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Exploring Equipment Electrocardiogram Mechanism for Performance Degradation Monitoring in Smart Manufacturing

机译:探索智能制造中性能下降监测的设备心电图机制

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摘要

Similar to the use of electrocardiogram (ECG) for monitoring heartbeat, this article proposes an equipment electrocardiogram (EECG) mechanism based on fine-grained collection of data during the entire operating duration of the manufacturing equipment, with the purpose of the EECG to reveal the equipment performance degradation in smart manufacturing. First, the system architecture of EECG in smart manufacturing is constructed, and the EECG mechanism is explored, including the granular division of the duration of the production process, the matching strategy for process sequences, and several important working characteristics (e.g., baseline, tolerance, and hotspot). Next, the automatic production line EECG (APL-EECG) is deployed, to optimize the cycle time of the production process and to monitor the performance decay of the equipment online. Finally, the performance of the APL-EECG was validated using a laboratory production line. The experimental results have shown that the APL-EECG can monitor the performance degradation of the equipment in real-time and can improve the production efficiency of the production line. Compared with a previous factory information system, the APL-EECG has shown more accurate and more comprehensive understanding in terms of data for the production process. The EECG mechanism contributes to both equipment fault tracking and optimization of production process. In the long run, APL-EECG can identify potential failures and provide assistance in for preventive maintenance of the equipment.
机译:类似于心电图(ECG)进行监测心跳,本文提出了一种基于制造设备的整个操作持续时间的细粒度数据集电池的设备心电图(EECG)机制,以EECG为目的揭示智能制造中的设备性能下降。首先,构建智能制造中EECG的系统架构,探索EECG机制,包括生产过程持续时间的粒度分割,处理序列的匹配策略以及几个重要的工作特征(例如,基线,容差和热点)。接下来,部署自动生产线EECG(APL-EECG),以优化生产过程的循环时间,并监控在线设备的性能衰减。最后,使用实验室生产线验证APL-EECG的性能。实验结果表明,APL-EECG可以实时监测设备的性能下降,可以提高生产线的生产效率。与以前的工厂信息系统相比,APL-EECG在生产过程的数据方面显示了更准确和更全面的理解。 EECG机制有助于设备故障跟踪和生产过程的优化。从长远来看,APL-EECG可以识别潜在的故障,并为设备的预防性维护提供帮助。

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