共查询到20条相似文献,搜索用时 15 毫秒
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O. O. Polushkin 《Russian Engineering Research》2010,30(2):125-127
A sufficient condition for static balancing of rotating bodies is derived. An example of the verification of balancing is
considered. 相似文献
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Adil Baykasoğlu Lale Özbakır 《The International Journal of Advanced Manufacturing Technology》2007,32(1-2):139-147
The advantages of U-type lines are very well known in industry. They offer improved productivity and quality, and are considered
as one of the better techniques in implementing just-in-time (JIT) systems. There is a growing interest in the literature
to organize traditional assembly lines as U-lines for improved performance. U-type assembly line balancing is an extension
of the traditional line balancing problem, in which tasks can be assigned from both sides of the precedence diagram. Although
there are many studies in the literature for the design of traditional straight assembly lines, the work on U-type lines is
limited. Moreover, in most of the previous studies, task times are assumed to be deterministic. In this paper, a new multiple-rule-based
genetic algorithm (GA) is proposed for balancing U-type assembly lines with stochastic task times. 相似文献
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介绍了旋转机械自动平衡技术的发展概况,着重综述了三种自动平衡装置的工作原理、平衡特点和各自的优缺点,并对它们的减振效益进行分析与对比,讨论了旋转机械自动平衡装置今后需要解决的问题. 相似文献
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S. B. Liu K. M. Ng H. L. Ong 《The International Journal of Advanced Manufacturing Technology》2008,36(1-2):169-177
In this paper, exact algorithms for solving the simple assembly line balancing type I problem are presented. The proposed
algorithms consist of a constructive and two destructive algorithms. Several well-known lower-bound computational methods
are also applied in these algorithms. Computational experiments were carried out to test the performance of the proposed algorithms
based on a set of benchmark problem instances. The computational results show that the algorithms proposed in this paper are
efficient in solving the simple assembly line balancing benchmark problem instances. Moreover, a problem instance whose optimal
solution had previously been unknown is solved by one of the proposed algorithms. 相似文献
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In analyzing signals from a wind turbine gearbox this paper suggests a new signal processing procedure named as CMF-EEMD method which is formed by applying conventional EEMD to a new type of combined mode function (CMF). This CMF consists of a low frequency CMF, denoted as CL, and a high frequency CMF, denoted as Ch. Then it optimizes the amplitude of the added noise in decomposing Ch and CL using EEMD. Finally, it calculates cyclic autocorrelation function (CAF) for every characteristic IMF from EEMD. The proposed procedure is applied to analyze the multi-faults of a wind turbine gearbox and the results confirm better performances in resolving different signal components by the proposed method than that from the cyclic autocorrelation function (CAF) of a direct EEMD analysis. 相似文献
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小波算法在旋转机械故障诊断系统中的应用 总被引:1,自引:0,他引:1
目前旋转机械在社会各个方面均有着广泛的应用,同时旋转机械的故障也十分普遍。一旦发生故障就会造成巨大的经济损失。该文基于小波算法对旋转机械故障特征进行提取,在时域和频域中对提取数据进行分析处理,在上位中对故障诊断结果进行显示。以Visual Studio 2010为开发平台,C#为开发语言,运用小波算法,最终完成了故障诊断系统的开发。验证结果表明,该诊断系统具有诊断精度高、速度快等优点。 相似文献
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小波变换在设备故障信号处理中得到广泛地应用,然而,小波变换只能消除白色噪声,对有色噪声不起作用.线调频小波变换统一了短时Fourier变换和小波变换的时频分析,是信号的时间-频率-尺度变换,能根据信号的特点自适应生成新的时频窗口.它不仅具有小波变换良好的时频局部性特点,而且它的时频窗口比小波变换的时频窗口更加灵活.本文应用线调频小波变换对旋转机械故障信号进行消噪,效果明显. 相似文献
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Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach. 相似文献
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旋转机械全息序列相似性匹配故障诊断方法 总被引:2,自引:1,他引:1
针对全息诊断分辨率低影响旋转机械故障诊断质量和自动化水平的问题,将时间序列相似性匹配的基本概念和方法引入故障诊断应用中,结合全息诊断信息融合分析旋转机械振动全貌的思想,定义了全息序列及其相似性度量模型,用类时间轴上的多维序列表征转子系统振动全貌,进而利用采用近似三角不等式与B+树结合剪枝策略的全息序列相似性匹配算法实现故障诊断.实验结果表明,该方法能够实现高质量的故障自动分类识别. 相似文献
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《Mechanical Systems and Signal Processing》2007,21(3):1300-1317
After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN. 相似文献
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Time-frequency distribution of vibration signal can be considered as an image that contains more information than signal in time domain. Manifold learning is a novel theory for image recognition that can be also applied to rotating machinery fault pattern recognition based on time-frequency distributions. However, the vibration signal of rotating machinery in fault condition contains cyclical transient impulses with different phrases which are detrimental to image recognition for time-frequency distribution. To eliminate the effects of phase differences and extract the inherent features of time-frequency distributions, a multiscale singular value manifold method is proposed. The obtained low-dimensional multiscale singular value manifold features can reveal the differences of different fault patterns and they are applicable to classification and diagnosis. Experimental verification proves that the performance of the proposed method is superior in rotating machinery fault diagnosis. 相似文献
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旋转机械是各种类型机械设备中数量最多、应用最广泛的一类机械,介绍了基于信号处理的旋转机械故障诊断,并给出了支持向量机下模式识别与故障检测的方法,对压缩机进行了实验研究,证明了方法的有效性。该方法可以广泛应用到工程实际中,为有关人员起到一定的参考作用。 相似文献
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讨论了应用信息融合技术对旋转机械故障进行温度诊断,提出了建立在温度和振动数据基础上和材料性能相联系的故障诊断方法。 相似文献