基于混沌振子的回转支承故障诊断方法的研究

发布时间：2018-06-30 06:40

本文选题：回转支承 + 仿真　；参考：《西安建筑科技大学》2014年硕士论文

【摘要】：随着我国经济的不断发展和工业化程度的不断提高，机械设备开始在现实生活中扮演着越来越重要的角色，而回转支承作为许多机械设备的关键部件也为国民经济的发展做着巨大的贡献。回转支承大多用于低速重载的场合，其信号微弱且易淹没在正常信号之中，其故障诊断的研究也一直是一个难题。近些年来，混沌理论作为一种新兴的方法在微弱信号的检测中起到了很好的效果，为了研究混沌系统是否适用于微弱信号的检测，本文结合混沌振子的相关理论对回转支承故障诊断进行了一定的研究。本文对回转支承的结构形式、运动特征等进行了简单介绍，对回转支承的一些典型故障进行总结，并对其点蚀故障的特征频率进行分析和计算。以某小型挖掘机的回转支承为研究对象，，结合挖掘机的工作特点，搭建了回转支承实验台和信号采集系统，分别采集了不同转速下的故障信号。在对回转支承进行故障诊断分析之前，还对回转支承进行了受力分析和接触分析，并对接触刚度等重要参数进行了详细计算，然后用PRO-E对回转支承进行建模，用ADAMS对回转支承进行仿真分析。本文在受力分析的基础之上又对回转支承的摩擦力矩进行了分析计算，通过回转支承摩擦力矩的理论值和仿真值的对比来验证结论。由于回转支承的转速较低且承重较大，其故障信号往往容易淹没在正常的信号和噪音信号之中，传统的故障诊断方法并不适用，所以需要使用其他的方法对回转支承进行故障诊断。本文将小波变换和Hilbert变换方法结合起来，用小波变换对信号进行分解和重构，再应用Hilbert变换进行解调和细化频谱分析，确认实验和仿真信号中含有故障特征频率。之后引入混沌理论并对其进行分析，并将故障信号加入一种新型混沌系统——变形Rossler混沌系统之中，通过相图的改变来判断回转支承是否故障，并通过计算其Lyapunov指数来定量地判断故障特征频率并得到结论。最后，通过仿真数据和实验数据的分析来互相验证其正确性，证明变形Rossler混沌系统对于微弱信号的检测是可行的。
[Abstract]:With the continuous development of our economy and the continuous improvement of industrialization, machinery and equipment are playing an increasingly important role in real life. As a key component of many mechanical equipment, rotary bearing also makes great contribution to the development of national economy. The slewing bearing is mostly used in low speed and heavy load situations, its signal is weak and easily submerged in the normal signal, and the research of fault diagnosis is always a difficult problem. In recent years, as a new method, chaos theory has played a good role in weak signal detection. In order to study whether chaotic system is suitable for weak signal detection, Based on the theory of chaotic oscillator, the fault diagnosis of rotary bearing is studied in this paper. In this paper, the structure and motion characteristics of rotary bearing are introduced, and some typical faults of rotary bearing are summarized, and the characteristic frequency of its pitting fault is analyzed and calculated. Taking the rotary support of a small excavator as the research object and combining the working characteristics of the excavator, the experimental platform of rotary support and the signal acquisition system are built, and the fault signals under different rotational speeds are collected respectively. Before the fault diagnosis and analysis of the slewing bearing, the stress analysis and contact analysis of the slewing bearing are carried out, and the contact stiffness and other important parameters are calculated in detail, and then the model of the slewing bearing is built with PRO-E. The simulation analysis of the slewing bearing is carried out by Adams. On the basis of stress analysis, the friction moment of slewing bearing is analyzed and calculated in this paper, and the conclusion is verified by comparing the theoretical value and simulation value of friction moment of slewing bearing. Because the rotating speed of the rotary bearing is low and the load bearing is large, the fault signal is often submerged in the normal signal and the noise signal, so the traditional fault diagnosis method is not suitable. Therefore, it is necessary to use other methods for fault diagnosis of slewing bearings. In this paper, the wavelet transform and Hilbert transform are combined to decompose and reconstruct the signal, and then the Hilbert transform is used to demodulate and refine the spectrum. The fault characteristic frequency is confirmed in the experimental and simulation signals. Then the chaos theory is introduced and analyzed, and the fault signal is added to a new chaotic system-deformed Rossler chaotic system. By calculating its Lyapunov exponent, the fault characteristic frequency is quantitatively determined and the conclusion is obtained. Finally, the correctness of the system is verified by the analysis of the simulation data and the experimental data. It is proved that the deformed Rossler chaotic system is feasible for weak signal detection.
【学位授予单位】：西安建筑科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TU621;TH165.3

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