基于量子优化和ARMA模型的齿轮箱故障诊断研究

发布时间：2019-01-12 11:59

【摘要】：齿轮箱是一种传动比恒定、传动效率高、传动比变化范围大的传动部件，被广泛应用在各种机械设备中。但因其结构复杂且工作环境恶劣因素很容易受到损害和出现故障，影响整个设备的正常运转。对齿轮箱的故障诊断对了解设备运行状态，及时发现设备异常具有着重要的现实意义。齿轮箱的振动信号中蕴含着大量信息，能够从嘈杂的振动信号中提取到包含故障特征的信号是一个关键因素。使用独立分量分析作为信号的预处理步骤，实现从众多混杂的信号中将含源的机械状态信号分离出来，基于“纯净”信号再对其进行ARMA建模分析，提取状态特征，提高故障诊断的有效性。论文分析了齿轮箱的常见失效形式，，并对齿轮和轴承失效作了重点分析。在研究了独立分量分析算法和量子粒子群算法的理论基础上，将二者相结合，通过选取一定的目标函数，将量子粒子群算法作为独立分量分析的优化算法，形成了量子粒子群优化独立分量分析算法。以JZQ250齿轮箱为实验对象，搭建了测试系统，采集了振动信号。将独立分量分析算法应用于齿轮箱的故障诊断中，取得了一定的效果。利用MATLAB软件平台开发了基于ICA算法的齿轮箱故障特征值提取软件，该软件由数据采集模块、ICA算法仿真模块、齿轮箱故障特征提取诊断模块组成，不仅模拟了信号的实时数据采集，而且成功的将ICA算法与ARMA模型用于齿轮箱的故障诊断中。
[Abstract]:Gearbox is a kind of transmission parts with constant transmission ratio, high transmission efficiency and wide range of transmission ratio. It is widely used in various mechanical equipment. However, because of its complex structure and poor working environment, it is easy to be damaged and malfunction, which affects the normal operation of the whole equipment. The fault diagnosis of gearbox is of great practical significance to understand the operation state of the equipment and find out the abnormal equipment in time. There is a lot of information in the vibration signal of the gearbox. It is a key factor to extract the signal containing the fault feature from the noisy vibration signal. Independent component analysis (ICA) is used as the signal preprocessing step to separate the mechanical state signal with source from many mixed signals. Based on the "pure" signal, it is modeled and analyzed by ARMA to extract the state feature. Improve the effectiveness of fault diagnosis. The common failure forms of gear box are analyzed, and the failure of gear and bearing is analyzed emphatically. Based on the theory of independent component analysis (ICA) and quantum particle swarm optimization (QPSO), quantum particle swarm optimization (QPSO) is used as the optimization algorithm of ICA by selecting a certain objective function. A quantum particle swarm optimization (QPSO) independent component analysis (ICA) algorithm is proposed. Taking the JZQ250 gearbox as the experimental object, the testing system was set up and the vibration signal was collected. The independent component analysis (ICA) algorithm is applied to the gearbox fault diagnosis. The software of gearbox fault eigenvalue extraction based on ICA algorithm is developed by using MATLAB software platform. The software consists of data acquisition module, ICA algorithm simulation module, gearbox fault feature extraction and diagnosis module. Not only the real-time data acquisition is simulated, but also the ICA algorithm and ARMA model are successfully used in the gearbox fault diagnosis.
【学位授予单位】：中北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TH165.3

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