振动信号离线分析系统及旋转机械故障诊断应用研究

发布时间：2018-07-10 13:47

本文选题：集成经验模态分解 + 本征时间尺度分解　；参考：《上海交通大学》2013年硕士论文

【摘要】：风机、压缩机和汽轮机等旋转机械设备广泛应用于现代化工业生产实践中，对这些设备开展状态监测与故障诊断工作，保障设备安全可靠的运行，具有重要的经济意义和社会意义。根据振动信号对旋转机械进行状态监测与故障诊断目前是设备管理维护的主要手段。对振动信号进行特征提取和分析，是进行准确诊断的必要前提。本文实现并改进了多种振动信号处理方法，并将其集成到《振动信号离线分析系统》之中，应用系统对仿真信号、实验室模拟信号和工程实际信号进行了全方位的分析，验证了算法的准确性和有效性。二十余种信号处理方法使得该系统功能丰富，算法上的改进使其分析效果更加理想，对提高我国旋转机械的故障诊断系统水平具有重要意义。在此基础上，，深入研究了集成经验模态分解（EEMD）和固有时间尺度（ITD）方法。EEMD是在经验模式分解（EMD）的基础上通过引入白噪声改进EMD缺点的一种新方法；ITD作为非平稳信号的有效信号处理方法，具有计算效率高等优点。EEMD和ITD两种方法都有效的解决了EMD方法的端点效应、模式混叠等缺点。本文不仅通过仿真信号对EMD、EEMD和ITD的分析效果进行了对比，总结了三种信号处理方法的优缺点，而且应用EEMD、ITD对齿轮实验台和水泥厂高温风机进行故障诊断，获取了设备的故障特征。音频信号与振动信号均包含着机组运行状态信息，两者相结合能够获取设备更加全面的信息。因此，本文在对利用振动信号对旋转机械进行故障诊断的基础上，以风力发电机组传动系统为研究对象，提出了声振耦合的分析方法。该方法的提出为旋转机械故障诊断提供了一个新的思路，将此方法应用到风力发电机组传动系统的整机评估和故障诊断中具有明显效果。
[Abstract]:Rotating machinery, such as fans, compressors and steam turbines, is widely used in modern industrial production practices. The monitoring and fault diagnosis of these equipment is carried out to ensure the safe and reliable operation of the equipment. It has important economic and social significance. State monitoring and fault diagnosis of rotating machinery based on vibration signal are the main means of equipment management and maintenance. Feature extraction and analysis of vibration signal is a necessary prerequisite for accurate diagnosis. In this paper, a variety of vibration signal processing methods are realized and improved, and integrated into the "Vibration signal Off-Line Analysis system". The simulation signal, the laboratory analog signal and the engineering actual signal are analyzed in all directions by the application of the system. The accuracy and validity of the algorithm are verified. More than 20 kinds of signal processing methods make the system rich in function, and the improvement of algorithm makes the analysis effect more ideal. It is of great significance to improve the level of fault diagnosis system of rotating machinery in China. On this basis, the methods of integrated empirical mode decomposition (EEMD) and inherent time scale (ITD) are studied. EEMD is a new method to improve the shortcomings of EMD by introducing white noise on the basis of empirical mode decomposition (EMD). ITD, as an effective signal processing method for non-stationary signals, has the advantages of high computational efficiency. Both EEMD and ITD can effectively solve the endpoints effect and mode aliasing of EMD. This paper not only compares the analysis results of EMDEEMD and ITD by simulation signal, summarizes the advantages and disadvantages of three signal processing methods, but also applies EEMD-ITD to fault diagnosis of gear test table and high temperature fan in cement plant. The fault characteristics of the equipment are obtained. Both audio signal and vibration signal contain the operation state information of the unit, and the combination of the two signals can obtain more comprehensive information of the equipment. Therefore, on the basis of fault diagnosis of rotating machinery using vibration signals, this paper presents an analysis method of acoustic-vibration coupling based on wind turbine transmission system. This method provides a new idea for the fault diagnosis of rotating machinery, and the application of this method to the whole machine evaluation and fault diagnosis of wind turbine transmission system has obvious effect.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TH165.3

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