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Search Results: 1 - 10 of 23272 matches for " Zhengjia He "
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Method for Vibration Response Simulation and Sensor Placement Optimization of a Machine Tool Spindle System with a Bearing Defect
Hongrui Cao,Linkai Niu,Zhengjia He
Sensors , 2012, DOI: 10.3390/s120708732
Abstract: Bearing defects are one of the most important mechanical sources for vibration and noise generation in machine tool spindles. In this study, an integrated finite element (FE) model is proposed to predict the vibration responses of a spindle bearing system with localized bearing defects and then the sensor placement for better detection of bearing faults is optimized. A nonlinear bearing model is developed based on Jones’ bearing theory, while the drawbar, shaft and housing are modeled as Timoshenko’s beam. The bearing model is then integrated into the FE model of drawbar/shaft/housing by assembling equations of motion. The Newmark time integration method is used to solve the vibration responses numerically. The FE model of the spindle-bearing system was verified by conducting dynamic tests. Then, the localized bearing defects were modeled and vibration responses generated by the outer ring defect were simulated as an illustration. The optimization scheme of the sensor placement was carried out on the test spindle. The results proved that, the optimal sensor placement depends on the vibration modes under different boundary conditions and the transfer path between the excitation and the response.
A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running Time
Zhongjie Shen,Zhengjia He,Xuefeng Chen,Chuang Sun,Zhiwen Liu
Sensors , 2012, DOI: 10.3390/s120810109
Abstract: Performance degradation assessment based on condition monitoring plays an important role in ensuring reliable operation of equipment, reducing production downtime and saving maintenance costs, yet performance degradation has strong fuzziness, and the dynamic information is random and fuzzy, making it a challenge how to assess the fuzzy bearing performance degradation. This study proposes a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description (FSVDD) and running time. FSVDD constructs the fuzzy-monitoring coefficient?which is sensitive to the initial defect and stably increases as faults develop. Moreover, the parameter describes the accelerating relationships between the damage development and running time. However, the index?with an oscillating trend disagrees with the irreversible damage development. The running time is introduced to form a monotonic index, namely damage severity index (DSI). DSI inherits all advantages of and overcomes its disadvantage. A run-to-failure test is carried out to validate the performance of the proposed method. The results show that DSI reflects the growth of the damages with running time perfectly.
Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information
Gaigai Cai,Xuefeng Chen,Bing Li,Baojia Chen,Zhengjia He
Sensors , 2012, DOI: 10.3390/s121012964
Abstract: The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools.
A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes
Yaguo Lei,Jing Lin,Zhengjia He,Detong Kong
Sensors , 2012, DOI: 10.3390/s120202005
Abstract: Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.
Simulation and Experimental Investigation of Structural Dynamic Frequency Characteristics Control
Xingwu Zhang,Xuefeng Chen,Shangqin You,Zhengjia He,Bing Li
Sensors , 2012, DOI: 10.3390/s120404986
Abstract: In general, mechanical equipment such as cars, airplanes, and machine tools all operate with constant frequency characteristics. These constant working characteristics should be controlled if the dynamic performance of the equipment demands improvement or the dynamic characteristics is intended to change with different working conditions. Active control is a stable and beneficial method for this, but current active control methods mainly focus on vibration control for reducing the vibration amplitudes in the time domain or frequency domain. In this paper, a new method of dynamic frequency characteristics active control (DFCAC) is presented for a flat plate, which can not only accomplish vibration control but also arbitrarily change the dynamic characteristics of the equipment. The proposed DFCAC algorithm is based on a neural network including two parts of the identification implement and the controller. The effectiveness of the DFCAC method is verified by several simulation and experiments, which provide desirable results.
Biaxial Yield Surface Investigation of Polymer-Matrix Composites
Junjie Ye,Yuanying Qiu,Zhi Zhai,Zhengjia He
Sensors , 2013, DOI: 10.3390/s130404051
Abstract: This article presents a numerical technique for computing the biaxial yield surface of polymer-matrix composites with a given microstructure. Generalized Method of Cells in combination with an Improved Bodner-Partom Viscoplastic model is used to compute the inelastic deformation. The validation of presented model is proved by a fiber Bragg gratings (FBGs) strain test system through uniaxial testing under two different strain rate conditions. On this basis, the manufacturing process thermal residual stress and strain rate effect on the biaxial yield surface of composites are considered. The results show that the effect of thermal residual stress on the biaxial yield response is closely dependent on loading conditions. Moreover, biaxial yield strength tends to increase with the increasing strain rate.
Damage Identification by the Kullback-Leibler Divergence and Hybrid Damage Index
Shaohua Tian,Zhibo Yang,Zhengjia He,Xuefeng Chen
Shock and Vibration , 2014, DOI: 10.1155/2014/962056
Abstract: The hybrid damage index (HDI) is presented as a mean for the damage identification in this paper, which is on the basis of the Kullback-Leibler divergence (KLD) and its approximations. The proposed method is suitable for detecting damage in one-dimensional structure and delamination in laminated composite. The first step of analysis includes obtaining the mode data of the structure before and after the damage, and then the KLD and its approximations are obtained. In addition, the HDI is obtained on the basis of the KLD and its approximations, utilizing the natural frequencies and mode shape at the same time. Furthermore, the modal strain energy (MSE) method is employed to verify the efficiency of the proposed method. Finally, to demonstrate the capability of the proposed method, examples of the beam and laminated composite are applied for checking the present approaches numerically, and the final results validate the effective and accurate performance of the present technique. 1. Introduction As evidenced by the vast literature in the damage detection, the structural health monitoring has become an increasingly crucial issue. To data, significant efforts have been made by researchers in the damage identification. The presence of damage generally produces changes in the structural stiffness matrix. Meanwhile, these changes are accompanied with changes in the structural modal parameters. This phenomenon has been widely noted and used by researchers in distinguishing the damage. However, using different modal parameters correlated with other relevant information in the damage identification may get very various results with varying accuracy. For this reason, seeking a proper selection or combination of dynamic parameters is an imperative purpose. From the perspective of the damage detection, Park et al. [1] reviewed the piezoelectric impedance-based structural health monitoring and applied it in the damage detection of civil structural components [2]. Sekhar [3] provided an excellent review on research advances in damage detection areas over the twenty years. Fan and Qiao [4] reviewed vibration-based damage identification methods and gave a comparative study on the damage detection, and the strain-based damage index for the structural damage identification was reviewed by Li [5]; the recurrence quantification analysis has emerged as a useful tool for detecting subtle nonstationarities and changes in the time-series data; Nichols et al. [6] extended the recurrence quantification analysis method to multivariate observations for the damage detection. Sun et
A Stochastic Wavelet Finite Element Method for 1D and 2D Structures Analysis
Xingwu Zhang,Xuefeng Chen,Zhibo Yang,Bing Li,Zhengjia He
Shock and Vibration , 2014, DOI: 10.1155/2014/104347
Abstract: A stochastic finite element method based on B-spline wavelet on the interval (BSWI-SFEM) is presented for static analysis of 1D and 2D structures in this paper. Instead of conventional polynomial interpolation, the scaling functions of BSWI are employed to construct the displacement field. By means of virtual work principle and BSWI, the wavelet finite elements of beam, plate, and plane rigid frame are obtained. Combining the Monte Carlo method and the constructed BSWI elements together, the BSWI-SFEM is formulated. The constructed BSWI-SFEM can deal with the problems of structural response uncertainty caused by the variability of the material properties, static load amplitudes, and so on. Taking the widely used Timoshenko beam, the Mindlin plate, and the plane rigid frame as examples, numerical results have demonstrated that the proposed method can give a higher accuracy and a better constringency than the conventional stochastic finite element methods. 1. Introduction The finite element method (FEM) is well accepted now in numerous industrial areas [1–4]. However, the validity of the results obtained by FEM can be drastically limited by the uncertainties of the parameters introduced in the model. Many factors may cause the uncertainties, such as a consequence of ignorance and spatial variability [5, 6]. On the other hand, the reliability design and analysis for engineering structures also require a kind of FE model which could deal with the variability and uncertainty. For example, a slight change in design variables may result in a poignant change of structural properties. As a powerful tool in computational stochastic mechanics, the stochastic finite element method (SFEM) appeared. SFEM is an extension of the classical FEM to stochastic framework. The most important factor of SFEM is the uncertainty modeling. Over the past several decades, many methods were combined with FEM to approximate the uncertainties mentioned above. Chakraborty and Dey investigated the stochastic finite element simulation based on the Neumann expansion of uncertain structures subjected to earthquake [7]. A method named first-order, second-moment (FOSM) method was proposed by Cornell [8]; in fact it is a special case of perturbation method [9, 10]. Valdebenito et al. studied the application of first-order expansion considering intervening variables for estimating second-order statistics for stochastic finite element analysis [11]. The Karhunen-Loeve [12] expansion, which can be seen as a kind of the orthogonal series expansion, is also an important representation of SFEM
LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information
Zhiwen Liu,Xuefeng Chen,Zhengjia He,Zhongjie Shen
Sensors , 2013, DOI: 10.3390/s130708679
Abstract: Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM.
A Complete Geometric Representation of Four-Player Weighted Voting Systems
Zhengjia Jiang
Journal of Mathematics Research , 2013, DOI: 10.5539/jmr.v5n1p122
Abstract: This paper seeks to expand voting power theory, a branch of game theory that applies to many important organizations. Typically, weighted voting systems are displayed using the algebraic representation, consisting of a quota and a weight vector. A newer idea, however, is the emph{geometric representation}. This representation maps all normalized weighted voting systems onto a simplex and thus can be called a complete representation of weighted voting systems. The concept of the emph{region}, sets of characteristically identical weighted voting systems, will be introduced, greatly simplifying the analysis of weighted voting systems. In this paper, four-player weighted voting systems are solved completely using the geometric representation. The geometric representation will be shown to be a useful alternative to the algebraic representation.
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