面向设备状态监测的可视化技术综述

doi:10.11896/jsjkx.210900167

摘要/Abstract

摘要： 随着传感器和数字化技术的发展,越来越多的设备和生产环境装配了传感器和相应的信息系统,这些传感器收集并传输了大量有价值的数据。面向设备状态监测的可视化技术,一方面可以整合操作人员的专业经验,客观评估设备的运行状态;另一方面能直观地解释数据模型的结果,以便对数据进行人机协同的智能分析。文中综述了数据可视化在设备状态监测中的相关研究,首先根据数据特征将设备状态监测数据分为网络数据、时空数据、多维数据和统计数据;然后在总结该场景的通用可视化流程的基础上,归纳出4类分析任务,即状态监测、相关性分析、异常原因推理、状态预测,针对每一类分析任务,归纳其中所用的可视化技术;最后,对设备状态监测可视化面临的挑战以及未来发展趋势进行总结和展望。

关键词: 可视化技术, 设备状态监测, 相关性分析, 异常原因推理, 预测分析

Abstract: With the development of sensors and digital technology,more and more equipment and production environment are equipped with sensors and corresponding information systems.These sensors collect and transmit a lot of valuable data.The visua-lization technology for equipment condition monitoring,on the one hand,can integrate the professional experience of operators to objectively evaluate the operation status of the equipment,on the other hand,can intuitively explain the results of the data model,so as to carry out intelligent analysis of human-computer cooperation on the data.This paper summarizes the related research work of data visualization in equipment condition monitoring,and summarizes the general visualization process for equipment condition monitoring.Firstly,according to the data characteristics,the equipment condition monitoring data is divided into network data,spatiotemporal data,multidimensional data and statistical data.Then,on the basis of summarizing the general visualization process of the scene,four kinds of analysis tasks are summarized:condition monitoring,correlation analysis,abnormal reason reasoning and condition prediction.For each kind of analysis task,the visualization technology is summarized.Finally,the challenges and future trends of equipment condition monitoring visualization are summarized and prospected.

Key words: Abnormal reason reasoning, Correlation analysis, Equipment condition monitoring, Forecast analysis, Visualization technology

中图分类号:

TP391

杨啸, 王翔坤, 胡浩, 朱敏. 面向设备状态监测的可视化技术综述[J]. 计算机科学, 2022, 49(7): 89-99. https://doi.org/10.11896/jsjkx.210900167

YANG Xiao, WANG Xiang-kun, HU Hao, ZHU Min. Survey on Visualization Technology for Equipment Condition Monitoring[J]. Computer Science, 2022, 49(7): 89-99. https://doi.org/10.11896/jsjkx.210900167

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