Zhang Xiangxiang, Hu Wenkai, Yang Fan
School of Automation, China University of Geosciences, Wuhan 430074, China.
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China.
Entropy (Basel). 2022 Jan 28;24(2):212. doi: 10.3390/e24020212.
Causality inference is a process to infer Cause-Effect relations between variables in, typically, complex systems, and it is commonly used for root cause analysis in large-scale process industries. Transfer entropy (TE), as a non-parametric causality inference method, is an effective method to detect Cause-Effect relations in both linear and nonlinear processes. However, a major drawback of transfer entropy lies in the high computational complexity, which hinders its real application, especially in systems that have high requirements for real-time estimation. Motivated by such a problem, this study proposes an improved method for causality inference based on transfer entropy and information granulation. The calculation of transfer entropy is improved with a new framework that integrates the information granulation as a critical preceding step; moreover, a window-length determination method is proposed based on delay estimation, so as to conduct appropriate data compression using information granulation. The effectiveness of the proposed method is demonstrated by both a numerical example and an industrial case, with a two-tank simulation model. As shown by the results, the proposed method can reduce the computational complexity significantly while holding a strong capability for accurate casuality detection.
因果关系推断是一个在通常复杂的系统中推断变量之间因果关系的过程，它常用于大规模流程工业中的根本原因分析。转移熵（TE）作为一种非参数因果关系推断方法，是检测线性和非线性过程中因果关系的有效方法。然而，转移熵的一个主要缺点在于计算复杂度高，这阻碍了其实际应用，特别是在对实时估计有高要求的系统中。受此问题的启发，本研究提出了一种基于转移熵和信息粒化的改进因果关系推断方法。通过一个将信息粒化作为关键前置步骤的新框架改进了转移熵的计算；此外，基于延迟估计提出了一种窗口长度确定方法，以便使用信息粒化进行适当的数据压缩。通过一个数值示例和一个工业案例，使用双罐模拟模型证明了所提方法的有效性。结果表明，所提方法在保持强大的准确因果关系检测能力的同时，可以显著降低计算复杂度。
