【作者】 葛欣；

【导师】 丁恩杰；

【作者基本信息】 中国矿业大学 ， 通信与信息系统， 2010， 博士

【摘要】 通风机是煤矿安全生产的关键设备,担负着向井下输送新鲜空气、排出粉尘和污浊气流、确保矿井安全生产的重任。作为矿井系统的动力源,该设备如果因某种故障而停机运行,将会给整个矿山的安全生产带来巨大威胁。因此,对煤矿通风机系统进行在线监控与故障诊断的研究具有重要的理论意义和实际应用价值。本论文以地理上分散的各矿区数据作为分析对象,在研究了网格和互操作技术的基础上,提出了分布式数据管理模型,并基于其提出了通风机故障诊断系统的架构。将远程数据收集到本地,并对其进行预处理,构建动态的本地仓库,从中抽取样本集;通过对风机的振动信号进行时频分析提取故障特征,并构造出适合于多类分类支持向量机的调节参数;提出了引入模糊补偿因子的状态相似度核函数进行分析建模,将多指标特征有效融合,实现了对多故障综合情况的有效诊断,开发了基于知识的通风机故障诊断系统。论文研究内容主要包括:(1)研究了网格的体系结构和互操作的层次模型,结合煤矿生产系统的客观条件以及故障诊断系统的需求,提出了分布式环境下数据管理的模型、服务以及工作流程;并在此基础上,提出了基于分布式环境的通风机故障诊断系统的框架。将本地数据和远程数据相集成,以风机各运行状态变量为样本空间、振动信号的特征频率为参数,利用基于支持向量机的模式识别技术将多方知识融合的风机故障诊断系统的体系结构。(2)研究了结构化数据和非结构化数据的构成和映射关系,以及异构数据间模式转换方法,提出了通过XML文档直接构建数据仓库的算法,实现了远程数据的集成。并且,对基于密度估计的数据预处理技术进行分析,提出了建立在微数据集上的基于误差调整密度估计的数据处理方法,对数据仓库中的不确定数据进行了平滑估计处理。(3)比较了短时傅立叶变换、Wigner-Ville分布分析、小波变换等常用的时频分析方法;系统地研究了经验模态分解的原理,探讨了Hilbert-Huang变换中的分解停止准则、边界处理方法、包络拟合方法,通过分析验证选定相应的处理方法。针对通风机振动信号的特点,提出了基于近似熵的Hilbert-Huang变换分析方法,对仿真信号和实测信号进行故障特征提取,并与第二代小波变换相对比,验证了算法的有效性。(4)研究了支持向量机的工作原理及算法,针对直接构造多类分类支持向量机的局限性,提出一种基于状态相似度核函数的新的模糊补偿多类支持向量机方法;利用振动信号时频分析所提取的故障特征参数作为模糊补偿因子,将多特征参数有效融合,通过状态相似度的核函数进行模式识别。该论文有图52幅,表19个,参考文献151篇。更多还原

【Abstract】 Mine ventilators are the key equipments in coal mining safety production, which undertake vital responsibilities for infusing fresh air to the underground, exhausting dust and dirty flow, and ensuring the safe production of the coal mines. As ventilator is the power source of mine system, an unexpected failure of it may cause significant economic and casualty losses. Therefore, it has important theoretical significance and practical application value to the research on the on-line monitoring and fault diagnosis of mine ventilation system.Geographically distributed databases in different mining areas are taken as analysis objects in this dissertation, through which a managemnt model of distributed data is proposed based on grid and interoperability techniques and the structure of ventilator fault diagnosis system is put forward. In this structure, remote data are colleted and preprocessed to build local dynamic data warehouse, from which sample set is selected; Meanwhile fault features are extracted through time-freqency analysis on vibration signals of ventilators, so as to be adapted to adjustment parameters of multi-class classifier of support vector machine; Then the state similarity kernel function is presented and fuzzy compensation factor is introduced to analyze and model, so that multiple attributes are integrated effectively and valid diagnosis of multi-concurrent faults is realized; Finally, a knowledge-based fault diagnosis system of mine ventilator is developed.The research of this Dissertation mainly includes:(1) Grid architecture and hierarchical model of interoperability are studied, and the model, services and working process of distributed data management are put forward according to the objective conditions of coal mine production system and the requirements of fault diagnosis system. On the basis of the model, the framework of ventilator fault diagnosis system on distributed environment is proposed. Local data and remote data are combined together, operating state variables are taken as sample space and eigenfrequency of vibration signals as parameters, and multiaspect data are integrated to diagnosis by using pattern recognition technology of support vector machine.(2) Composition and mapping relation between structured data and unstructured data are studied, and the pattern transformation method among heterogeneous data is also analysed. The algorithm of building data warehouse directly by XML documents is proposed, which realizes the integration of remote data. Moreover, data pre-processing techniques dependent on density estimation is analysed, in order to put forward the data processing method based on error-adjusted density estimation and micro-dataset, which can execute smoothing estimation on uncertain data of data warehouse.(3) Some commonly used time-frequency analysis methods are compared such as short-time fourier transform, Wigner-Ville distribution and wavelet transform, and systematically research on the principle of empirical mode decomposition is also described. By analyzing and verifying some problems of Hilbert-Huang transform, such as decomposition stopping criterion, boundary treating methods and envelope fitting methods, suitable processing methods are chosen. Aiming at the characteristic of ventilator vibration signal, fault features extraction method is proposed based on approximate entropy and Hilbert-Huang transform, and applied to simulation signal and real signal. Compared with the second generation wavelet transform, experimental result demonstrates validity of this method.(4) The working principle and algorithms of support vector machines is researched, and aiming at the limitations of directly constructing multi-class classifier of support vector machine, a new fuzzy compensation multi-class support vector machine is presented based on state similarity kernel function. This method takes fault features extracted from time-frequency analysis of vibration signals as fuzzy compensation factors, integrates multiple characteristic parameters effectively, and uses state similarity kernel function to pattern recognition.In this Dissertation, there are fifty-two figures, nineteen tables, and one hundred and fifty-one reference documents.更多还原