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基于视觉和内部传感的机电系统运行状态监控技术的研究-机械工程专业论文.docx

发布:2019-03-26约6.96万字共69页下载文档
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基于视觉和内部传感的机电系统运行状态监控技术的研究 THE RESEARCH ON MONITORING TECHNIQUE OF RUNNING STATES OF ELECTRO-MECHANICAL SYS- TEMS BASED ON VISION AND INTERNAL SENSORS ABSTRACT With the continuous development of modern production and the continuous progress of science and technology, the electro-mechanical equipments are becoming large-scale, high-speed and precise, which makes the degree of automation of production systems increasingly higher, and the mutual coupling effects between the subsystems more stronger, resulting that the possibility of faults of electro-mechanical systems is greatly increased, and the approach of faults appear becomes more complex. In order to avoid unnecessary economic losses and casualties, the state monitoring and fault diagnosis technology has been widely used in various fields. In this thesis, the research on monitoring technique of running states of electro-mechanical systems based on vision and internal sensors has been studied in depth, the major research contents are as follows: This thesis analyzes the situation of study and development trend of state monitoring and fault diagnosis technology, machine vision technology, information fusion technology and petri net theory both at home and abroad, and proposes the monitoring method of running states of electro-mechanical systems based on vision and internal sensors. This thesis analyzes the states and faults of the electro-mechanical systems in detail, an approach to represent the state information with code is adopted, every code corresponds to a running state, and it can determine whether the running state is normal by monitoring the change of the coded information. The thesis establishes a visual monitoring system on the packing production line of manganese additives, describes the workflow of the production line, and analyzes the faults in every monitoring location. The thesis researches the visual attention mechanisms. An algorithm of image preprocessing based on Itti’s visual attention model
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