Journal of Vibroengineering
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A method for shifting natural frequencies of a dynamic system to desired values with concentrated mass modifications
Get PDFIn this study, shifting a certain number of natural frequencies of a dynamic system to the desired values with the concentrated mass modifications is considered. A new method is proposed in order to determine the necessary mass modifications. The method proposed is based on the Sherman-Morrison formula and uses the receptances that are related to the modification coordinates of the original system. The system is sequentially modified at the predefined locations by a number of unknown masses which equal the number of frequencies that will be shifted. The method yields a set of nonlinear equations which equal the number of shifting frequencies, then the necessary masses are estimated by solving these equations numerically. The efficiency of the proposed method is shown by various numerical and experimental applications. It is shown that the method is very effective and can be used for real applications
Regularized kernel function parameter of KPCA using WPSO-FDA for feature extraction and fault recognition of gearbox
Get PDFGearbox is subject to damage or malfunctions by complicated factors such as installation position and operation condition, meanwhile, accompanied by some nonlinear behaviors, which increase the difficulty of fault diagnosis and identification. Kernel principal component analysis (KPCA) is a commonly used method to realize nonlinear mapping via kernel function for feature extraction. However, choosing an appropriate kernel function and the proper setting of its parameter are decisive to obtain a high performance of the kernel methods. In this paper, we present a novel approach combining PSO and KPCA to enhance the fault classification performance. The standard particle swarm optimization (WPSO) was used to regularize kernel function parameter of KPCA instead of the empirical value. In particular, in view of the thought of Fisher Discriminate Analysis (FDA) in pattern recognition, the optimal mathematical model of kernel parameter was constructed, and its global optimal solution was searched by WPSO. The effectiveness of the method was proven using the Iris data set classification and gearbox faults classification. In the process, gearbox fault experiments were carried out, and the vibration signals in different conditions have been tested and processed, and the fault feature parameters were extracted. At last the analysis results of gearbox fault recognition was obtained by KPCA and compared with PCA. The results show that the separability of failure patterns in the feature space is improved after kernel parameter optimized by WPSO-FDA. The problems of single failure and compound fault recognition have been effectively solved by the optimized KPCA
A new weak fault diagnosis method based on multi-scale wavelet noise tuning cascaded multi-stable stochastic resonance
Get PDFFor detecting the weak fault diagnosis submerged in heavy noise, a new method called multi-scale cascaded multi-stable stochastic resonance (MCMSR) is studied. The method can effectively extract weak fault diagnosis from noise background using multi-scale wavelet noise tuning stochastic resonance (SR). Firstly, input signal with noise is decomposed by multi-scale wavelets transformation, and each scale signal is adjusted by scaling factor, then the decomposed signal is used as the input of cascaded multi-stable systems to achieve the detection of fault diagnosis. If the input signal is a large parameter signal, to conform to the conditions of SR, the decomposed signal must be processed by twice sampling. The simulation and experimental signals are carried out to test the feasibility of the method. From the signal to noise ratio (SNR) comparison curves of original signal, SR output signal and MCMSR output signal plotted together, we can find that the useful signal can be enhanced by MCMSR method than SR method. The experimental results indicate that the MCMSR can extract fault diagnosis from heavy background noise
Using artificial neural network for forward kinematic problem of under-constrained cable robots
Get PDFCable-Driven Parallel Robot has many advantages. However, the problems of cable collision between each other and environment, the lack of proper structure and non-positive cable tension prevent the spread of them. In this work, a neural network (NN) model of under constrained cable robots is presented with external forces applied to the end-effector (EE) for computing the position of it. As in under-constrained robot’s kinematics and statics are innately coupled together, and they contemporaneously should be considered the forward kinematic problem of the robot change to an optimization problem. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The main contribution of this paper has three goals. First, a method is used toward kinematic problem of the under constrained cable robot modeling using four bar linkage kinematic concept, which could be used in online control approaches for real-time purposes. Second, in order to track the position of end-effector, an online PD controller is designed by the three error criteria methods such as IAE, ISE and ITSE. Finally, as the third contribution, NN control approach is applied in order to validate the model. A model is created based on the robot’s geometry and dynamic to solve the forward kinematics problem. So, the forward kinematic problem is solved offline and used online. Moreover, an analysis of workspace is performed which discovers that the solution of the forward kinematic problem of the under-constrained cable robots is unique in this case. In addition, a modified local linear model tree algorithm for nonlinear system modelling are proposed. The results show the effectiveness of the proposed approach in modeling the under constrained cable robot
Structural damage detection based on cloud model and Dempster-Shafer evidence theory
Get PDFCloud model and D-S theory have been widely used in uncertainty reasoning. Meanwhile, modal strain energy and Inner Product Vector are also utilized as damage-sensitive features to detect structural damage. In this paper, a new structural damage identification approach is proposed based on Dempster-Shafer theory and cloud model. Cloud models were created to make uncertainty reasoning in damage structures by modal strain energy and the Inner Product Vector of acceleration. Then the results of the two methods were combined by using the Dempster-Shafer theory. Due to the classical D-S theory involves counter – intuitive behavious when the high conflicting evidences exists, the distance function was introduced to correct the conflict factor K and combine the evidences. Moreover, a model of simple beam was created to verify the feasibility and accuracy for the single-damage and the multiple-damage. The effects of noise on damage detection were investigated simultaneously. The results show that the method has strong anti-noise ability and high accuracy
An influence of the parameters of the loading heads on stability and free vibrations of a damaged column subjected to a specific load
Get PDFIn this paper, the studies on an influence of the parameters of the loading heads on the stability and free vibrations as well as on loading capacity of the cracked column subjected to a specific load are presented. The defect of the host structure has a form of a crack and is simulated by means of the rotational spring. The boundary problem is formulated by use of Hamilton’s principle on the basis of which the differential equations of motion as well as natural boundary conditions are obtained. The main scope of this study is to estimate an influence of the parameters of the loading heads on stability and free vibrations as well as on loading capacity of the cracked column. Finally, the numerical simulations are compared to the experimental ones
Modelling the influence of sub-grade material parameters on the response of railway line under moving train loads
Get PDFThe sub-grade of the railway track subjected to dynamic loading is governed by its dynamic material parameters, the basic dynamic material parameters are unit weight, Young’s modulus, Poisson’s ratio and the material damping. However, there is a need to identify the parameters of the sub-grade that has significant effect on the dynamic response of the railway track to achieve the specified technical requirements because the behavior of the sub-grade cannot be easily estimated or verified. Hence, this paper uses three-dimensional finite elements ABAQUS coupled with Artificial Non-Reflecting Boundary to investigate the effect of varying the unit weight, Young’s modulus, Poisson's ratio and the damping ratio of the sub-grade in the surface vibration induced at the embankment railway track in terms of Peak Vertical Velocity due to moving of the train loads. The input soil parameters used are soft clay, stiff clay, loose and dense uniform sand to demonstrate the parametric study. The predicated results show that both the Young’s modulus and damping ratio of the sub-grade have quite significant effect on the induced surface vibrations of railway track; while the influence of both unit weight and Poisson’s ratio are very small. Hence to fulfill the technical requirement of the railway track, special attention regarding to the Young’s modulus and the damping ratio of the sub-grade should be paid
Erratum: Monitoring of blast-induced ground vibration using WSN and prediction with an ANN approach of ACC dungri limestone mine, India
Get PDFThe correlation analysis of gear tooth broken-pitting compound fault and single fault based on Laplacian Eigenmaps
Get PDFGear break and pitting are two common faults in transmission system, when these two faults coexist and form a compound fault, the damage speed and frequency of gear transmission system will be greatly increased. Taking the gear fault-pitting compound fault as the object, the dynamic model of gear single fault and compound fault is established, and the vibration characteristics of gear single fault, pitting single fault and broken tooth-pitting compound fault signal are analyzed. The characteristic manifolds of the intrinsic dimension space in the case of gear single failure and compound fault are extracted by using the Laplacian Eigenmaps algorithm, the evolution trend of single fault and compound fault in the overlapping region of the feature space, the degree of correlation and the curvature of the fault circle core are analyzed and obtained. The study found that with the deepening of the fault severity, the overlapping area of fault circle between compound fault and single fault become smaller gradually, that is, the degree of correlation become weakened, tooth broken single fault and compound fault can be identified in mid-late stage of fault, while the pitting single fault and compound fault are in the late stage. The experimental results of gearbox compound fault correlation show that the conclusion of the simulation analysis is correct and effective, which provides a new idea for the diagnosis of mechanical complex faults