基于EMD和共振解调的滚动轴承故障诊断方法研究

发布时间：2019-04-21 17:03

【摘要】：滚动轴承是旋转机械的重要零部件之一,其工作状态直接决定机械系统的性能及运行工况。在实际的工程实践中,滚动轴承的一个微小故障轻则可能导致生产线的停机,重则还可能损坏设备并造成严重的经济损失。因此,开展滚动轴承故障诊断与预报的研究对避免重大事故、变革维修体制和促进经济发展等都具有重要的现实意义。本文介绍了滚动轴承的机械结构、振动机理、故障形式及成因和故障特征等。详细研究了故障诊断领域应用较多的理论和方法,这些方法包括特征参数判别诊断法、共振解调诊断法、基于Hilbert-Huang变换的诊断法。本文利用振动法采集滚动轴承的故障信号,并搭建了现场试验台进行信号采集。通过对共振解调技术和Hilbert-Huang变换的研究发现:基于传统的共振解调技术的诊断方法存在带通滤波参数(中心频率和滤波带宽)需要预先确定和固定的滤波频带具有局限性等缺陷,而基于Hilbert-Huang变换的诊断方法虽然能够同时从时间尺度和频率尺度很好的描述信号的变化情况,但是通过Hilbert谱和边际谱还是难以观察到明显的故障特征信号。本文采用将这两种诊断方法结合起来进行故障诊断,利用了EMD分解的自适应性弥补了共振解调技术需要固定滤波参数的缺陷,利用共振解调技术能提取调制在高频固有振动中故障信息的能力弥补了HHT无法突显故障特征的缺陷,并用实验验证了该方法的有效性,实验结果表明基于EMD的共振解调技术能够准确可靠的进行滚动轴承的故障诊断。并在此基础上,针对EMD共振解调故障诊断方法存在计算量大和选取IMF分量进行特征提取需要人工干预的缺陷,本文提出了改进的EMD共振解调故障诊断方法,并将该改进方法与传统的共振解调方法和EMD共振解调方法在计算量、智能程度和有效性方面进行了对比分析,分析结果表明本文提出的改进EMD共振解调方法优于传统的共振解调法和EMD共振解调方法。
[Abstract]:Rolling bearing is one of the important parts of rotating machinery. Its working state directly determines the performance and operating condition of the mechanical system. In practical engineering practice, a slight failure of rolling bearing may lead to the shutdown of the production line, and may also damage the equipment and cause serious economic losses. Therefore, the research on fault diagnosis and prediction of rolling bearings is of great practical significance for avoiding major accidents, reforming maintenance system and promoting economic development. In this paper, the mechanical structure, vibration mechanism, fault form, cause of failure and fault characteristics of rolling bearing are introduced. The theories and methods applied in the field of fault diagnosis are studied in detail. These methods include characteristic parameter discriminant diagnosis method, resonance demodulation diagnosis method and diagnosis method based on Hilbert-Huang transform. In this paper, the vibration method is used to collect the fault signal of rolling bearing, and the field test-bed is built to collect the signal. Through the research of resonance demodulation technology and Hilbert-Huang transform, it is found that the diagnosis method based on traditional resonance demodulation technology has bandpass filter parameters (center frequency and filter bandwidth) which need to be determined and fixed in advance. With limitations and other defects, The diagnosis method based on Hilbert-Huang transform can describe the change of signal from both time scale and frequency scale at the same time, but it is difficult to observe the obvious fault characteristic signal by Hilbert spectrum and marginal spectrum. In this paper, the two diagnostic methods are combined for fault diagnosis, and the self-adaptability of EMD decomposition is used to make up for the defect that resonance demodulation technology needs fixed filter parameters. The fault information of modulation in high frequency natural vibration can be extracted by resonance demodulation technology, which makes up for the defect that HHT can not highlight the fault characteristics, and the effectiveness of this method is verified by experiments. The experimental results show that the resonance demodulation technology based on EMD can accurately and reliably diagnose the fault of rolling bearings. On this basis, in view of the shortcomings of EMD resonance demodulation fault diagnosis method, such as large computational complexity and the need of manual intervention to select IMF components for feature extraction, an improved EMD resonance demodulation fault diagnosis method is proposed in this paper. The improved method is compared with the traditional resonance demodulation method and the EMD resonance demodulation method in terms of computation, intelligence and effectiveness. The analysis results show that the improved EMD resonance demodulation method is superior to the traditional resonance demodulation method and the EMD resonance demodulation method.
【学位授予单位】：上海师范大学
【学位级别】：硕士
【学位授予年份】：2011
【分类号】：TH133.33;TH165.3

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