面向服务的全断面掘进机生命周期健康管理技术研究

发布时间：2018-05-06 13:14

  本文选题：全断面掘进机 + 生命周期　； 参考：《东北大学》2014年博士论文

【摘要】：作为一种典型的复杂装备,全断面掘进机在我国基础设施建设、城市现代化进程等方面,尤其是城市地铁和海底隧道的建设中占据着无可替代的地位,研究全断面掘进机的健康管理技术对于提高其自身的安全性、可靠性及有效性,降低施工成本,保障施工安全,提高我国装备制造业水平都具有重要意义。但全断面掘进机复杂的组成、结构和工作环境造成了其故障多发性的特点,传统的健康管理技术和服务方式越来越难以满足全断面掘进机诊断维护的需求。对于使用者来说,如何提高和保障全断面掘进机的工作可靠性与掘进高效性是一个亟待解决的问题；而对于其制造企业来说,全断面掘进机质量的竞争焦点集中到了设计开发阶段和售后服务阶段,为客户提供高可靠性的产品和迅速有效的健康管理服务是全断面掘进机制造企业所面临的重要问题。基于上述动机,本文从产品生命周期的角度考虑全断面掘进机的健康管理知识的获取问题,以全断面掘进机国家重点实验室建设为背景,研究全断面掘进机生命周期内的健康管理技术及其应用,论文的主要成果与创新包括：(1)分析了健康管理服务模式的转变以及健康管理在全断面掘进机生命周期中的角色,提出了面向服务的全断面掘进机生命周期的健康管理理念。从功能结构、概念模型、总体结构、逻辑结构四个方面对面向服务的全断面掘进机生命周期健康管理系统的体系结构进行了详细设计。(2)分析了全断面掘进机状态监测与预测评估的主要方法,设计了基于虚拟仪器的全断面掘进机状态监测系统。建立了基于灰色预测和神经网络的全断面掘进机运行状态预测评估模型,通过推进系统液压油温度变化预测和主轴承轴瓦振动幅值变化预测,初步实现了全断面掘进机关键部件运行状态的评估。(3)应用粗糙集和决策树技术建立了基于数据挖掘的全断面掘进机故障诊断模型。通过粗糙集理论选择全断面掘进机故障特征参数,经过数据预处理,由改进的C4.5算法进行决策。最后,采用Clementine技术实现了诊断模型。诊断模型的实现,为故障维护决策支持奠定了基础。(4)分析了全断面掘进机刀具的磨损形式和磨损因素,建立了基于Markov链的刀具健康管理模型。通过实际掘进数据,研究了识别刀具磨损的判定依据；通过工业实验手段,探究了提高掘进效率的全断面掘进机工作参数之间的关系。(5)分析了全断面掘进机维修任务指派问题的分类、特点和性能指标分类方法,建立了基于FAHP-匈牙利混合算法的全断面掘进机故障任务指派模型,得到的结果既考虑了决策者的主观因素,又使得掘进机维修时间、维修成本和维修后循环等达到最优。(6)在理论研究的基础上,以全断面掘进机国家重点实验室的设备健康管理建设项目为依托,研究了面向服务的全断面掘进机生命周期健康管理系统的实施策略,构建了基于SOA的全断面掘进机健康管理原型系统。详细介绍了全断面掘进机健康管理系统的设计,对部分关键功能模块进行了原型系统开发,并给出了系统运行示例,验证了上述理论研究的正确性与技术的可行性。
[Abstract]:As a typical complex equipment, the full section roadheader occupies an irreplaceable position in the construction of infrastructure and the process of urban modernization in China, especially in the construction of urban subway and undersea tunnel. The study of the health management technology of full section roadheader to improve its safety, reliability and effectiveness is reduced. Construction cost, security of construction and improving the level of equipment manufacturing in China are of great significance. However, the complex composition, structure and working environment of the full section roadheader have caused the multiple characteristics of the fault. The traditional health management technology and service mode are more and more difficult to meet the needs of the diagnosis and maintenance of the full section tunnelling machine. How to improve and guarantee the working reliability and high efficiency of the full section roadheader is an urgent problem to be solved. For its manufacturing enterprises, the focus of the quality of the full section roadheader is focused on the design and development stage and the after-sales service stage, providing customers with high reliability products and rapid and effective health. Health management service is an important problem in the manufacturing enterprise of full section roadheader. Based on the above motive, this paper considers the problem of obtaining the health management knowledge of the full section roadheader from the point of view of the life cycle of the product, and studies the health tube in the life cycle of the full face roadheader in the background of the construction of the National Key Laboratory of the full section roadheader. The main achievements and innovations of the paper are as follows: (1) the transformation of health management service mode and the role of health management in the life cycle of full section roadheader are analyzed. The health management concept of the life cycle of full section roadheader facing service is put forward. The function structure, conceptual model, overall structure, logic junction are made. The architecture of the life cycle health management system for full section roadheader to service is designed in detail in four aspects. (2) the main methods of the state monitoring and prediction evaluation of the full section roadheader are analyzed, and the state monitoring system based on the virtual instrument is designed. Based on the grey prediction and neural network, the system is designed. The operation state prediction model of the full section roadheader is used to predict the hydraulic oil temperature change prediction and the vibration amplitude change of the spindle bearing bush. The evaluation of the key components of the full section roadheader is preliminarily realized. (3) the full section roadheader based on data mining is established by using the rough set and the decision tree technology. The fault diagnosis model is selected by rough set theory. After data preprocessing, the improved C4.5 algorithm is used to make decision. Finally, the diagnosis model is realized by Clementine technology. The realization of the diagnosis model has laid the foundation for the decision support of the fault maintenance. (4) analysis of the wear and tear of the cutting tool for the full section boring machine. Form and wear factors, the tool health management model based on Markov chain is set up. Through the actual driving data, the identification basis of tool wear identification is studied. Through industrial experiment, the relationship between the working parameters of the full section roadheader is explored. (5) the assignment problem of the full section roadheader maintenance task is analyzed. Classification, characteristics and performance index classification methods, the fault assignment model of full section roadheader based on FAHP- Hungarian hybrid algorithm is established. The results not only consider the subjective factors of the decision-makers, but also make the time of maintenance, maintenance cost and post maintenance cycle optimal. (6) on the basis of theoretical research, it is based on the theoretical research. Based on the equipment health management construction project of the State Key Laboratory of the section roadheader, the implementation strategy of the life cycle health management system for the full section roadheader is studied. The SOA based health management prototype system of the full section roadheader is constructed. The design of the health management system of the full section roadheader is introduced in detail. The prototype system is developed based on the key function modules, and an example of the system operation is given to verify the correctness and feasibility of the theoretical research.

【学位授予单位】：东北大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：U455.3
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本文编号：1852451