【作者】 胡超；

【导师】 于刚；

【作者基本信息】 哈尔滨工业大学 ， 机械电子工程， 2015， 硕士

【摘要】 在生产生活中位于关键环节的旋转机械,人们对它们的可靠性要求非常高,实现对其进行状态监测和实时快速故障诊断就变得十分必要和重要。旋转机械故障诊断多以设备运行过程中产生的振动信号为载体,通过信号处理方法,得到感兴趣的信号与信息。在旋转机械运行过程中,由不同的振动源激励产生的振动信号经过系统的混合作用产生传感器的观测信号。寻找相关的方法将表征故障类型的信息从观测信号中分离或提取出来就成为旋转机械混合故障诊断需要完成的任务。本文以不同类型的混合故障为研究重点,将主要分析旋转机械中振动信号的混合形式,根据齿轮啮合振动模型和轴承的故障响应模型,建立旋转机械混合故障仿真振动信号,使用独立分量分析(ICA)方法对仿真振动信号进行诊断分析,分离出每一个故障振动信号。通过对比分析仿真振动信号的分离结果,说明了ICA方法在混合故障诊断方面的不足与局限性。针对ICA方法在分离过程中存在的问题,考虑旋转机械中故障零部件与传感器安装状态之间的空间分布信息和故障信号本身的时频域先验信息,将不同类型的约束条件融入到传统的独立分量分析方法中,形成基于空间约束的独立分量分析与基于时间约束的独立分量分析故障信号提取方法。首先将空间约束条件引入到独立分量分析方法中,提出适用于旋转机械系统的空间约束独立分量分析(SCICA)方法;着重说明空间约束条件的获取与处理过程,以及空间约束ICA算法的实现。然后将故障信号的特征频率作为时间约束条件,将时间约束条件具体化为时间参考信号,引入基于参考信号的独立分量分析(ICA-RT)方法;同时,使用空间约束条件生成空间参考信号代替时间参考信号,提出基于空间参考信号的独立分量分析(ICA-RS)方法。在齿轮箱动态模拟系统平台上进行实验,测试不同类型的混合故障振动信号,使用基于分离思想的ICA方法和基于提取思想的SCICA、ICA-RS和ICA-RT方法对混合故障信号进行诊断,通过对比分析不同方法的诊断结果,可以看出基于约束的ICA方法成功地将故障振动信号提取了出来,并且空间约束ICA在稳定性和精确性方面更为明显突出。更多还原

【Abstract】 It’s very necessary and important to monitor the condition of rotating machinery and conduct real-time fault diagnosis, along with the requirement of reliability of some rotating machinery on vital position increasingly high. Rotating machinery fault diagnosis is mostly based on vibration signals of equipment, after some signal processing, obtaining interesting signal. During the operation process of the mechanical system, vibration signals from different sources are mixed together generating the sensor observed signals. It’s practical significant to seek for some methods to separate or extract the signals representing fault information from the observed signals.This paper starts from the point of rotating machinery multi-fault, researches the mixed mode of the rotating machinery vibration signals, and establishes the mixed fault simulation signals based on gear meshing model and bearing fault response model. We use independent component analysis(ICA) to diagnose the simulation signals, separate every fault source, and analyze the deficiency and limitation of ICA through the separated result.Aiming at the problem of the ICA method, we consider the spatial information between fault components with sensors in mechanic system and the prior information about frequency characteristics of fault signals, integrate different constrained condition into classic independent component analysis method, generate the extracted methods of the temporal constrained ICA and the spatial constrained ICA. We import the spatial constraint into ICA considering, then propose the spatially constrained independent component analysis(SCICA) method. We emphasize the acquisition and processing process of spatial constraints, and find general spatial constrained vectors. Then we use frequency characteristics of fault signals as temporal constraints, introduce the method of independent component analysis with reference(ICA-RT), converting the temporal constraints into temporal reference signals. At the same time, we also use spatial constrained vectors to generate the reference signals, propose the independent component analysis with spatial reference(ICA-RS) method.We test different kinds of multi-fault signal on the platform of gearbox dynamic system, using the separated method of ICA and the extracted methods of SCICA, ICA-RS and ICA-RT to diagnose the fault signals, conduct comparative analysis of effect between these methods, finding that the constrained ICA methods can extract the fault sources effectively and the stability and accuracy of the spatial constrained ICA methods’ diagnosis results is more outstanding.更多还原