Big data and reliability applications: The complexity dimension
Big data and reliability applications: The complexity dimension
复制标题
DOI：
10.1080/00224065.2018.1438007
复制
发表时间：
2018-01-01
影响因子：
2.5
通讯作者：
Meeker, William Q.
中科院分区：
文献类型：
--
作者：
Hong, Yili;Zhang, Man;Meeker, William Q.
大数据不仅数据量大，而且数据结构复杂。复杂性给大数据分析带来了独特的挑战。Meeker和Hong(2014；Quality Engineering，pp.102-16)广泛讨论了大数据和可靠性方面的机遇和挑战；他们还描述了生成可用于可靠性分析的大数据的工程系统。Meeker和Hong(2014)专注于大规模系统运行和环境数据(即高频多变量时间序列数据)，并提供了如何将这些数据作为协变量与传统可靠性响应(如故障发生时间、事件再次发生时间和退化测量)联系起来的例子。本文旨在通过关注如何使用具有复杂结构的数据来进行可靠性分析来扩展这一讨论。这些数据类型包括高维传感器数据、功能曲线数据和图像流。我们首先回顾了这些方面的最新发展，然后讨论了如何开发分析方法来解决可靠性应用中大数据的复杂特征所产生的具有挑战性的方面。还将讨论现代统计方法的使用，如变量选择、函数数据分析、图像标量回归、时空数据模型和机器学习技术。
Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges on big data analytics. Meeker and Hong (2014; Quality Engineering, pp. 102-16) provided an extensive discussion of the opportunities and challenges on big data and reliability; they also described engineering systems which generate big data that can be used in reliability analysis. Meeker and Hong (2014) focused on large-scale system operating and environment data (i.e., high-frequency multivariate time series data) and provided examples on how to link such data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. This article intends to extend that discussion by focusing on how to use data with complicated structures to do reliability analysis. Such data types include high-dimensional sensor data, functional curve data, and image streams. We first provide a review of recent developments in those directions, then we provide a discussion on how analytical methods can be developed to tackle the challenging aspects that arise from the complex features of big data in reliability applications. The use of modern statistical methods such as variable selection, functional data analysis, scalar-on-image regression, spatio-temporal data models, and machine-learning techniques will also be discussed.