基于多尺度leaders多重分形与多尺度约束PCA的汽车起重机主泵特征提取方法研究

项目名称： 基于多尺度leaders多重分形与多尺度约束PCA的汽车起重机主泵特征提取方法研究

项目编号： No.51205371

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 机械工程学科

项目作者： 杜文辽

作者单位： 郑州轻工业学院

项目金额： 25万元

中文摘要： 汽车起重机广泛应用于基础建设项目，工作环境恶劣，是事故率最高的特种机械设备之一。柱塞泵是起重机液压系统的关键部件，及时、准确地对其故障进行诊断是设备安全可靠运行的重要保障，而提取有效反映设备运行状态的特征是准确诊断的关键。本课题以汽车起重机主泵振动信号为研究对象，鉴于其典型的非平稳、非线性特性，提出基于多尺度分解系数leaders的多重分形特征提取方法。从基本的小波leaders出发，进一步提出频带自适应的经验模式分解和小波包分解系数leaders的多重分形特征提取模型。在利用多尺度分析刻画信号局部特性的同时，结合机械设备故障信息的先验知识，提出多尺度带约束主成分分析特征降维概念，建立对应的特征空间投影框架，开发基于奇异值分解和机器学习算法的多尺度带约束主成分分析特征降维算法，消除特征中的冗余信息，突出有效特征。为提高汽车起重机主泵故障诊断的水平提供进一步的理论和技术支持。

中文关键词： 柱塞泵；多重分形；特征提取；特征降维；故障诊断

英文摘要： Truck cranes are widely used in numerous infrastructure projects. Because of the severe working condition, truck crane is one type of special mechanical equipments with a highest accident rate. Plunger pump is a key assembly of truck crane, and promptly and accurately dealing with the equipment breakdown is very important in terms of reliability and downtime decreasing. Extracting the features relevant to the equipment conditions from mechanical signals including abundant running information is crucial to fault diagnosis. This project takes the vibration signal of the plunger pump in truck crane as study object. Because the signal has a typical non-linear and non-stationary character, a novel multifractal features extraction method based on multiscale decomposition leaders is proposed. We begin with the wavelet leaders, and then the research is deeply into the frequency band adaptive decomposition, including empirical mode decomposition (EMD) and wavelet packet decomposition, respectively. Based on the capacity of intensive depiction of signals' local characteristics, and the fault diagnosis prior knowledge of mechanical equipment, a multiscale constrained principal component analysis (MCPCA) based conception of features dimensionality reduction is proposed, and a corresponding unified projection frame is estab

英文关键词： Plunger pump；Multifractal analysis；Features extraction；Attribute reduction；Fault diagnosis