Static balancing of rotating bodies

A sufficient condition for static balancing of rotating bodies is derived. An example of the verification of balancing is considered.     

Stochastic U-line balancing using genetic algorithms

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.     

旋转机械故障诊断专家系统

在旋转机械最佳诊断方法理论的指导下,依据基于黑灰白的推理机技术开发了旋转机械故障诊断专家系统。本文着重说明了故障诊断系统中推理机和知识库这两部分的设计及实现,最后根据工厂实际数据进行验证,列出了验证的结果。     

旋转机械自动平衡装置综述

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.     

钻具旋转机械手设计

研究了钻具在两个工况的位置及相互的转换关系,利用机、电、液技术设计一套旋转机械手使其设想变为现实。本旋转机械手主要由机座、旋转轴系、旋转臂、夹持器和旋转油缸组成。这些部件通过本设计整合后实现钻具在钻杆架及锚道之间的工位转换作业,以实现该工况作业的自动化。     

Branch-and-bound algorithms for simple assembly line balancing problem

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.     

A novel procedure for diagnosing multiple faults in rotating machinery

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 C_L, and a high frequency CMF, denoted as C_h. Then it optimizes the amplitude of the added noise in decomposing C_h and C_L 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.     

小波算法在旋转机械故障诊断系统中的应用

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。     

基于线调频小波变换的旋转机械故障诊断

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.     

回转机械自同期运动机理的建模与仿真

提出了回转机械自同期运动的概念,通过实验测试分析了其自动平衡效果。通过建模与仿真,揭示出回转机械的自同期运动机理和滚动摩擦因数、粘性阻尼系数、加速度等参数的影响规律。     

Deep residual learning-based fault diagnosis method for rotating machinery

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.     

旋转机械全息序列相似性匹配故障诊断方法

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.     

Decision tree and PCA-based fault diagnosis of rotating machinery

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.     

Multiscale singular value manifold for rotating machinery fault diagnosis

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.     

旋转机械基于信号的故障检测分析

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。     

旋转机械温度诊断方法研究

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。     

旋转机械故障诊断研究现状

对旋转机械故障产生的机理、特征提取、装置的开发与研究,分别进行了阐述.并且对旋转机械故障诊断的人工智能专家系统、模式识别技术、分形理论、数据挖掘方法、信息融合技术的研究现状进行了综述.