矿用输送带纵向撕裂故障在线监测及系统关键问题的研究

Belt conveyor transportation is the main transportation mode in coal production. Mine conveyor belts often have surface faults, such as longitudinal tear, which will cause accidents of belt broken or longitudinal tear for lacking the detection and treatment in time. Key problems of on-line monitoring and system for longitudinal tear fault of mine conveyor belt are studied in this project. The theory and method of mine conveyor belts surface fault are mainly studied. Surface fault on-line monitoring method is proposed, which can be used to improve detection accuracy, reliability and real-time capability. A fast processing algorithm to deal with the conveyor belt surface image in real time is put forward to improve the image quality and processing speed. Feature extraction, recognition and positioning algorithms are proposed to achieve automatic identification and location of the longitudinal tear fault. The longitudinal tear fault on-line monitoring system of the mine conveyor belt is developed, which implement remote transmission of surface images, real-time processing, and surface fault online monitoring. The experimental platform is built to verify the correctness and validity of the algorithms and methods. The results of the project can monitor the conveyor belt state, identify longitudinal tear surface fault automatically, and alarm in time to provide the stopping signal. Safety accidents, casualty and economic losses can be avoided, and the production efficiency can be improved. Thus, it has wide application prospect in coal and mine.

带式输送机运输是煤矿生产中主要运输方式，其使用的矿用输送带经常产生纵向撕裂等表面故障，由于得不到及时检测和处理，会造成重大断带或纵向撕裂安全事故。本项目是对矿用输送带纵向撕裂故障在线监测及系统关键问题进行研究。重点研究矿用输送带表面故障的在线监测理论与方法，提出表面故障在线监测方法，提高检测的准确性、可靠性和实时性；提出输送带表面图像的快速处理算法，提高图像的质量和处理速度，实现图像的实时处理；提出输送带纵向撕裂故障图像的特征提取和识别与定位算法，实现对纵向撕裂故障的自动识别和定位；研制矿用输送带纵向撕裂故障在线监测系统，实现对表面图像远程传输、实时处理，表面故障的在线监测；搭建实验平台，验证算法、方法的正确性和有效性；项目成果能在线监测输送带状态，自动识别纵向撕裂等表面故障，及时报警，给出停机信号，避免安全事故的发生，人员伤亡和经济损失，提高生产效率，在煤炭、矿山等领域有广泛应用前景。

带式输送机是煤矿生产中主要运输方式，其使用的矿用输送带经常产生纵向撕裂等表面故障，由于得不到及时检测和处理，会造成重大断带或纵向撕裂安全事故。本项目对矿用输送带纵向撕裂故障在线监测及系统关键问题进行了研究。重点研究了矿用输送带表面故障的在线监测理论与方法，提出了一种基于单个线阵CCD摄像机和线性LED光源的矿用输送带表面故障在线监测方法，实现了表面故障的在线监测，提高了检测的准确性、可靠性和实时性；提出了矿用输送带表面图像的快速处理算法，实现了对输送带运行图像的实时处理，提高了图像的质量和处理速度，图像处理速度达到了480Mb/s；提出了输送带纵向撕裂故障图像的特征提取和识别与定位算法，实现了对纵向撕裂的自动识别和定位，提高了检测的准确性和实时性；提出了基于机器视觉的矿用输送带跑偏的特征提取和识别检测方法，实现了对输送带跑偏故障的检测；提出了基于机器视觉的矿用输送带表面破损特征提取和故障识别算法，实现了对表面损伤进行检测；研制了一种单个线阵CCD摄像机的矿用输送带纵向撕裂故障在线监测系统，图像分辨率为1mm×1mm，采用单个CCD摄像机在满足物距不少于1.2m情况下输送带最大宽度为2.4m，最大运行速度为6m/s，实现了对表面图像远程传输、实时处理，表面故障的在线监测，能够及时报警，并给出停机信号；搭建了实验平台，验证了算法、方法的正确性和有效性。共申请专利9项，获发明专利4项，实用新型专利3项；发表学术论文21篇，其中SCI、EI检索8篇；培养青年教师8人，博士后1人，博士7人，硕士17人。项目成果能在线监测输送带状态，自动识别纵向撕裂等表面故障，及时报警，给出停机信号，避免安全事故的发生，人员伤亡和经济损失，提高了生产效率，广泛的应用于煤炭、矿山、电力、港口和化工等领域。