本项目主要迎合工业4.0时代众实际系统具有的大数据特征（如数据高维、数据高容量以及数据结构特征多变等），对传统的数据驱动的故障检测方法（如PCA、PLS）作出重要改进与提升，使它们具有可扩展、性能一致以及持续学习的能力。本项目紧紧围绕上述要求，凝练出四个新颖的问题：高维数据及特征多变导致的变量选择问题、给定检测性能下的最少数据问题、监控变量关系的欠估计或过估计问题、以及对数据结构特征的持续学习问题。基于PCA及PLS方法，本项目将针对上述问题提出具有冲突调整能力的故障检测方法、最少数据驱动的故障检测方法、基于部分互信息最大化的故障检测方法、以及对数据结构特征具有持续学习能力的故障检测方法。这些方法不仅丰富和提升已有的数据驱动的故障检测研究，而且能够对大数据驱动的故障检测做出创新性贡献。项目组所在的团队具有良好的工作条件和研究基础，有望为大数据驱动的故障检测带来重要进展。

Many practical systems in Industry 4.0 Era have big data characteristics such as high dimension, high volume, and variant structure properties. Starting from these characteristics, this project will improve and enhance the traditional data-driven fault detection methods including PCA and PLS. One main aim is to make PCA and PLS have the scalability, performance consensusability and continual learning ability. Focusing on the above-mentioned requirements, this project proposes four novel problems: the problem of variable selection resulting from high dimension and variant structure properties, the problem of the minimum data given the detection performance, the problem of over/under estimation of the variable relationship, and the problem of continual learning for structural property of data. Based on PCA and PLS, this project will mainly consider and resolve these problems. It will develop the fault detection approaches for adjusting conflicts, the fault detection approaches driven by the minimum data, the fault detection approaches based on the maximization of partial mutual information, and the fault detection approaches with continual learning ability for structural property of data. These approaches will improve and enhance the existing data-driven fault detection, and contribute to big data-driven fault detection. The team of the project team has good working conditions and research foundation, which is expected to bring important progress to the big data-driven fault detection.