Showing papers in "Journal of Vibroengineering in 2016"

A rail-borne piezoelectric transducer for energy harvesting of railway vibration

Abstract: This paper investigates design, modelling, and test issues related to piezoelectric energy transducer. The model analyzes a rail-borne “seismic” energy harvester that is designed to generate electrical energy from local variations in rail acceleration. The energy harvester analyzed in this model consists of a piezoelectric PZT film clamped at one end to the rail with a tip mass mounted on its other end. It includes two sub-models in this paper: a vehicle-track interaction model considering vehicle travelling load; and a cantilevered piezoelectric beam model for the visualization of voltage and power profile and frequency response. Four rail irregularities (American 6th grade track spectrum, Chinese track spectrum, German high and low-disturbance track spectrum) are compared and implemented into the calculation script. The calculated results indicate a rail displacement of 0.2 mm to 0.8 mm. Vibration tests of the proposed rail-borne device are conducted; a hydraulic driven system with excitation force up to 140 kN is exploited to generate the realistic wheel-rail interaction force. The proposed rail-borne energy harvester is capable of energy harvesting at low-frequency (5 Hz to 7 Hz) and small railway vibration (0.2 mm to 0.4 mm rail displacement). The output power of 4.9 mW with a load impedance of 100 kOhm is achieved. The open circuit peak-peak voltage reaches 24.4 V at 0.2 mm/7 Hz/5 g wheel-rail excitation. A DC-DC buck converter is designed, which works at the resonance frequency of 23 Hz/5 g on a lab vibration rig, providing a 3.3 VDC output.

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

Abstract: A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity.

Novel fusion computing method for bio-medical image of WSN based on spherical coordinate

Abstract: In bio-medical field, embedded numerous sensing nodes can be used to monitor and interact with physical world based on signal analysis and processing Data from many different sources can be collected into massive data sets via localized sensor networks Understanding the environment requires collecting and analyzing data from thousands of sensors monitoring, this is big data environment The application of bio-medical image fusion for big-data computing has strong development momentum, big-data bio-medical image fusion is one of key problems, so the fusion method study is a hot topic in the field of signal analysis and processing The existing methods have many limitations, such as large delay, data redundancy, more energy cost, low quality, so novel fusion computing method based on spherical coordinate for big-data bio-medical image of WSN is proposed in this paper In this method, the three high-frequency coefficients in wavelet domain of bio-medical image are pre-processed This pre-processing strategy can reduce the redundant ratio of big-data bio-medical image Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm Finally, based on multi-scale edge of bio-medical image, it can be fused and reconstructed Experimental results indicate the novel method is effective and very useful for transmission of big-data bio-medical image, which can solve the problem of data redundancy, more energy cost and low quality

Combination of principal component analysis and time-frequency representations of multichannel vibration data for gearbox fault detection

Abstract: A multichannel vibration data processing method in the context of local damage detection in gearboxes is presented in this paper. The purpose of the approach is to achieve more reliable information about local damage by using several channels in comparison to results obtained by single channel vibration analysis. The method is a combination of time-frequency representation and Principal Component Analysis (PCA) applied not to the raw time series but to each slice (along the time) from its spectrogram. Finally, we create a new time-frequency map which aggregated clearly indicates presence of the damage. Details and properties of this procedure are described in this paper, along with comparison to single-channel results. We refer to autocorrelation function of the new aggregated time frequency map (1D signal) or simple spectrum (that might be somehow linked to classical envelope analysis). The results are very convincing – cyclic impulses associated with local damage might be clearly detected. In order to validate our method, we used a model of vibration data from heavy duty gearbox exploited in mining industry.

Data-driven vibration signal filtering procedure based on the α-stable distribution

Abstract: A novel procedure for data-driven enhancement of informative signal is presented in this paper The introduced methodology covers decomposition of the signal via time-frequency spectrogram into set of narrowband sub-signals Furthermore, each of the sub-signals is considered as a sample of independent identically distributed random variables and we model the distribution of the sample, in contrast to the classical methodology where the simple statistics, for example kurtosis, for each sub-signal was calculated This approach provides a new perspective in the signal processing techniques for local damage detection Using our methodology one can eliminate potential risk related to high sensitivity towards single outlier In the proposed procedure we model each sub-signal in time-frequency representation by α-stable distribution This distribution is a generalization of standard Gaussian one and allows us for modeling sub-signals related to both informative and non-informative frequencies As a result, we obtain distribution of stability parameter vs frequencies that is analogy to spectral kurtosis approach well known in the literature Such characteristic is basis for filter design used for raw signal enhancement To evaluate efficiency of our method we compare raw and filtered signal in time, time-frequency and frequency (envelope spectrum) domains Moreover, we present comparison to the spectral kurtosis approach The presented methodology we applied to simulated signal and real vibration signal from two stage heavy duty gearbox used in mining industry

Damage detection of pipeline multiple cracks using piezoceramic transducers

Abstract: To study the feasibility of detecting pipeline multi-cracks damage using piezoceramic transducers, the electromechanical impedance method and the stress wave based active sensing method were used respectively to perform the damage detection of pipeline with multi-cracks. In this research, the lead zirconate titanate (PZT) type transducers were used due to its strong piezoelectric effect and low cost. During the experiments, two artificial cracks on the pipeline specimen were created, ranging from 0 mm to 9 mm, and seven different operating conditions were generated for each artificial crack. In the monitoring test, for the electromechanical impedance method, the damage index based on Root Mean Square Deviation (RMSD) was used, and for the active sensing method, the damage index based on Wavelet Packet Energy Loss (WPEL) was used. In addition, the relationship between the crack depth and RMSD as well as the relationship between the crack depth and location and WPEL were analyzed. The results show that RMSD and WPEL indices increase with the increase of the depth of pipeline cracks. In addition, the WPEL index increases with the appearance of new cracks. Quantitative analysis of pipeline crack damage can be realized by electromechanical impedance method, and localization analysis on the pipeline multi-cracks damage can be achieved by stress wave method based on sensor arrays.

Rapid early damage detection using transmissibility with distance measure analysis under unknown excitation in long-term health monitoring

Abstract: Accurate and efficient damage detection in long-term health monitoring for structures still encounters many difficulties due to the effect of environment. Furthermore, recorded big data requires efficient damage detection algorithm. In this study, an efficient and effective damage detection algorithm is proposed using transmissibility along with Mahalanobis distance and Hotelling T-square. A numerically simulated beam and an experimentally tested laboratory structure are used to validate the proposed algorithm. Results demonstrate good performance of the proposed technique in damage detection.

Multiple damage detection and localization in beam-like and complex structures using co-ordinate modal assurance criterion combined with firefly and genetic algorithms

Abstract: Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm.

A review of command shaping techniques for elimination of residual vibrations in flexible-joint manipulators

Abstract: Command shaping is an important open-loop control method for improving the settling time and positioning accuracy. This technique also minimizes residual vibrations. Shaped command profiles are formed by convolving a sequence of impulses or solving special functions for the desired command signal. To determine the input shaper controller commands, estimated values of the system natural frequency and damping ratio are required to make the necessary calculations. However, real systems cannot be modelled precisely, while robustness of the shaper to modelling errors is an important design consideration. Many robust input shapers have been developed and reported in the literature. It has been observed that the robust shapers typically have longer travelling time durations that lead to slow system response. This makes a relationship between shaper rising/travelling time and robustness. This paper presents a review of command shaping methods and analyses the compromise between duration of motion and shaper robustness for positive and smoothly shaped reference commands.

Effect of temperature on the performance of a giant magnetostrictive ultrasonic transducer

Abstract: The effect of temperature on the performance of a giant magnetostrictive ultrasonic transducer (GMUT) was investigated by measuring variations in the resonance frequency and mechanical quality factor of the GMUT at different temperatures. The equivalent circuit model of the GMUT was presented and the total electrical impedance equation was obtained. Curves of the impedance circle were obtained at different temperatures to determine the resonance frequency and mechanical quality factor. To verify the impedance-based results and obtain precise values of the resonance frequency and effective frequency bandwidth, the amplitude-frequency response within the same temperature range was examined experimentally. These results were consistent with those of the impedance analysis, which demonstrates the validity of the equivalent circuit model. Moreover, the resonance frequency and effective bandwidth of the GMUT were found to decrease with increasing temperature, which means that the vibration amplitude is more sensitive to variation in the resonance frequency at high temperature owing, for example, to static or dynamic system loading, changes in the material properties, or drive-signal variability. Accordingly, the temperature in the GMUT should be precisely controlled to improve the stability of vibration.

Research on fault diagnosis of hydraulic pump using convolutional neural network

Abstract: The failure mechanism of hydraulic pump is complex, and its faulty features are frequently submerged in the nonlinear interference caused by various components. The fault diagnosis of hydraulic pump is a challenge in the field of machinery. The conventional fault diagnosis approaches have several drawbacks. First, the operator should be cognizant of the mechanism of hydraulic pump. Second, the procedure is onerous, and has many parameters to set. Third, the shallow classification is weak for this complex problem, which leads to low accuracy rate. This paper developed a new scheme by using improved convolutional neural network. It can be directly used without human intervention, although the operator knows little knowledge about hydraulic pump. Therefore, it is simple to be employed and easy for widely promotion. Validated by fault diagnosis cases of hydraulic pump, the proposed scheme is not only simple for application, but also is superior to other machine learning algorithms, especially when the pump speed varies.

Damage detection of concrete piles subject to typical damages using piezoceramic based passive sensing approach

Abstract: Pile foundations are typically comprised in concealed construction work. In recent years, some major categories of concrete piles subject to typical damages have caused a lot of engineering disasters and accidents. These accidents have been caused by collapse of civil structures resulting in great casualties and economic loss. Therefore, damage detection and real-time health monitoring on foundation piles is an urgent research requirement. In this research, a piezoceramic based passive sensing approach is proposed to detect typical damages types of concrete piles, including partial mud intrusion, secondary concrete pouring interface, circumferential crack, and full mud intrusion. In this passive sensing approach, induced stress waves are generated by the impact hammer on the top surface of a pile and one smart aggregate embedded on the bottom of each pile is used as a sensor to receive the propagating wave signals. These sensors are embedded before pouring concrete. Structural defects affect the natural frequency of the pile. The power spectrum of piles with different types of damage were compared by plotting the sensor signals in frequency domain. The natural frequency decreases with the increase in defect severity. The experimental results demonstrate that the proposed approach can detect all four typical damage types in concrete piles.

Investigating the influence of computational model complexity on noise and vibration modeling of powertrain

Abstract: The paper presents advanced computational models suitable for the development of a modern powertrain in the field of noise and vibration. The aim is to decide how detailed the model should be to correctly describe the vibrational and acoustic performance of the powertrain. In general, the advanced computational model of the powertrain – a virtual powertrain – is developed as a powerful tool for the solution of structural and also thermal and fatigue problems. Main results from the field of vibrations are verified by technical experiments using laser vibration technique and strain gauges. Afterwards, the simpler computational models are compared with the virtual powertrain and the results are discussed. The virtual powertrain is assembled, as well as numerically solved, in Multi Body System extended by user written subroutines. The virtual engine results are validated by measurements performed on compression ignition in-line six-cylinder engine.

A comparative study of signal processing methods for structural health monitoring

Abstract: In this paper four non-parametric and five parametric signal processing techniques are reviewed and their performances are compared through application to a sample exponentially damped synthetic signal with closely-spaced frequencies representing the ambient response of structures. The non-parametric methods are Fourier transform, periodogram estimate of power spectral density, wavelet transform, and empirical mode decomposition with Hilbert spectral analysis (Hilbert-Huang transform). The parametric methods are pseudospectrum estimate using the multiple signal categorization (MUSIC), empirical wavelet transform, approximate Prony method, matrix pencil method, and the estimation of signal parameters by rotational invariance technique (ESPRIT) method. The performances of different methods are studied statistically using the Monte Carlo simulation and the results are presented in terms of average errors of multiple sample analyses.

Vibroacoustic diagnostics of a radial microturbine and a scroll expander operating in the organic Rankine cycle installation

Abstract: The article presents the results of vibroacoustic research on a prototypical 4-stage radial microturbine and a scroll expander operating in the organic Rankine cycle with the low-boiling fluid HFE7100. The high-speed microturbogenerator had the electrical capacity of 3 kWe at the nominal speed of 24000 rpm. The low-speed expander with a capacity of 1 kWe and a nominal speed of 3600 rpm was made by Air Squared. The frequency characteristics and overall vibration level (vibration velocity Vrms) measurements were conducted for both the microturbine and the expander, depending on the rotational speed and on the power consumption of electrical energy receivers. The level of noise emitted by the microturbine and expander was also determined. The research was carried out for various electrical loads of the expansion devices generators running in the ORC system. The devices were tested in the following electric power ranges: from 550 We to 1150 We (scroll expander) and from 800 We to 1800 We (radial microturbine). Based on the obtained results, dynamic state assessment of the tested machines was performed and their noise and vibration levels were analysed.

Tuning of LQR controller for an experimental inverted pendulum system based on The Bees Algorithm

Abstract: Stabilizing of an inverted pendulum (IP) system is a main problem for researchers working on control theory. Balancing of an inverted pendulum system is one of the major benchmark problems in the control system community. This paper presents optimal tuning of linear quadratic regulator (LQR) controller with The Bees Algorithm (BA) for a linear inverted pendulum. In this paper, a metaheuristic approach which is a nature-inspired search method that mimics the foraging behavior of honey bees is used for design of LQR controller to obtain optimal performance. In LQR controller design, state (Q) and control (R) weighting matrices are basic parameters of LQR which are tuning by designer using trial and error method in usually. The Bees Algorithm optimizes the weighting matrices of the LQR controller so that it can move the cart to a desired position with the minimum change in pendulum’s angle from vertically upright position during the movement. The tuned LQR controller is benchmarked on the linear inverted pendulum experimental device (IP02) that is manufactured by QUANSER Company. After description of the system and The Bees Algorithm, the paper gives the experimental results obtained from the IP02 system to demonstrating the efficiency of the tuning of the LQR controller. Simulation and experimental results are given graphically to show the success of controller. As a result of the paper, the performance of LQR controller shows the effectiveness of The Bees Algorithm which is a diversity method for provide an efficient solution to conventional trial and error design approach.

Fault diagnosis of gearboxes using wavelet support vector machine, least square support vector machine and wavelet packet transform

Abstract: This work focuses on a method which experimentally recognizes faults of gearboxes using wavelet packet and two support vector machine models. Two wavelet selection criteria are used. Some statistical features of wavelet packet coefficients of vibration signals are selected. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Eventually, the gearbox faults are classified using these statistical features as input to least square support vector machine (LSSVM) and wavelet support vector machine (WSVM). Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of fault classification demonstrate that the WSVM identified the fault categories of gearbox more accurately and has a better diagnosis performance as compared to the LSSVM.

Dynamic characteristics of rotor system and rub-impact fault feature research based on casing acceleration

Abstract: Aimed at the vibration of whole aero-engine, a coupled dynamic model of rotor-ball bearing-stator of aero-engine is built. By means of the lumped mass method, taking into account the nonlinear rub-impact, bearing failure force and deformation of the casing, the dynamic equation of the system containing typical rub-impact is derived. The response of the system under different conditions is obtained by using the fourth order Runge-Kutta numerical integration algorithm. By adopting the finite element analysis software ANSYS, the finite element model of the rotor shaft is established and the first six-order natural frequencies of the rotor system are acquired. Taking advantage of the parameters of the signal in time domain and frequency domain, frequency characteristics are extracted as the fault features. The single-point rubbing experiment is carried out in the test rig, and the working speed is higher than the first critical speed, so the rotor shaft is flexible rotor. By the methods of spectrum and cepstrum analysis, the rub-impact characteristics of the casing vibration acceleration time series data are analyzed. The results show that the casing vibration acceleration has obvious impact characteristics; the impact frequency is equal to the product of rotating frequency and number of the blades; the impact frequency component and its multiple-frequencies are demonstrated in the frequency spectrum; the strength of impact is modulated by the rotating frequency, so that there are families of side bands on impact frequency and both sides of frequency doubling, and the interval of sideband equals the rotating frequency. The frequency components of the rotating frequency and its frequency doubling are clearly shown in the cepstrum. By comparing the simulation and experiment, the rubbing characteristics found out in this paper has enough accuracy.

A hybrid artificial neural network with dempster-shafer theory for automated bearing fault diagnosis

Abstract: Bearing fault diagnosis has a pivotal role in condition-based maintenance. Vibration spectra analysis has been proven to be the most efficient method for rotating machinery fault diagnosis. Vibration spectra can be analyzed by various signal processing tools (e.g. wavelet analysis, empirical mode decomposition, Hilbert-Huang transform). However, they involve human expertise in ensuring its maximum success. Machine learning tools (e.g. artificial neural networks (ANN), support vector machines (SVM)) can be an alternative for an automatic fault diagnosis. Researchers have studied the feasibility of ANN for automatic fault diagnosis since last decades. Most of the researchers reported positive finding in adapting ANN for automatic fault diagnosis. However, its accuracy is highly dependent on the neural networks structure such as number of nodes, hidden layers, and sigmoid function. This study proposed a hybrid algorithm used for automated bearing fault diagnosis based on ANN and Dempster-Shafer (DS) theory. The hybrid algorithm employed DS theory to improve the fault diagnosis results from ANN by eliminating conflicting results generated by ANN. Four conditions of bearing namely healthy condition and three types of faults included ball, inner race, and outer race faults classify by the proposed hybrid algorithm and artificial neural networks. The superiority of the hybrid algorithm was shown by comparing its result with the performance of ANN alone.

A unified solution for free vibration of orthotropic circular, annular and sector plates with general boundary conditions

Abstract: This paper adopts a modified Fourier-Ritz approach to study the free vibration characteristics of orthotropic circular, annular and sector thin plates subjected to general boundary conditions. For the arbitrary plate forms and the boundary conditions, the displacements can be written in the form of a standard Fourier cosine series supplemented with several auxiliary functions. The auxiliary functions, which are closed-form and introduced to remove all the potential discontinuities of the original displacement function and its derivatives in the whole domain, can be usefully employed in improving the convergence of the results. The artificial boundary spring technique and artificial coupling spring technique are adopted to simulate the arbitrary boundary conditions and to ensure appropriate continuity conditions along the radial edges, respectively. Because the displacement field is sufficiently smooth in the whole solution domain, the accurate solution can be obtained by using the Ritz procedure on the basis of the energy functions. The accuracy, reliability and versatility of the current method are fully demonstrated and verified through numerical examples involving plates with various shapes and boundary conditions.

Study on an engineering measure to improve internal explosion resistance capacity of segmental tunnel lining structures

Abstract: With the increase of terrorist bombing attacks on subway systems, few research results could be traced on the internal explosion capacity of segmental tunnel linings. This paper presented some full-scale test results of segmental tunnel linings under internal explosions, and the deformation and failure patterns of segmental tunnel lining were analyzed and outlined, wherein the key factors and positions that dominate the damage of a segmental lining were figured out. Then based on a conceptual idea, attempts were made, by adding flexible damping cushions on the joints, to relieve the damage degree of contact area of bolts. At last, numerical simulations were performed and it was shown that, the localized failures of joint areas of tunnel segments could be relieved effectively after introduction of this measure, so the internal explosion resistance performance of segmental lining structures could be optimized and hence improved.

Gearbox fault diagnosis based on local mean decomposition, permutation entropy and extreme learning machine

Abstract: This paper presents a fault diagnosis method for gearbox based on local mean decomposition (LMD), permutation entropy (PE) and extreme learning machine (ELM). LMD, a new self-adaptive time-frequency analysis method, is applied to decompose the vibration signal into a set of product functions (PFs). Then, PE values of the first five PFs (PF-PE) are calculated to characterize the complexity of the vibration signal. Finally, for the purpose of less time-consuming and higher accuracy, ELM is used to identify and classify of gearbox in different fault types. The experimental results demonstrate that the proposed method is effective in diagnosing and classifying different states of gearbox in short time.

New mixed adaptive detection algorithm for moving target with big data

Abstract: Aiming at the troubles (such as complex background, illumination changes, shadows and others on traditional methods) for detecting of a walking person, we put forward a new adaptive detection algorithm through mixing Gaussian Mixture Model (GMM), edge detection algorithm and continuous frame difference algorithm in this paper. In time domain, the new algorithm uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection algorithm which mixes the edge detection algorithm, continuous frame difference algorithm and GMM to get the initial contour of moving target with big data, and gets the ultimate moving target with big data. This algorithm not only can adapt to the illumination gradients and background disturbance occurred on scene, but also can solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional algorithm. As experimental result showing, this algorithm holds better real-time and robustness. It is not only easily implemented, but also can accurately detect the moving target with big data.

A unified solution for free vibration of orthotropic annular sector thin plates with general boundary conditions, internal radial line and circumferential arc supports

Abstract: In this paper, a modified Fourier-Ritz approach is adopted to analyze the free vibration of orthotropic annular sector thin plates with general boundary conditions, internal radial line and circumferential arc supports. In the present method, regardless of boundary conditions, the displacements of the sector plates are invariantly expressed as a standard Fourier cosine series and several auxiliary closed-form functions. These auxiliary functions are introduced to eliminate any potential discontinuities of the original displacement function and its derivatives throughout the whole domain including its edges, and then to effectively enhance the convergence of the results. Since the displacement field is constructed to be adequately smooth in the whole solution domain, an accurate solution can be obtained by using Ritz procedure based on the energy functions of the sector plates. The excellent accuracy and reliability of the current solutions are compared with the results found in the literature, and numerous new results for annular sector plates with various boundary conditions are presented. New results are obtained for annular sector plates subjected to elastic boundary restraints and arbitrary internal radial line and circumferential arc supports in both directions, and they may be served as benchmark solutions for future researches.

Fuzzy control of active suspension system

Abstract: The main objective of this paper is to investigate the performance of active suspension system, using suspension deflection of the vehicle body as the principal criterion of control and fuzzy-logic as the control scheme. This work describes the application of fuzzy logic technique to the control of a continuously damping automotive suspension system. Active suspension systems are multivariable dynamic systems for which it is difficult to derive mathematical models. Therefore, analytical control schemes based on such models are complex to construct and generally do not perform well in practice. Hence intelligent control schemes like fuzzy logic controllers that can control the un modelled part of the suspension dynamics are simple to realize and can yield accurate control. This paper has described a proposed fuzzy control scheme for suspensions of the vehicle, because of its inherent ability to represent dynamics, the controller is easy to adapt for control tasks. The paper also describes the model and controller used in the study and discusses the vehicle response results obtained from a range of road input simulations. The simulation results obtained have confirmed the feasibility of the proposed fuzzy control scheme in Active suspension system.

Optimized modeling and experiment test of a fluid inerter

Abstract: This paper presents the fluid structure of the third passive vibration isolation element inerter. The fluid inerter ideally has the same characteristic that the force applying to the two terminals is proportional to the relative acceleration as the ball-screw inerter and rack-and-pinion inerter. An optimized nonlinear model of the fluid inerter is introduced, and the effect of nonlinearities compromising friction, oil density and viscosity of the fluid are discussed and analyzed. Simulations show that the friction has a great effect on the dynamic performance of fluid inerter in low frequency and the influence of the viscosity is not negligible. The damping force and the inertia force will become larger with the increase of the frequency and the inertia force will become more and more apparent in higher frequency. Furthermore, experiments are carried out to test the effectiveness of the fluid inerter. Results show that the optimized nonlinear model of the fluid inerter is deemed effective.

A unified solution for the in-plane vibration analysis of multi-span curved Timoshenko beams with general elastic boundary and coupling conditions

Abstract: A unified solution for the in-plane vibration analysis of multi-span curved Timoshenko beams with general elastic boundary and coupling conditions by combining with the improved Fourier series method and Rayleigh-Ritz technique is presented in this paper. Under the current framework, regardless of boundary conditions, each of displacements and rotations of the curved Timoshenko beams is represented by the modified Fourier series consisting of a standard Fourier cosine series and several closed-form auxiliary functions introduced to ensure and accelerate the convergence of the series representation. All the expansion coefficients are determined by the Rayleigh-Ritz technique as the generalized coordinates. The convergence and accuracy of the present method are tested and validated by a lot of numerical examples for multi-span curved Timoshenko beams with various boundary restraints and general elastic coupling conditions. In contrast to most existing methods, the current method can be universally applicable to general boundary conditions and elastic coupling conditions without the need of making any change to the solution procedure.

Vibration analysis of a single-layered graphene sheet embedded in visco-Pasternak’s medium using nonlocal elasticity theory

Abstract: The vibration analysis of a single-layered graphene sheet (SLGS) embedded in viscoelastic medium is presented by using the nonlocal elasticity theory. The medium is considered by adding the damping to the usual foundation model which characterized by the linear Winkler’s modulus and Pasternak’s (shear) foundation modulus. The governing dynamical equation is obtained and solved for simply-supported SLGSs. The effects of many parameters like nonlocal parameter, aspect ratio, Winkler-Pasternak’s foundation, damping coefficient, and mode numbers on the vibration frequencies of the SLGSs are investigated in detail. The present results are compared with the corresponding available in the literature. Additional results are tabulated and plotted for sensing the effect of all used parameters and to investigate the visco-Pasternak’s parameters for future comparisons.

Rolling bearing fault diagnosis by a novel fruit fly optimization algorithm optimized support vector machine

Abstract: Based on the nonlinear and non-stationary characteristics of rotating machinery vibration, a FOA-SVM model is established by Fruit Fly Optimization Algorithm (FOA) and combining the Support Vector Machine (SVM) to realize the optimization of the SVM parameters. The mechanism of this model is imitating the foraging behavior of fruit flies. The smell concentration judgment value of the forage is used as the parameter to construct a proper fitness function in order to search the optimal SVM parameters. The FOA algorithm is proved to be convergence fast and accurately with global searching ability by optimizing the analog signal of rotating machinery fault. In order to improve the classification accuracy rate, built FOA-SVM model, and then to extract feature value for training and testing, so that it can recognize the fault rolling bearing and the degree of it. Analyze and diagnose actual signals, it prove the validity of the method, and the improved method had a good prospect for its application in rolling bearing diagnosis.

Dual-loop PID control with PSO algorithm for the active suspension of the electric vehicle driven by in-wheel motor

Abstract: Using the active suspension system of an electric vehicle driven by two rear in-wheel motors as the research object, a 14-degree of freedom coupled vehicle dynamic model is established. Based on the model, a dual-loop proportion integration differentiation controller based on the particle swarm algorithm is designed to control the active suspension in this paper. The designed controller can not only ease the vibration of the vehicle body from the road surface roughness and the unbalanced electromagnetic force but also can improve the ride comfort of the vehicle. To further verify the effectiveness of the control method, the control effect of the active suspension controller designed in this paper is compared with that of a passive suspension and a dual-loop proportion integration differentiation controller without the particle swarm algorithm. The results show that the vertical vibration acceleration, the roller angle and the pitch angle of the vehicle body are significantly improved with the dual-loop proportion integration differentiation controller based on the particle swarm algorithm. Compared with the passive suspension and the dual-loop proportion integration differentiation controller without the particle swarm algorithm, the improvement ratio of the vertical vibration acceleration is 20.92 % and 11.93 %, respectively; the roll angle improvement ratio can reach 57.23 % and 22.02 %, respectively; and the improvement ratio of the pitch angle is 30.23 % and 18.94 %, respectively. The comparison results show that the dual-loop proportion integration differentiation controller optimized with the particle swarm algorithm can better improve the ride comfort of the vehicle.