Showing papers in "Shock and Vibration in 2016"

Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

Abstract: The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.

A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition

Abstract: Variational mode decomposition (VMD) is a new method of signal adaptive decomposition. In the VMD framework, the vibration signal is decomposed into multiple mode components by Wiener filtering in Fourier domain, and the center frequency of each mode component is updated as the center of gravity of the mode’s power spectrum. Therefore, each decomposed mode is compact around a center pulsation and has a limited bandwidth. In view of the situation that the penalty parameter and the number of components affect the decomposition effect in VMD algorithm, a novel method of fault feature extraction based on the combination of VMD and particle swarm optimization (PSO) algorithm is proposed. In this paper, the numerical simulation and the measured fault signals of the rolling bearing experiment system are analyzed by the proposed method. The results indicate that the proposed method is much more robust to sampling and noise. Additionally, the proposed method has an advantage over the EMD in complicated signal decomposition and can be utilized as a potential method in extracting the faint fault information of rolling bearings compared with the common method of envelope spectrum analysis.

Vibration Response Characteristics of the Cross Tunnel Structure

Abstract: It is well known that the tunnel structure will lose its function under the long-term repeated function of the vibration effect. A prime example is the Xi’an cross tunnel structure (CTS) of Metro Line 2 and the Yongningmen tunnel, where the vibration response of the tunnel vehicle load and metro train load to the structure of shield tunnel was analyzed by applying the three-dimensional (3D) dynamic finite element model. The effect of the train running was simulated by applying the time-history curves of vibration force of the track induced by wheel axles, using the fitted formulas for vehicle and train vibration load. The characteristics and the spreading rules of vibration response of metro tunnel structure were researched from the perspectives of acceleration, velocity, displacement, and stress. It was found that vehicle load only affects the metro tunnel within 14 m from the centre, and the influence decreases gradually from vault to spandrel, haunch, and springing. The high-speed driving effect of the train can be divided into the close period, the rising period, the stable period, the declining period, and the leaving period. The stress at haunch should be carefully considered. The research results presented for this case study provide theoretical support for the safety of vibration response of Metro Line 2 structure.

Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

Abstract: Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.

Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

Abstract: In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN). By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.

Interaction between Walking Humans and Structures in Vertical Direction: A Literature Review

Abstract: Realistic simulation of the dynamic effects of walking pedestrians on structures is still a considerable challenge. This is mainly due to the inter- and intrasubject variability of humans and their bodies and difficult-to-predict loading scenarios, including multipedestrian walking traffic and unknown human-structure interaction (HSI) mechanisms. Over the past three decades, several attempts have been made to simulate walking HSI in the lateral direction. However, research into the mechanisms of this interaction in the vertical direction, despite its higher likelihood and critical importance, is fragmented and incoherent. It is, therefore, difficult to apply and codify. This paper critically reviews the efforts to date to simulate walking HSI in the vertical direction and highlights the key areas that need further investigation.

A Review of the Evaluation, Control, and Application Technologies for Drill String Vibrations and Shocks in Oil and Gas Well

Abstract: Drill string vibrations and shocks (V&S) can limit the optimization of drilling performance, which is a key problem for trajectory optimizing, wellbore design, increasing drill tools life, rate of penetration, and intelligent drilling. The directional wells and other special trajectory drilling technologies are often used in deep water, deep well, hard rock, and brittle shale formations. In drilling these complex wells, the cost caused by V&S increases. According to past theories, indoor experiments, and field studies, the relations among ten kinds of V&S, which contain basic forms, response frequency, and amplitude, are summarized and discussed. Two evaluation methods are compared systematically, such as theoretical and measurement methods. Typical vibration measurement tools are investigated and discussed. The control technologies for drill string V&S are divided into passive control, active control, and semiactive control. Key methods for and critical equipment of three control types are compared. Based on the past development, a controlling program of drill string V&S is devised. Application technologies of the drill string V&S are discussed, such as improving the rate of penetration, controlling borehole trajectory, finding source of seismic while drilling, and reducing the friction of drill string. Related discussions and recommendations for evaluating, controlling, and applying the drill string V&S are made.

Review of Energy Harvesters Utilizing Bridge Vibrations

Abstract: For health monitoring of bridges, wireless acceleration sensor nodes (WASNs) are normally used. In bridge environment, several forms of energy are available for operating WASNs that include wind, solar, acoustic, and vibration energy. However, only bridge vibration has the tendency to be utilized for embedded WASNs application in bridge structures. This paper reports on the recent advancements in the area of vibration energy harvesters (VEHs) utilizing bridge oscillations. The bridge vibration is narrowband (1 to 40 Hz) with low acceleration levels (0.01 to 3.8 g). For utilization of bridge vibration, electromagnetic based vibration energy harvesters (EM-VEHs) and piezoelectric based vibration energy harvesters (PE-VEHs) have been developed. The power generation of the reported EM-VEHs is in the range from 0.7 to 1450000 μW. However, the power production by the developed PE-VEHs ranges from 0.6 to 7700 μW. The overall size of most of the bridge VEHs is quite comparable and is in mesoscale. The resonant frequencies of EM-VEHs are on the lower side (0.13 to 27 Hz) in comparison to PE-VEHs (1 to 120 Hz). The power densities reported for these bridge VEHs range from 0.01 to 9539.5 μW/cm3 and are quite enough to operate most of the commercial WASNs.

A Study of Rockburst Hazard Evaluation Method in Coal Mine

Abstract: With the increasing of coal mining depth, the mining conditions are deteriorating, and dynamic hazard is becoming more likely to happen. This paper analyzes the relations and differences between rockburst in the coal mine and rockburst in the metal mine. It divides coal mine rockburst into two types including static loading type during roadway excavation process and dynamic loading type during mining face advancing. It proposes the correlation between the formation process of rockburst and the evolution of overlying strata spatial structure of the stope, criterion of rockburst occurrence, new classification, and predictive evaluation method for rockburst hazard that rockburst damage evaluation (RDE) = released energy capacity (REC)/absorbed energy capacity (AEC). Based on the relationship between RDE value and its corresponding level of rockburst hazard, the rockburst hazard can be divided into five types and evaluation index of each type can be achieved. Then the ongoing rockburst damage level can be classified in one of the five types, and the relative parameters, such as hazard extent, controlling measures also can be achieved. This new quantitative method could not only assess the impacting direction of rockburst occurrence, but also verify the effect of preventive measures for rockburst.

Boundary Conditions in 2D Numerical and 3D Exact Models for Cylindrical Bending Analysis of Functionally Graded Structures

Abstract: The cylindrical bending condition for structural models is very common in the literature because it allows an incisive and simple verification of the proposed plate and shell models. In the present paper, 2D numerical approaches (the Generalized Differential Quadrature (GDQ) and the finite element (FE) methods) are compared with an exact 3D shell solution in the case of free vibrations of functionally graded material (FGM) plates and shells. The first 18 vibration modes carried out through the 3D exact model are compared with the frequencies obtained via the 2D numerical models. All the 18 frequencies obtained via the 3D exact model are computed when the structures have simply supported boundary conditions for all the edges. If the same boundary conditions are used in the 2D numerical models, some modes are missed. Some of these missed modes can be obtained modifying the boundary conditions imposing free edges through the direction perpendicular to the direction of cylindrical bending. However, some modes cannot be calculated via the 2D numerical models even when the boundary conditions are modified because the cylindrical bending requirements cannot be imposed for numerical solutions in the curvilinear edges by definition. These features are investigated in the present paper for different geometries (plates, cylinders, and cylindrical shells), types of FGM law, lamination sequences, and thickness ratios.

Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain

Abstract: Gearboxes and induction motors are important components in industrial applications and their monitoring condition is critical in the industrial sector so as to reduce costs and maintenance downtimes. There are several techniques associated with the fault diagnosis in rotating machinery; however, vibration and stator currents analysis are commonly used due to their proven reliability. Indeed, vibration and current analysis provide fault condition information by means of the fault-related spectral component identification. This work presents a methodology based on vibration and current analysis for the diagnosis of wear in a gearbox and the detection of bearing defect in an induction motor both linked to the same kinematic chain; besides, the location of the fault-related components for analysis is supported by the corresponding theoretical models. The theoretical models are based on calculation of characteristic gearbox and bearings fault frequencies, in order to locate the spectral components of the faults. In this work, the influence of vibrations over the system is observed by performing motor current signal analysis to detect the presence of faults. The obtained results show the feasibility of detecting multiple faults in a kinematic chain, making the proposed methodology suitable to be used in the application of industrial machinery diagnosis.

Numerical Study on Pounding between Two Adjacent Buildings under Earthquake Excitation

Abstract: Seismic excitation, which results in large horizontal relative displacements, may cause collisions between two adjacent structures due to insufficient separation distance between them. Such collisions, known as earthquake-induced structural pounding, may induce severe damage. In this paper, the case of pounding between two adjacent buildings is studied by the application of single degree-of-freedom structural models. Impact is numerically simulated with the use of a nonlinear viscoelastic model. Special attention is focused on calculating values of impact forces during collisions which have significant influence of pounding-involved response under ground motions. The results of the study indicate that the impact force time history is much dependent on the earthquake excitation analyzed. Moreover, the peak impact forces during collision depend substantially on such parameters as gap size, coefficient of restitution, impact velocity, and stiffness of impact spring element. The nonlinear viscoelastic model of impact force with the considered relation between the damping coefficient and the coefficient of restitution has also been found to be effective in simulating earthquake-induced structural pounding.

Damage Detection and Localization from Dense Network of Strain Sensors

Abstract: Structural health monitoring of large systems is a complex engineering task due to important practical issues. When dealing with large structures, damage diagnosis, localization, and prognosis necessitate a large number of sensors, which is a nontrivial task due to the lack of scalability of traditional sensing technologies. In order to address this challenge, the authors have recently proposed a novel sensing solution consisting of a low-cost soft elastomeric capacitor that transduces surface strains into measurable changes in capacitance. This paper demonstrates the potential of this technology for damage detection, localization, and prognosis when utilized in dense network configurations over large surfaces. A wind turbine blade is adopted as a case study, and numerical simulations demonstrate the effectiveness of a data-driven algorithm relying on distributed strain data in evidencing the presence and location of damage, and sequentially ranking its severity. Numerical results further show that the soft elastomeric capacitor may outperform traditional strain sensors in damage identification as it provides additive strain measurements without any preferential direction. Finally, simulation with reconstruction of measurements from missing or malfunctioning sensors using the concepts of virtual sensors and Kriging demonstrates the robustness of the proposed condition assessment methodology for sparser or malfunctioning grids.

A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

Abstract: A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs) by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE) is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.

Design, Analysis, and Experimental Evaluation of a Double Coil Magnetorheological Fluid Damper

Abstract: A magnetorheological (MR) damper is one of the most advanced devices used in a semiactive control system to mitigate unwanted vibration because the damping force can be controlled by changing the viscosity of the internal magnetorheological (MR) fluids. This study proposes a typical double coil MR damper where the damping force and dynamic range were derived from a quasistatic model based on the Bingham model of MR fluid. A finite element model was built to study the performance of this double coil MR damper by investigating seven different piston configurations, including the numbers and shapes of their chamfered ends. The objective function of an optimization problem was proposed and then an optimization procedure was constructed using the ANSYS parametric design language (APDL) to obtain the optimal damping performance of a double coil MR damper. Furthermore, experimental tests were also carried out, and the effects of the same direction and reverse direction of the currents on the damping forces were also analyzed. The relevant results of this analysis can easily be extended to the design of other types of MR dampers.

Shaking Table Tests on Earthquake Response Characterization of a Complex Museum Isolated Structure in High Intensity Area

Abstract: Owing to special functional requirements of museum, such as great space and story height for exhibitions, large floor slab openings in plan and long span truss in elevation are becoming increasingly considered in museum design, which leads to challenges to structural safety. The aseismic performance of an isolated museum structure in high earthquake intensity regions was thus studied because of its complexity and irregularity. In order to observe the seismic characteristics and verify isolation effect, shaking table tests of a 1/30-scale structural model with and without base isolation bearings have been carried out under minor, moderate, and major earthquakes. The experimental results show that isolated structure dynamic characteristics and isolation effect are stable and storey peak acceleration responses of superstructure are less than that of fixed structure. Storey drifts of isolated structure meet required limits stipulated in Chinese design code and torsion responses of the bearings are not remarkable. It is suggested that seismic performances of complex museum structures have been effectively improved with isolation in use.

A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction

Abstract: Axle box bearings are the most critical mechanical components of railway vehicles. Condition monitoring is of great benefit to ensure the healthy status of bearings in the railway train. In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM) using vibration signals is proposed which is conducted in three main steps. Firstly, fast nonlocal means is used for denoising and ensemble empirical mode decomposition is applied to extract fault feature information. Then a new statistical method of feature extraction, symmetric alpha-stable distribution, is employed to obtain representative features from intrinsic mode functions. Additionally, the hybrid fault feature sets are input into LS-SVM to identify the fault type. To enhance the performance of LS-SVM in the case of small-scale samples, Morlet wavelet kernel function is combined with LS-SVM for the classification of fault type and fault severity and the particle swarm optimization is used for the optimization of LS-WSVM parameters. Finally, the experimental results demonstrate that the proposed approach performs more effectively and robustly than the other methods in small-scale samples for fault detection and classification of railway vehicle bearings.

A Novel Method for Adaptive Multiresonance Bands Detection Based on VMD and Using MTEO to Enhance Rolling Element Bearing Fault Diagnosis

Abstract: Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. Specifically, the method is conducted by the following three steps. First, the VMD is introduced to decompose the raw vibration signal. Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. Third, Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). Meanwhile, the physical meaning of MTEO is also discovered in this paper. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis.

Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier

Abstract: Targeting the nonstationary and non-Gaussian characteristics of vibration signal from fault rolling bearing, this paper proposes a fault feature extraction method based on variational mode decomposition (VMD) and autoregressive (AR) model parameters. Firstly, VMD is applied to decompose vibration signals and a series of stationary component signals can be obtained. Secondly, AR model is established for each component mode. Thirdly, the parameters and remnant of AR model served as fault characteristic vectors. Finally, a novel random forest (RF) classifier is put forward for pattern recognition in the field of rolling bearing fault diagnosis. The validity and superiority of proposed method are verified by an experimental dataset. Analysis results show that this method based on VMD-AR model can extract fault features accurately and RF classifier has been proved to outperform comparative classifiers.

Development of a Vehicle-Bridge-Soil Dynamic Interaction Model for Scour Damage Modelling

Abstract: Damage detection in bridges using vibration-based methods is an area of growing research interest. Improved assessment methodologies combined with state-of-the-art sensor technology are rapidly making these approaches applicable for real-world structures. Applying these techniques to the detection and monitoring of scour around bridge foundations has remained challenging; however this area has gained attraction in recent years. Several authors have investigated a range of methods but there is still significant work required to achieve a rounded and widely applicable methodology to detect and monitor scour. This paper presents a novel Vehicle-Bridge-Soil Dynamic Interaction (VBSDI) model which can be used to simulate the effect of scour on an integral bridge. The model outputs dynamic signals which can be analysed to determine modal parameters and the variation of these parameters with respect to scour can be examined. The key novelty of this model is that it is the first numerical model for simulating scour that combines a realistic vehicle loading model with a robust foundation soil response model. This paper provides a description of the model development and explains the mathematical theory underlying the model. Finally a case study application of the model using typical bridge, soil, and vehicle properties is provided.

Improved Element Erosion Function for Concrete-Like Materials with the SPH Method

Abstract: The subject of the paper is a description of a simple test from the field of terminal ballistics and the handling of issues arising during its simulation using the numerical techniques of the finite element method. With regard to the possible excessive reshaping of the finite element mesh there is a danger that problems will arise such as the locking of elements or the appearance of negative volumes. It is often necessary to introduce numerical extensions so that the simulations can be carried out at all. When examining local damage to structures, such as the penetration of the outer shell or its perforation, it is almost essential to introduce the numerical erosion of elements into the simulations. However, when using numerical erosion, the dissipation of matter and energy from the computational model occurs in the mathematical background to the calculation. It is a phenomenon which can reveal itself in the final result when a discrepancy appears between the simulations and the experiments. This issue can be solved by transforming the eroded elements into smoothed particle hydrodynamics particles. These newly created particles can then assume the characteristics of the original elements and preserve the matter and energy of the numerical model.

Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT

Abstract: The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but also how early these failures can be detected. The purpose of this paper is to present an experimental study of partially damaged rotor bar in induction motor under different load conditions based on discrete wavelet transform analysis. The approach is based on the extraction of features from vibration signals at different level of damage and three mechanical load conditions. The proposed analysis is reliable for tracking the damage in rotor bar. The paper presents an analysis and extraction of vibration features for partially damaged rotor bar in induction motors. The experimental analysis shows the change in behavior of vibration due to load condition and progressive damage.

Control Performance and Robustness of Pounding Tuned Mass Damper for Vibration Reduction in SDOF Structure

Abstract: This paper investigates the control performance of pounding tuned mass damper (PTMD) in reducing the dynamic responses of SDOF (Single Degree of Freedom) structure. Taking an offshore jacket-type platform as an example, the optimal damping ratio and the gap between mass block and viscoelastic material are presented depending on a parametric study. Control efficiency influenced by material properties and contact geometries for PTMD is analyzed here, as well as robustness of the device. The results of numerical simulations indicated that satisfactory vibration mitigation and robustness can be achieved by an optimally designed PTMD. Comparisons between PTMD and traditional TMD demonstrate the advantages of PTMD, not only in vibration suppression and costs but also in effective frequency bandwidth.

Experimental and Numerical Evaluation of Progressive Collapse Behavior in Scaled RC Beam-Column Subassemblage

Abstract: An experimental test was carried out on a 3/10 scale subassemblage in order to investigate the progressive collapse behavior of reinforced concrete (RC) structures. Investigation of alternative load paths and resistance mechanisms in scaled subassemblage and differences between the results of full-scale and scaled specimens are the main goals of this research. Main characteristics of specimen response including load-displacement curve, mechanism of formation and development of cracks, and failure mode of the scaled specimen had good agreement with the full-scale specimen. In order to provide a reliable numerical model for progressive collapse analysis of RC beam-column subassemblages, a macromodel was also developed. First, numerical model was validated with experimental tests in the literature. Then, experimental results in this study were compared with validated numerical results. It is shown that the proposed macromodel can provide a precise estimation of collapse behavior of RC subassemblages under the middle column removal scenario. In addition, for further evaluation, using the validated numerical model, parametric study of new subassemblages with different details, geometric and boundary conditions, was also done.

Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

Abstract: Traditional fault diagnosis methods of bearings detect characteristic defect frequencies in the envelope power spectrum of the vibration signal. These defect frequencies depend upon the inherently nonstationary shaft speed. Time-frequency and subband signal analysis of vibration signals has been used to deal with random variations in speed, whereas design variations require retraining a new instance of the classifier for each operating speed. This paper presents an automated approach for fault diagnosis in bearings based upon the 2D analysis of vibration acceleration signals under variable speed conditions. Images created from the vibration signals exhibit unique textures for each fault, which show minimal variation with shaft speed. Microtexture analysis of these images is used to generate distinctive fault signatures for each fault type, which can be used to detect those faults at different speeds. A -nearest neighbor classifier trained using fault signatures generated for one operating speed is used to detect faults at all the other operating speeds. The proposed approach is tested on the bearing fault dataset of Case Western Reserve University, and the results are compared with those of a spectrum imaging-based approach.

Modeling and Optimization of Vehicle Suspension Employing a Nonlinear Fluid Inerter

Abstract: An ideal inerter has been applied to various vibration engineering fields because of its superior vibration isolation performance. This paper proposes a new type of fluid inerter and analyzes the nonlinearities including friction and nonlinear damping force caused by the viscosity of fluid. The nonlinear model of fluid inerter is demonstrated by the experiments analysis. Furthermore, the full-car dynamic model involving the nonlinear fluid inerter is established. It has been detected that the performance of the vehicle suspension may be influenced by the nonlinearities of inerter. So, parameters of the suspension system including the spring stiffness and the damping coefficient are optimized by means of QGA (quantum genetic algorithm), which combines the genetic algorithm and quantum computing. Results indicate that, compared with the original nonlinear suspension system, the RMS (root-mean-square) of vertical body acceleration of optimized suspension has decreased by 9.0%, the RMS of pitch angular acceleration has decreased by 19.9%, and the RMS of roll angular acceleration has decreased by 9.6%.

Research on the Rockburst Tendency and AE Characteristics of Inhomogeneous Coal-Rock Combination Bodies

Abstract: In order to research the influence of homogeneity on the rockburst tendency and on AE characteristics of coal-rock combination body, uniaxial compressive tests of inhomogeneous coal-rock combination bodies obeyed by the Weibull distribution were simulated using particle flow code (). Macromechanical properties, energy evolution law, and AE characteristics were analyzed. The results show that (1) the elastic modulus homogeneity has an exponential relation with macroscopic modulus , and the bonding strength homogeneity has an exponential relation with uniaxial compressive strength ; (2) the rockburst tendency of the coal-rock combination body will increase with the increase of or , and is the leading factor influencing this tendency; and (3) both the change law of AE hits and lasting time in different periods of AE characteristics are influenced by , but just influences the lasting time. The more inhomogeneous the coal-rock combination body is, the shorter the lasting time in booming period of AE characteristics will be. This phenomenon can be used to predict the rockburst tendency of the coal-rock combination body.

A Unified Spectro-Geometric-Ritz Method for Vibration Analysis of Open and Closed Shells with Arbitrary Boundary Conditions

Abstract: This paper presents free vibration analysis of open and closed shells with arbitrary boundary conditions using a spectro-geometric-Ritz method. In this method, regardless of the boundary conditions, each of the displacement components of open and closed shells is represented simultaneously as a standard Fourier cosine series and several auxiliary functions. The auxiliary functions are introduced to accelerate the convergence of the series expansion and eliminate all the relevant discontinuities with the displacement and its derivatives at the boundaries. The boundary conditions are modeled using the spring stiffness technique. All the expansion coefficients are treated equally and independently as the generalized coordinates and determined using Rayleigh-Ritz method. By using this method, a unified vibration analysis model for the open and closed shells with arbitrary boundary conditions can be established without the need of changing either the equations of motion or the expression of the displacement components. The reliability and accuracy of the proposed method are validated with the FEM results and those from the literature.

Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

Abstract: Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.

On the Synchronization of Two Eccentric Rotors with Common Rotational Axis: Theory and Experiment

Abstract: We study synchronization of two eccentric rotors (ERs) with common rotational axis in the vibration system of the far-resonant spatial motion. We deduce the dimensionless coupling equation of two ERs with applying the average method of small parameters. We convert the synchronization problem into the existence and stability of solving the zero solutions for the dimensionless coupling equations. By introducing the synchronization torque and the difference between the residual torques of two motors, we obtain the synchronization condition that two ERs achieve the synchronized motion. We derive the stability condition of the synchronized motion, which satisfies Routh-Hurwitz criterion. We discuss numerically the choosing motion feature of the vibration system, which indicates that the vibration system has two steady motion modes. The synchronization torque forces the phase difference to approach when the structural parameters of the vibration system satisfy the condition of the spatial cone motion, and the synchronization torque drives the phase difference to approach zero when those satisfy the condition of the spatial circle motion. Finally, through the comparison and analysis of experimental data, the structural parameters of the vibration system satisfying the above two conditions can guarantee the synchronization stability for two ERs.