due to their highly nonlinear dynamic behavior, it is very difficult to obta...Magnetorheological (MR) dampers are one of the most promising new devices for civil infrastructural vibration control applications. However, due to their highly nonlinear dynamic behavior, it is very difficult to obtain of a mathematical model of inverse MR damper that has an explicit relationship between the desired damper force and the command signal (voltage). This force voltage relationship is especially required for the structural vibration control design and simulation using MR dampers. This paper focuses on using a neural network (NN) technique to emulate the inverse MR damper model. The output of the neural network can be used to command the MR damper for generating desired forces. Numerical simulations are also presented to illustrate the effectiveness of this inverse model in semi active vibration control using MR dampers.展开更多
Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
Combining with the characteristic of the fuzzy control and the neural networkcontrol(NNC), a new kind of the fuzzy neural network controller is proposed, and the synthesisdesign method of the control law and fast spee...Combining with the characteristic of the fuzzy control and the neural networkcontrol(NNC), a new kind of the fuzzy neural network controller is proposed, and the synthesisdesign method of the control law and fast speed learning algorithm of the parameters of networks areput forward. The output of the controller is composed of two parts, part one is derived on basis ofthe principle of sliding control, the lower order model and the estimated parameters of the plantare only required, part two is derived on basis FNN, it is used to compensate the uncertainties ofthe systems. Because new type of FNN controller extracts from the advantages of the intelligentcontrol and model based sliding mode control, the numbers of adjusting parameters and the structureof FNN are simplified at large, and the practical significance and variation range are attached toeach layer of the network and its connected weights, the control performance and learning speed areincreased at large. The Tightness of the conclusions is verified by the experiment of anelectro-hydraulic position servo system of the mold of the continuous casting machinery.展开更多
This study empirically tested if the personality trait of optimism and the interpersonal capability to generate optimism in one’s network nodes (i.e., alter-optimism) influences the social relationship patterns. The ...This study empirically tested if the personality trait of optimism and the interpersonal capability to generate optimism in one’s network nodes (i.e., alter-optimism) influences the social relationship patterns. The results provide evidence that optimism trait is independent from the way social networks of personal-issue sharing, advice-seeking, problem-solving, and innovation, are structured. In contrary, the alter-optimism capability does provide a good explanation of one’s social network position. Implications of these findings are discussed at the end.展开更多
Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The...Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The Second Order Reliability Method(SORM)has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures,when compared to the slower MCS techniques.However,SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation.The most suitable approach to do this is to use a symbolic solver,which renders the simulations very slow,although still faster than MCS.Moreover,the inability to obtain the derivative of the performance function with respect to some parameters,such as ply thickness,limits the capabilities of the classical SORM.In this work,a Neural Network-Based Second Order Reliability Method(NNBSORM)is developed to replace the finite element algorithm in the stochastic analysis of laminated composite plates in free vibration.Because of the ability to obtain expressions for the first and second derivatives of the NN system outputs with respect to any of its inputs,such as material properties,ply thicknesses and orientation angles,the need for using a symbolic solver to calculate the derivatives of the performance function no longer exists.The proposed approach is accordingly much faster,and easily allows for the consideration of ply thickness uncertainty.The present analysis showed that dealing with ply thicknesses as random variables results in 37%increase in the laminate’s probability of failure.展开更多
Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry.The likelihood of failure has the propensity of...Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry.The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions.Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery.The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features.In this paper, the efficacy and the leverage of a pre-trained convolutional neural network(CNN) is harnessed in the implementation of a robust fault classification model.In the absence of sufficient data, this method has a high-performance rate.Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier.The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly.The proposed approach is carried out on bearing vibration data and shows high-performance results.In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator(HI) under varying operating conditions for a given fault condition.展开更多
The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the...The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment.In engineering scenarios,co-frequency vibration faults are highlighted by rotational frequency and are difficult to identify,and existing intelligent methods require more hardware conditions and are exclusively time-consuming.Therefore,Lightweight-convolutional neural networks(LW-CNN)algorithm is proposed in this paper to achieve real-time fault diagnosis.The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method.Based on LW-CNN and data augmentation,the real-time intelligent diagnosis of co-frequency is realized.Moreover,a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis.It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.展开更多
The deep understanding on sand and sand dunes scale can be useful to reveal the formation mechanism of the sandstorm for early sandstorm forecast. The current sandstorm observation methods are mainly based on conventi...The deep understanding on sand and sand dunes scale can be useful to reveal the formation mechanism of the sandstorm for early sandstorm forecast. The current sandstorm observation methods are mainly based on conventional meteorological station and satellites remote sensing, which are difficult to acquire sand scale information. A wireless sensing network is implemented in the hinterland of desert, which includes ad hoc network,sensor, global positioning system(GPS) and system integration technology. The wireless network is a three-layer architecture and daisy chain topology network, which consists of control station, master robots and slave robots.Every three robots including one master robot and its two slave robots forms an ad hoc network. Master robots directly communicate with radio base station. Information will be sent to remote information center. Data sensing system including different kinds of sensors and desert robots is developed. A desert robot is designed and implemented as unmanned probing movable nodes and sensors' carrier. A new optical fiber sensor is exploited to measure vibration of sand in particular. The whole system, which is delivered to the testing field in hinterland of desert(25 km far from base station), has been proved efficient for data acquisition.展开更多
Segmented Active Constrained Layer Damping(SACLD)is an intelligent vibration-damping structure,which could be applied to the sectors of aviation,aerospace,and transportation engineering to reduce the vibration of flex...Segmented Active Constrained Layer Damping(SACLD)is an intelligent vibration-damping structure,which could be applied to the sectors of aviation,aerospace,and transportation engineering to reduce the vibration of flexible structures.Moreover,machine learning technology is widely used in the engineering field because of its efficient multi-objective optimization.The dynamic simulation of a rotational segmental flexible manipulator system is presented,in which enhanced active constrained layer damping is carried out,and the neural network model of Genetic Algorithm-Back Propagation(GA-BP)algorithm is investigated.Vibration suppression and structural optimization of the SACLD manipulator model are studied based on vibration mode and damping prediction.The modal responses of the SACLD manipulator model at rest and rotation are obtained.In addition,the four model indices are optimized using the GA-BP neural network:axial incision size,axial incision position,circumferential incision size,and circumferential incision position.Finally,the best model for vibration suppression is obtained.展开更多
This review considers unexpected destructive disasters involving fluid power plants, such as nuclear electric power plants and fluid power plants. It specifically addresses the possibility of fluid vibration induced i...This review considers unexpected destructive disasters involving fluid power plants, such as nuclear electric power plants and fluid power plants. It specifically addresses the possibility of fluid vibration induced in a pipeline network of such a plant. The authors investigate the flow oscillation induced within a T-junction for laminar steady flow at a Reynolds number less than 10<sup>3</sup> and clarify that there is a periodic fluid oscillation with a constant Strouhal number independent of several flow conditions. Generally, a nuclear electric power plant is constructed using straight pipes, elbows, and T-junctions. Indeed, a T-Junction is a basic fluid element of a pipeline network. The flow in a fluid power plant is turbulent. There are peculiar flow phenomena that occur at high Reynolds numbers, which are also seen in other flow situations;e.g., Kaman vortices are observed around a circular cylinder in low Reynolds numbers, around structures like bridges and downstream of islands in oceans. Although the flow situation of a T-junction and elbow in a fluid power plant, such as the fluid suddenly changing its flow direction is turbulent flow, the authors mention the possibility of the fluid-induced vibration of a pipeline network.展开更多
The appearance of flow instabilities like the blockage severity,impeller cut flaws,pitted cover plate flaws can cause to diminish the efficiency of centrifugal pump(CP),and may result in excessive vibration and noise,...The appearance of flow instabilities like the blockage severity,impeller cut flaws,pitted cover plate flaws can cause to diminish the efficiency of centrifugal pump(CP),and may result in excessive vibration and noise,and their failure may lead to the system imploding.To bridge the gap of downfall in the efficiency of CP,it is crucial that a system can be created to monitor the condition of the CP and must be maintained.The present work proposes at identifying and determining the severity of various blockage levels in the inlet pipe with three different kinds of pumps using three distinct sensors.One pump works faultlessly(healthy pump),another has cuts artificially made on the impeller blade,and the third has pits artificially created on the cover plate.The inlet pipe blockage mimics pump blockage which is made more severe step by step.As the blockage gets worse and the flow slows down,recirculation starts,causing vapor bubbles to form.Utilizing a mechanical modulating valve,the inlet flow area of the pipe is partitioned into six intervals(0%,16.7%,33.3%,50%,66.6%,and 83.33%)to replicate pump blockage.This obstruction directly influences vibrations,current line signals,and fluid dynamic pressure.To gather data across a spectrum of blockage levels and operational frequencies(30 Hz,35 Hz,40 Hz,45 Hz,50 Hz,55 Hz,and 60 Hz),a combination of a pressure transducer,accelerometer,and current probes were strategically employed in this investigation.Multiple sets of statistical features were extracted from the data,and through various algorithms,the most effective combined statistical feature set was determined.In this domain,the combination of standard deviation,mean,and entropy demonstrates superior performance compared to other features.This feature set was input into an ANN model,which is developed by optimizing parameters like hidden layer count,neurons,epochs and then the results of this investigation are then compared with existing literature.It has been noted that employing combinations of multiple sets of statistical features significantly improves the accuracy in identifying obstruction levels,often achieving near-perfect accuracy for various feature sets(nearly 100%across various combinations).In comparison to other SOTA methods,this approach achieves higher accuracy,ranging from 2.41%to 15.69%across different metrics.This study presents a method to classify inlet pipe blockages into various levels,enhancing maintenance prioritization and reducing downtime and repair costs,ensuring long-term equipment health and operational efficiency.The fault prediction methodology proves highly robust across various CP operating conditions.展开更多
文摘A method for reducing noise radiated from structures by vibration absorbers is presented. Since usual design method for the absorbers is invalid for noise reduction, the peaks of noise power in the frequency domain as cost functions are applied. Hence, the equations for obtaining optimal parameters of the absorbers become nonlinear expressions. To have the parameters, an accelerated neural network procedure has been presented. Numerical calculations have been carried out for a plate type cantilever beam with a large width, and experimental tests have been also performed for the same beam. It is clarified that the present method is valid for reducing noise radiated from structures. As for the usual design method for the absorbers, model analysis has been given, so the number of absorbers should be the same as that of the considered modes. While the nonlinear problem can be dealt with by the present method, there is no restriction on the number of absorbers or the model number.
基金supported by the China State Railway Group Co.,Ltd.Science and Technology Research and Development Program Project(Grant No.L2024G007)the Natural Science Foundation of Hunan Province(Grant No.2024JJ5427)+1 种基金the National Natural Science Foundation of China(Grant No.52478321,52078485)the Science and Technology Research and Development Program Project of China Railway Group Limited(Grant No.2021-Special-08,2022-Key-06&2023-Key-22).
文摘To enhance the efficiency of stochastic vibration analysis for the Train-Track-Bridge(TTB)coupled system,this paper proposes a prediction method based on a Genetic Algorithm-optimized Backpropagation(GA-BP)neural network.First,initial track irregularity samples and random parameter sets of the Vehicle-Bridge System(VBS)are generated using the stochastic harmonic function method.Then,the stochastic dynamic responses corresponding to the sample sets are calculated using a developed stochastic vibration analysis model of the TTB system.The track irregularity data and vehicle-bridge random parameters are used as input variables,while the corresponding stochastic responses serve as output variables for training the BP neural network to construct the prediction model.Subsequently,the Genetic Algorithm(GA)is applied to optimize the BP neural network by considering the randomness in excitation and parameters of the TTB system,improving model accuracy.After optimization,the trained GA-BP model enables rapid and accurate prediction of vehicle-bridge responses.To validate the proposed method,predictions of vehicle-bridge responses under varying train speeds are compared with numerical simulation results.The findings demonstrate that the proposed method offers notable advantages in predicting the stochastic vibration response of high-speed railway TTB coupled systems.
基金support of the National Natural Science Foundation of China (No. 51675022, 11702011)China Postdoctoral Science Foundation (No. 2017M610741)
文摘The work aims to provide a further investigation of the dynamic characteristics of an integral bladed disk（also called ‘blisk＇） with a Parallel Piezoelectric Network（PPN）. The PPN is constructed by parallelly interconnecting the piezoelectric patches distributed in the blisk. Two kinds of PPN are considered, namely mono-periodic PPN and bi-periodic PPN. The former has a piezoelectric patch in each sector, and the later has one patch every few sectors. The vibration suppression performance of both kinds of PPN has been studied through modal analysis, forced response analysis, and statistical analysis. The research results turn out that the PPN will only affect mechanical frequencies near the electrical frequency clusters slightly, and the bi-periodic PPN will make the nodal diameter spectrum of the modes more complex, but the amplitude corresponding to the new nodal diameter component is much smaller than that of the nodal diameter component corresponding to the mono-periodic system. The mechanical coupling between the blades and the disk plays an important role in the damping effect of the PPN, and it should be paid attention to in applications. The mono-periodic PPN can effectively suppress the amplitude magnification of the forced response induced by the mistuning of the blisk; meanwhile, it can mitigate the vibration localization of the mistuned electromechanical system. If piezoelectric patches are set only in part of the sectors, the bi-periodic PPN still has a vibration suppression ability, but the effect is related to the number and spatial distribution of the piezoelectric patches.
文摘Vibration monitoring by virtual sensing methods has been well developed for linear timeinvariant structures with limited sensors.However,few methods are proposed for Time-Varying(TV)structures which are inevitable in aerospace engineering.The core of vibration monitoring for TV structures is to describe the TV structural dynamic characteristics with accuracy and efficiency.This paper propose a new method using the Long Short-Term Memory(LSTM)networks for Continuously Variable Configuration Structures(CVCSs),which is an important subclass of TV structures.The configuration parameters are used to represent the time-varying dynamic characteristics by the‘‘freezing"method.The relationship between TV dynamic characteristics and vibration responses is established by LSTM,and can be generalized to estimate the responses with unknown TV processes benefiting from the time translation invariance of LSTM.A numerical example and a liquid-filled pipe experiment are used to test the performance of the proposed method.The results demonstrate that the proposed method can accurately estimate the unmeasured responses for CVCSs to reveal the actual characteristics in time-domain and modal-domain.Besides,the average one-step estimation time of responses is less than the sampling interval.Thus,the proposed method is promising to on-line estimate the important responses of TV structures.
文摘The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This paper develops a vibration isolation system of micro-manufacturing platform. The brains of many kinds of birds can isolate vibrations well, such as woodpecker’s brain. When a woodpecker pecks the wood at the speed as 1.6 times as the velocity of sound, its brain will tolerate the wallop 1 500 times of the weight of itself without any damage. The isolation mechanics and organic texture of woodpecker’s brain that has good isolation characteristics were studied. A structure model of vibration isolation system for the micro-manufacturing platform is established based on the bionics of the bird’s brain vibration isolation mechanism. In order to isolate effectively the high frequency vibrations from the ground, a rubber layer is used to isolate vibrations passively between the micro-manufacturing platform’s pedestal and the ground. This layer corresponds to the cartilage and muscles in the outer meninges of the bird’s brain. The active vibration isolation technique is adopted to isolate vibrations between the micro-manufacturing platform and the pedestal. Air springs are used as elastic components, which correspond to the interspaces between the outer meninges and the encephala of the bird’s brain. Actuators are made of giant magnetostrictive material, and it corresponds to the nerves and neural muscles linking the meninges and the encephala. The actuators and air springs are arranged vertically in parallel to make use of the giant magnetostrictive actuators effectively. The air springs support almost all weight of the micro-manufacturing platform and the giant magnetostrictive actuators support almost no weight. In order to realize high performance to isolate complex micro-vibration, the control method using a three-layer neural network is presented. This vibration control system takes into account the floor disturbance and the direct disturbance acting on the micro-manufacturing platform. The absolute acceleration of the micro-manufacturing platform is used as the performance index of vibration control. The performance of the control system is tested by numerical simulation. Simulation results show that the active vibration isolation system has good isolation performance against the floor disturbance and the direct disturbance acting on the micro-manufacturing platform in all the frequency range.
基金Project supported by the Key Project(No.60934001)the General Projects(Nos.51175181and90505014)of the National Natural Science Foundation of Chinaby the Fundamental Research Funds for the Central Universities,SCUT(No.2012ZZ0060)
文摘This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the dynamics model of the system. A back propagation neural network （BPNN） based proportional-derivative （PD） algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.
文摘Roller bearing failure is one of the most common faults in rotating machines.Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed.But feature extraction from fault signals requires expert prior information and human labour.Recently,deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data.Given its robust performance in image recognition,the convolutional neural network(CNN)architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions.This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis.The first stage in the proposed method is to generate the RGB vibration images(RGBVIs)from the input vibration signals.To begin this process,first,the 1-D vibration signals were converted to 2-D grayscale vibration Images.Once the conversion was completed,the regions of interest(ROI)were found in the converted 2-D grayscale vibration images.Finally,to produce vibration images with more discriminative characteristics,an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images(RGBVIs)with sets of colours and texture features.In the second stage,with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions.Two cases of fault classification of rolling element bearings are used to validate the proposed method.Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy.Moreover,several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy,precision,recall,and F-score.
基金This work was supported in part by the Key Research and Development Program of Zhejiang Province,China(Grant No.2021C05005)the National Natural Science Foundation of China(Grant No.81771880)the Tianjin Municipal Government of China(Grant No.19JCQNJC12800).
文摘Wearable flexible sensors attached on the neck have been developed to measure the vibration of vocal cords during speech.However,highfrequency attenuation caused by the frequency response of the flexible sensors and absorption of high-frequency sound by the skin are obstacles to the practical application of these sensors in speech capture based on bone conduction.In this paper,speech enhancement techniques for enhancing the intelligibility of sensor signals are developed and compared.Four kinds of speech enhancement algorithms based on a fully connected neural network(FCNN),a long short-term memory(LSTM),a bidirectional long short-term memory(BLSTM),and a convolutional-recurrent neural network(CRNN)are adopted to enhance the sensor signals,and their performance after deployment on four kinds of edge and cloud platforms is also investigated.Experimental results show that the BLSTM performs best in improving speech quality,but is poorest with regard to hardware deployment.It improves short-time objective intelligibility(STOI)by 0.18 to nearly 0.80,which corresponds to a good intelligibility level,but it introduces latency as well as being a large model.The CRNN,which improves STOI to about 0.75,ranks second among the four neural networks.It is also the only model that is able to achieves real-time processing with all four hardware platforms,demonstrating its great potential for deployment on mobile platforms.To the best of our knowledge,this is one of the first trials to systematically and specifically develop processing techniques for bone-conduction speed signals captured by flexible sensors.The results demonstrate the possibility of realizing a wearable lightweight speech collection system based on flexible vibration sensors and real-time speech enhancement to compensate for high-frequency attenuation.
基金This project was supported by National Natural Science Foundation of China
文摘This paper advances a new approach based on wavelet and wavelet packet transforms in tandem with a fuzzy cluster neural network,abbreviated WPFCNN.Wavelets and wavelet packets decompose a vibration signal into different bands at different levels and provides multiresolution or multiscale views of a signal which is stationary or nonstationary. Fuzzy mathematics processes uncertain problems in engineering and converts the attributes extracted by wavelet packets to fuzzy membership degree.To achieve self-organizing classification,the MAXNET neural network is employed.WPFCNN integrates the advantages of wavelet packets and fuzzy cluster with MAXNET.The approach is adopted to process and classify vibration signal of a NH_3 compressor in a petrochemical plant.The results indicate that it is a useful and effective intelligence classification in the field of condition monitoring and fault diagnosis.
文摘Magnetorheological (MR) dampers are one of the most promising new devices for civil infrastructural vibration control applications. However, due to their highly nonlinear dynamic behavior, it is very difficult to obtain of a mathematical model of inverse MR damper that has an explicit relationship between the desired damper force and the command signal (voltage). This force voltage relationship is especially required for the structural vibration control design and simulation using MR dampers. This paper focuses on using a neural network (NN) technique to emulate the inverse MR damper model. The output of the neural network can be used to command the MR damper for generating desired forces. Numerical simulations are also presented to illustrate the effectiveness of this inverse model in semi active vibration control using MR dampers.
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
基金This project is supported by National Natural Science Foundation of China (No.59975003).
文摘Combining with the characteristic of the fuzzy control and the neural networkcontrol(NNC), a new kind of the fuzzy neural network controller is proposed, and the synthesisdesign method of the control law and fast speed learning algorithm of the parameters of networks areput forward. The output of the controller is composed of two parts, part one is derived on basis ofthe principle of sliding control, the lower order model and the estimated parameters of the plantare only required, part two is derived on basis FNN, it is used to compensate the uncertainties ofthe systems. Because new type of FNN controller extracts from the advantages of the intelligentcontrol and model based sliding mode control, the numbers of adjusting parameters and the structureof FNN are simplified at large, and the practical significance and variation range are attached toeach layer of the network and its connected weights, the control performance and learning speed areincreased at large. The Tightness of the conclusions is verified by the experiment of anelectro-hydraulic position servo system of the mold of the continuous casting machinery.
文摘This study empirically tested if the personality trait of optimism and the interpersonal capability to generate optimism in one’s network nodes (i.e., alter-optimism) influences the social relationship patterns. The results provide evidence that optimism trait is independent from the way social networks of personal-issue sharing, advice-seeking, problem-solving, and innovation, are structured. In contrary, the alter-optimism capability does provide a good explanation of one’s social network position. Implications of these findings are discussed at the end.
文摘Monte Carlo Simulations(MCS),commonly used for reliability analysis,require a large amount of data points to obtain acceptable accuracy,even if the Subset Simulation with Importance Sampling(SS/IS)methods are used.The Second Order Reliability Method(SORM)has proved to be an excellent rapid tool in the stochastic analysis of laminated composite structures,when compared to the slower MCS techniques.However,SORM requires differentiating the performance function with respect to each of the random variables involved in the simulation.The most suitable approach to do this is to use a symbolic solver,which renders the simulations very slow,although still faster than MCS.Moreover,the inability to obtain the derivative of the performance function with respect to some parameters,such as ply thickness,limits the capabilities of the classical SORM.In this work,a Neural Network-Based Second Order Reliability Method(NNBSORM)is developed to replace the finite element algorithm in the stochastic analysis of laminated composite plates in free vibration.Because of the ability to obtain expressions for the first and second derivatives of the NN system outputs with respect to any of its inputs,such as material properties,ply thicknesses and orientation angles,the need for using a symbolic solver to calculate the derivatives of the performance function no longer exists.The proposed approach is accordingly much faster,and easily allows for the consideration of ply thickness uncertainty.The present analysis showed that dealing with ply thicknesses as random variables results in 37%increase in the laminate’s probability of failure.
基金supported by the National Natural Science Foundation of China (42027805)National Aeronautical Fund (ASFC-2017 2080005)National Key R&D Program of China (2017YFC03 07100)。
文摘Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry.The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions.Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery.The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features.In this paper, the efficacy and the leverage of a pre-trained convolutional neural network(CNN) is harnessed in the implementation of a robust fault classification model.In the absence of sufficient data, this method has a high-performance rate.Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier.The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly.The proposed approach is carried out on bearing vibration data and shows high-performance results.In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator(HI) under varying operating conditions for a given fault condition.
基金Supported by National Natural Science Foundation of China(Grant Nos.51875031,52242507)Beijing Municipal Natural Science Foundation of China(Grant No.3212010)Beijing Municipal Youth Backbone Personal Project of China(Grant No.2017000020124 G018).
文摘The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment.In engineering scenarios,co-frequency vibration faults are highlighted by rotational frequency and are difficult to identify,and existing intelligent methods require more hardware conditions and are exclusively time-consuming.Therefore,Lightweight-convolutional neural networks(LW-CNN)algorithm is proposed in this paper to achieve real-time fault diagnosis.The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method.Based on LW-CNN and data augmentation,the real-time intelligent diagnosis of co-frequency is realized.Moreover,a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis.It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.
基金International S&T Cooperation Program of China(No.2011DFA11780)
文摘The deep understanding on sand and sand dunes scale can be useful to reveal the formation mechanism of the sandstorm for early sandstorm forecast. The current sandstorm observation methods are mainly based on conventional meteorological station and satellites remote sensing, which are difficult to acquire sand scale information. A wireless sensing network is implemented in the hinterland of desert, which includes ad hoc network,sensor, global positioning system(GPS) and system integration technology. The wireless network is a three-layer architecture and daisy chain topology network, which consists of control station, master robots and slave robots.Every three robots including one master robot and its two slave robots forms an ad hoc network. Master robots directly communicate with radio base station. Information will be sent to remote information center. Data sensing system including different kinds of sensors and desert robots is developed. A desert robot is designed and implemented as unmanned probing movable nodes and sensors' carrier. A new optical fiber sensor is exploited to measure vibration of sand in particular. The whole system, which is delivered to the testing field in hinterland of desert(25 km far from base station), has been proved efficient for data acquisition.
基金This research was funded by the National Natural Science Foundation of China(Nos.12072159,12232012,and 12102191)the Fundamental Research Funds for the Central Universities,China(No.30922010314).
文摘Segmented Active Constrained Layer Damping(SACLD)is an intelligent vibration-damping structure,which could be applied to the sectors of aviation,aerospace,and transportation engineering to reduce the vibration of flexible structures.Moreover,machine learning technology is widely used in the engineering field because of its efficient multi-objective optimization.The dynamic simulation of a rotational segmental flexible manipulator system is presented,in which enhanced active constrained layer damping is carried out,and the neural network model of Genetic Algorithm-Back Propagation(GA-BP)algorithm is investigated.Vibration suppression and structural optimization of the SACLD manipulator model are studied based on vibration mode and damping prediction.The modal responses of the SACLD manipulator model at rest and rotation are obtained.In addition,the four model indices are optimized using the GA-BP neural network:axial incision size,axial incision position,circumferential incision size,and circumferential incision position.Finally,the best model for vibration suppression is obtained.
文摘This review considers unexpected destructive disasters involving fluid power plants, such as nuclear electric power plants and fluid power plants. It specifically addresses the possibility of fluid vibration induced in a pipeline network of such a plant. The authors investigate the flow oscillation induced within a T-junction for laminar steady flow at a Reynolds number less than 10<sup>3</sup> and clarify that there is a periodic fluid oscillation with a constant Strouhal number independent of several flow conditions. Generally, a nuclear electric power plant is constructed using straight pipes, elbows, and T-junctions. Indeed, a T-Junction is a basic fluid element of a pipeline network. The flow in a fluid power plant is turbulent. There are peculiar flow phenomena that occur at high Reynolds numbers, which are also seen in other flow situations;e.g., Kaman vortices are observed around a circular cylinder in low Reynolds numbers, around structures like bridges and downstream of islands in oceans. Although the flow situation of a T-junction and elbow in a fluid power plant, such as the fluid suddenly changing its flow direction is turbulent flow, the authors mention the possibility of the fluid-induced vibration of a pipeline network.
文摘The appearance of flow instabilities like the blockage severity,impeller cut flaws,pitted cover plate flaws can cause to diminish the efficiency of centrifugal pump(CP),and may result in excessive vibration and noise,and their failure may lead to the system imploding.To bridge the gap of downfall in the efficiency of CP,it is crucial that a system can be created to monitor the condition of the CP and must be maintained.The present work proposes at identifying and determining the severity of various blockage levels in the inlet pipe with three different kinds of pumps using three distinct sensors.One pump works faultlessly(healthy pump),another has cuts artificially made on the impeller blade,and the third has pits artificially created on the cover plate.The inlet pipe blockage mimics pump blockage which is made more severe step by step.As the blockage gets worse and the flow slows down,recirculation starts,causing vapor bubbles to form.Utilizing a mechanical modulating valve,the inlet flow area of the pipe is partitioned into six intervals(0%,16.7%,33.3%,50%,66.6%,and 83.33%)to replicate pump blockage.This obstruction directly influences vibrations,current line signals,and fluid dynamic pressure.To gather data across a spectrum of blockage levels and operational frequencies(30 Hz,35 Hz,40 Hz,45 Hz,50 Hz,55 Hz,and 60 Hz),a combination of a pressure transducer,accelerometer,and current probes were strategically employed in this investigation.Multiple sets of statistical features were extracted from the data,and through various algorithms,the most effective combined statistical feature set was determined.In this domain,the combination of standard deviation,mean,and entropy demonstrates superior performance compared to other features.This feature set was input into an ANN model,which is developed by optimizing parameters like hidden layer count,neurons,epochs and then the results of this investigation are then compared with existing literature.It has been noted that employing combinations of multiple sets of statistical features significantly improves the accuracy in identifying obstruction levels,often achieving near-perfect accuracy for various feature sets(nearly 100%across various combinations).In comparison to other SOTA methods,this approach achieves higher accuracy,ranging from 2.41%to 15.69%across different metrics.This study presents a method to classify inlet pipe blockages into various levels,enhancing maintenance prioritization and reducing downtime and repair costs,ensuring long-term equipment health and operational efficiency.The fault prediction methodology proves highly robust across various CP operating conditions.
