基于混沌弱信号检测技术的轴承异常微弱信号辨识

发布时间：2018-05-02 15:02

本文选题：混沌弱信号检测技术 + 微弱信号　；参考：《内蒙古科技大学》2013年硕士论文

【摘要】：滚动轴承是现如今应用最多的机械部件之一，现场大型设备基本都是旋转机械，几乎每台旋转机械上都能看到轴承的身影。每年因为旋转机械故障而造成的经济损失高达数百亿，而因为轴承问题导致的故障几乎占到所有旋转机械故障的1/3以上。任何故障都是从轻微开始，直到造成严重事故。因此在轴承出现故障之初就将其检测出来非常重要。作为非线性科学的分支——混沌理论，现如今已经在很多工程领域都得到了广泛应用。把混沌理论应用到微弱信号检测中去是目前研究的轴承故障问题的主要方法之一。本文基于国内外混沌理论的研究基础上，广泛吸取了各领域对混沌理论的深刻理解和研究成果，利用混沌理论来识别滚动轴承微弱故障信号中所包含的轴承故障信息，判断出轴承故障位置。本文主要做了如下几个方面的内容：首先，本文介绍了混沌的基本概念、发展历程、主要成就，以及常见故障诊断的判别方法，叙述了混沌理论对现代科学的主要影响和意义。并且对弱信号进行了简单的介绍，给出了简单的处理方法，并且对信号的性能进行了综述。其次，建立了检测混沌微弱周期信号的模型。利用Duffing混沌系统的动力学行为特点来检测微弱周期信号，向处在临界周期状态的系统中加入微弱周期信号，观察Duffing方程的相轨迹图是否发生突变。从而可以反映出是否存在信噪比较大的微弱周期信号。最后，对传统Duffing方程做了进一步的改进，并利用改进方程来检测轴承点蚀故障微弱振动信号。轴承故障信号是多种不同信号混合在一起所形成的非常复杂的复合信号。轴承故障信号具有一定的混沌特性。每一种不同型号的轴承或者是同一轴承不同位置的特征信号都不相同。根据轴承的这一特性提出了基于混沌理论的检测轴承故障的方法。该方法只对特征信号敏感，而对噪声信号具有很好的抑制作用。对于初期采集到的信号由于存在许多无用信号，为了减小计算量，，需要在利用Duffing方程进行分析之前，事先利用小波包降噪将原始信号进行初步降噪。通过最终的结果表明利用小波理论和混沌理论能够很好的将滚动轴承外圈早期故障信息反映出来，从而说明该方法是判断滚动轴承故障问题的有效方法。
[Abstract]:Rolling bearing is one of the most widely used mechanical parts nowadays. The field large-scale equipment is basically rotating machinery, almost every rotating machine can see the shape of bearing. Every year, the economic loss caused by rotating machinery faults is as high as tens of billions, and the faults caused by bearing problems account for almost a third of all rotating machinery failures. Any malfunction starts slightly until it causes a serious accident. Therefore, it is very important to detect the bearing at the beginning of the failure. As a branch of nonlinear science, chaos theory has been widely used in many engineering fields. Applying chaos theory to weak signal detection is one of the main methods of bearing fault problem. Based on the research of chaos theory at home and abroad, this paper has widely absorbed the deep understanding and research results of chaos theory in various fields, and used chaos theory to identify the bearing fault information contained in the weak fault signal of rolling bearing. Determine the bearing fault location. The main contents of this paper are as follows: First of all, this paper introduces the basic concept of chaos, the development of chaos, the main achievements, as well as the common fault diagnosis method, and describes the main impact and significance of chaos theory on modern science. The weak signal is introduced briefly, the processing method is given, and the performance of the signal is summarized. Secondly, a model for detecting chaotic weak periodic signals is established. The weak periodic signal is detected by the dynamic behavior of the Duffing chaotic system, and the weak periodic signal is added to the system in the critical periodic state. The phase locus of the Duffing equation is observed to be abrupt. Therefore, the existence of weak periodic signal with high SNR can be reflected. Finally, the traditional Duffing equation is further improved, and the improved equation is used to detect the weak vibration signal of bearing pitting fault. Bearing fault signal is a very complex composite signal formed by the mixing of many different signals. The bearing fault signal has certain chaos characteristic. Each type of bearing or the same bearing at different locations has different characteristic signals. According to this characteristic of bearing, a method of detecting bearing fault based on chaos theory is proposed. This method is only sensitive to the characteristic signal and has a good suppression effect on the noise signal. Because there are many useless signals in the initial signal, in order to reduce the computational complexity, it is necessary to reduce the initial noise of the original signal by using wavelet packet denoising before using the Duffing equation to analyze the signal. The final results show that the wavelet theory and chaos theory can well reflect the early fault information of the outer ring of rolling bearing, which shows that this method is an effective method to judge the fault problem of rolling bearing.
【学位授予单位】：内蒙古科技大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TH133.33

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