工业数据可视分析综述

自 2013 年工业 4.0 的概念被提出以来，全世界的工业进程都飞速奔向智能制造时代。而数据感知技术的发展进一步帮助收集海量工业数据，工业信息化革新正是机遇。但是，工业数据具有规模大、维度高、结构多变和内容复杂的特性，对其进行分析是一项严峻的挑战。多变的应用场景又导致分析的灵活度要求提高，往往需要专家参与分析循环，因此可视化在工业数据分析中有了更广泛的应用。该综述首先按生产阶段及属性分别总结了工业场景下常用的数据类型；其次，根据数据属性，按时间、空间、时空结合分类，介绍了对应的可视化方法；再次，总结了可视分析在工业场景下的应用，并讨论如何合理地集成自动化分析方法以提高分析能力；最后，展望了工业数据可视分析的发展前景，并提出未来的研究方向。

A Review on Industrial Data Visual Analytics

Since industry 4.0 was introduced in 2013, industries across the globe have been rushing towards the era of intelligent manufacturing. The advances in data sensing technologies have further helped the collection of massive industrial data, providing an excellent opportunity for innovations in industrial informatization. However, it remains a major challenge to analyze industrial data because of its large scale, high dimensionality, heterogeneity and complexity. Constantly changing application scenarios also lead to strict requirements in the flexibility of analyses, which demand placing domain experts in the analysis loop. Therefore, visualization has been widely applied to analyzing industrial data. This review article first summarizes the data types commonly used in the industrial scenarios based on the production stages and properties. Then, based on the data properties, this paper introduces visualization methods for the temporal, spatial and spatio-temporal types. Further, this paper overviews the applications of visual analytics in the industrial scenarios and discusses the integration of automated analysis methods in the visual analytics systems. Finally, this paper prospects the development of industrial data visual analytics and possible research directions for the future.