【作者】 任全民；

【导师】 马孝江；

【作者基本信息】 大连理工大学 ， 机械电子工程， 2006， 博士

【摘要】 机械故障诊断是以机器学为基础的一门综合性技术，它的本质是机器运行状态的模式识别问题，其关键就是故障信号的特征提取与分类。论文基于国家自然科学基金项目“局域波法及其工程应用研究”(50475155)和往复式压缩机故障诊断的横向课题，以现场工作的超高压聚乙烯压缩机为研究对象，利用压缩机的振动信号实现了它关键部件的状态监测与故障诊断。本文针对往复式压缩机主要部件的故障机理及其振动信号的特征，分别引入了适合分析它们的方法——局域波法、广义分形维法和改进的小波包阈值消噪方法，以便准确地从中提取可靠的故障特征。在特征提取的基础上，对往复式压缩机在线监测和智能诊断的方法进行了研究。并在现场工作的超高压聚乙烯压缩机状态监测中验证了这些方法的有效性。本论文主要的工作如下： 1．以超高压聚乙烯C-2二次压缩机为例介绍了往复式压缩机的工作原理和结构特征，总结了往复式压缩机主要零部件的故障机理和失效形式；使用振动信号对压缩机的关键部件进行了状态监测，规划了每列气缸主要零部件测点的位置和检测参数；使用传统信号处理方法对比分析了各测点振动信号的特征，初步得到了压缩机振动信号的一些统计特征，为使用振动信号实现往复式压缩机的故障诊断奠定了基础。 2．在深入研究局域波理论基础上，结合高阶统计量的方法，提出了一种往复式压缩机气阀特征提取的新方法。建立了气阀振动信号的多调幅调频模型，利用局域波法的多分辨性和分解信号自适应性，对振动信号实现了解调分析并得到了它的特征频段；通过特征频段瞬时幅值的归一化处理，并计算包含气阀故障信息的高阶统计量参数；试验结果证实了这些特征参数很好地反映了气阀的劣化过程，对压缩机气阀的故障诊断具有很好的工程适用性。 3．实现了基于振动信号分析的填料密封故障诊断。研究了往复式压缩机填料密封的故障机理，鉴于填料函处振动信号的复杂性，传统信号处理方法无法满足故障诊断的要求，使用了多重分形理论对非线性振动信号进行了研究分析；为了能够获取淹没在噪声中微弱信号的几何结构信息，将原始信号转化为它的分贝值形式；试验的结果表明广义维数谱能够很好地反映填料密封的工作状态，该方法为往复式压缩机密封件的故障诊断提供了新的思路。 4．提出了一种往复式压缩机传动部件早期故障检测的方法。在小波包变换和阈值消噪的基础上，引进了小波包系数极大似然阈值估计的方法，并使用小波包系数的Shannon熵选择了最佳的小波包函数；该方法可以实现强噪声背景下弱冲击信号的提取，使用模更多还原

【Abstract】 Mechinery diagnosis is a branch of mechanology. Its essence is the pattern recognition of machine running condition. Its key issues are the feature extraction and classification for fault signals. The dissertation is carried out based on the project of "Research on Local Wave Method and its Engineering Application" (supported by Chinese National Nature Science Foundation, No. 50475155) and the project of research on fault diagnosis of reciprocating compressors. The working states of the secondary ethylene hypercompressor in site are studied and condition monitoring and fault diagnosis methods for reciprocating compressorss are implemented by using vibration signals. Since different components have the different failure modes and so do the characteristics of vibration signals, various modern signal processing methods, such as Local-wave method, multifractals theory, improved WPT denoising method and so on are applied to extract fault features from vibration signals with high accuracy and reliability. Based on the extracted features, the methods of intelligence diagnosis for reciprocating compressor are also studied. The validity of all these methods are tested through the secondary ethylene hypercompressors in the Low Density Polyethylene production plant. The main content is listed as follows:1. Operation and structure characteristics of reciprocating compressors are illustrated by using the C-2 secondary ethylene hypercompressor as an example. Disable modes and fault mechanism of the main components are summarized. Then vibration signals are used in reciprocating compressor condition monitoring and the location of measurement points on each cylinder is presented. The characteristics of vibration signals from all measurement points on time and frequency domain are analyzed and some statistic results are obtained. The basis of reciprocating compressor fault diagnosis by using the vibration signals is founded.2. A new approach that extracts fault features from the reciprocating compressor valves is presented after Local-wave method is deeply studied, combined with higher order statistics theory. The vibration signals from combined suction/discharge valves of the secondary ethylene hypercompressor are analyzed. According to its more AM-FMs model for vibration signals, Local-wave method is used to demodulate the vibration signal due to its multi-resolution and self-adaptation in decomposing signal adap. The instantaneous amplitude of characteristic frequency band is normalized and the higher order statistics parameters are calculated from them. The experiment results show that these parameters reflect the更多还原