【作者】 吴勇军；

【导师】 陈恩利； 申永军；

【作者基本信息】 石家庄铁道学院 ， 机械设计及理论， 2008， 硕士

【摘要】 齿轮系统作为现代工业系统中必不可少的机械传动装置,具有传递动力大,传递运动准确,传动平稳等诸多优点。随着现代机械设备向着大型化、高效率、高强度、自动化及高性能方向发展,作为传递运动和动力的齿轮装置发挥着越来越重要的作用。然而,由于其自身结构复杂,工作环境恶劣等原因,齿轮及齿轮箱都容易受到损害和出现故障。因此,采用先进的技术对齿轮及齿轮箱进行状态监测与故障诊断,可实现由事后维修、定期检修到视情维修的根本转变,减少不必要的损失,从而创造更大的经济效益和社会效益,具有重大的意义。本文针对齿轮箱常见故障,分析其典型故障机理及振动特性,重点研究了两种故障特征提取技术,包括基于重构吸引子轨迹矩阵的改进奇异值分解技术和基于改进奇异值分解技术与盲信号分离相结合的故障诊断新技术,并将这两种方法用于实测故障信号的分析中,其结果为齿轮系统故障诊断提供了新的思路。本文的主要研究内容及结论如下：(1)改进了现有奇异值分解技术。第二章在详细研究基于重构吸引子轨迹矩阵的奇异值分解技术基本原理的基础上,引入自相关分析,改进了原有算法,使其更加科学合理。数值仿真试验及实测数据分析表明：改进后的奇异值分解技术能够成功地提取强噪声背景下的调制故障信息,对齿轮系统故障诊断具有重要意义。(2)研究了盲信号分离的基本原理及应用。第三章详细推导了以JADE法为代表的批处理方法、以Infomax为代表的自适应算法及以FastICA为代表的固定点算法,并分析各自特点。数值仿真试验表明：以JADE法及FastICA法为代表的盲信号分离技术在多路混合信号中进行源分离是很有效的,这为齿轮系统故障诊断提供了新的思路。(3)进行齿轮箱故障诊断实验。第四章根据现有实验条件,设计了实验方案,对原有齿轮箱故障实验台进行改进,设置典型故障类型进行了相关故障诊断实验。(4)提出基于改进的奇异值分解技术与盲信号分离相结合的故障诊断新方法。第五章首先利用奇异值分解技术对实测信号降噪处理,再利用盲信号分离技术对降噪后信号进行盲源分离,通过奇异值分解技术与盲信号分离技术相结合,成功地分离出实测信号中的典型故障信号,其故障特征与实验设置故障完全吻合。同时,本实验数据的分离结果表明：JADE法与FastICA算法都能取得很好的分离效果。最后,第六章对本文所取得的研究成果进行了总结,并指出了若干值得进一步研究的方向。更多还原

【Abstract】 The gear system, which is considered as the necessary mechanical transmission equipment in modern industry, has many advantages such as large driving force, exact transferring motion, smooth transmission and so on. Gear system is becoming more and more important in transferring power and motion along with the development of large-scale, high efficiency and high strength equipments. However, the gears and gear boxes would be damaged and become failure easily because of its complex structure and bad running conditions. Accordingly, it is significant to research on condition monitoring and fault diagnosis for gear system by advanced technologies, which could not only change the current postmortem servicing and regular examining to repairing according to the specific state, but also bring more economical and social benefit.This thesis is focused on the typical fault mechanism and vibration characteristic of gear system. It is mainly including two fault feature extracting techniques, one of which is the improved algorithm of the singularity value decomposition (SVD) about track matrix of attractor reconstructed by time series, and the other is the new method of using both the SVD algorithm and the blind signal separation (BSS) algorithm. Both the methods mentioned above are used in extracting the fault features, which could provide a new thought for faults diagnosis of the gear system.The main research contents and the key conclusions are shown as follows:(1)The existing singularity value decomposition (SVD) algorithm is improved in the second chapter. After studying the fundamental of the SVD about track matrix of attractor reconstructed by time series, the autocorrelation analysis is introduced, which improved the current algorithm and made it more logical. The analysis of the simulation signals and the measured signals from gear box shows that it is successful for the improved SVD algorithm in extracting modulated signals mixed by strong noises, which is significant to the gear system fault diagnosis.(2)The third chapter is mainly on the fundamental and application of the BSS algorithm. Firstly, the batch-processing algorithm such as JADE, the self-adaptive algorithm such as Infomax and the fixed-point algorithm FastICA are researched in detail. And their characteristics are also researched. The simulation shows that both the JADE algorithm and the FastICA algorithm are effective in separating source signals from multi-signals, which provides a new way for the gear system fault diagnosis.(3)The experiment for gear box faults diagnosis is carrying out in the fourth chapter. According to the laboratorial condition, some works are done, including designing the experimentation, improving the former gear box faults diagnosis platform, and carrying out the experiment with different diagnosis styles.(4)The fifth chapter is focused on a new diagnosis technology based on both SVD and BSS. The signal-to-noise-ratio (SNR) of the measured signal is enhanced by the SVD algorithm at first. Then the signals after processing are separated by the BSS algorithm. The separation of typical diagnosis signals is quite successful by using the new method. The characteristic of the separated signals is quite accordant with the setting faults. At the same time, the separated results show that both the SVD algorithm and the BSS algorithm can do well in processing measured signals.At last, the main results of this dissertation are summarized in the last chapter, and some possible valuable research directions are also pointed out.更多还原