Deep-learning-based multistate monitoring method of belt conveyor turning section

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Mengchao Zhang, Kai Jiang, Shuai Zhao, Nini Hao, Yuan Zhang
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引用次数: 0

Abstract

During transportation, bulk materials are susceptible to spillage due to equipment instability and environmental factors, resulting in increased maintenance costs and environmental pollution. Thus, intelligent and efficient condition monitoring is crucial for maintaining operational efficiency of transfer equipment. It facilitates the timely identification of potential safety hazards, preventing accidents from occurring or their impact from spreading, thereby minimizing production and maintenance costs. This study presents a deep-learning-based multioperation synchronous monitoring method suitable for belt conveyors that integrate target segmentation and detection networks to simultaneously diagnose belt deviation, measure conveying load, identify idlers, and do other tasks on a self-made dataset. This method effectively reduces the complexity of multistate simultaneous monitoring and monitoring costs, thereby avoiding environmental pollution caused by transportation accidents. Experimental results show that the segmentation accuracy of the proposed method can be up to 88.72%, with a detection accuracy of 91.3% and an overall inference speed of 90.9 frames per second. Furthermore, by extending the dataset, the proposed method can incorporate additional tasks, such as belt damage, scattered material, and foreign object identifications. This study has practical significance in ensuring the normal and eco-friendly operation of bulk material transportation. Our source dataset is available at https://github.com/zhangzhangzhang1618/dataset-for-turnning-section
基于深度学习的带式输送机转弯段多状态监测方法
在运输过程中,由于设备不稳定和环境因素,散装物料容易发生溢出,造成维护成本增加和环境污染。因此,智能、高效的状态监测对于维持输送设备的运行效率至关重要。它有助于及时识别安全隐患,防止事故的发生或影响的蔓延,从而最大限度地降低生产和维护成本。本研究提出了一种基于深度学习的带式输送机多工况同步监测方法,该方法将目标分割和检测网络相结合,在自制数据集上同时进行带偏差诊断、输送负荷测量、托辊识别等任务。该方法有效降低了多态同时监测的复杂性和监测成本,从而避免了交通事故对环境的污染。实验结果表明,该方法的分割准确率可达88.72%,检测准确率为91.3%,整体推理速度为90.9帧/秒。此外,通过扩展数据集,该方法可以纳入额外的任务,如带损坏,散落材料和异物识别。本研究对保证散料运输的正常、环保运行具有现实意义。我们的源数据集可在https://github.com/zhangzhangzhang1618/dataset-for-turnning-section上获得
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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