摘要：AbstractIn terms of an alarm system, the propagation of a fault is identified as the main reason for low efficiency and the leading cause of dramatic industrial accidents. Thus, tracing the root causes of faulty conditions that lead to alarm floods is necessary. For root cause tracing, a widely accepted method is to characterize the process by causality at first and then trace the root causes. This work focuses on the former part. The conventional techniques to deal with causal analysis of industrial processes have difficulty in handling the nonlinearity of variables, obtaining accurate probability density and time lag, etc. In this work, a novel causal network construction method based on convergent cross mapping (CCM) that accurately describe process causality was proposed to deal with the above problems. First, the original monitoring variables were determined by a maximum Lyapunov method to determine whether they were chaotic time series, which aims to judge whether the application conditions of CCM can be satisfied. Then, some characteristic variables are selected from original variables through data preprocessing and descending dimension methods, which are defined as nodes that constitute the causal network. Second, the CCM-based methods are used to identify the causal direction and indirect causal relationship between variables, so as to construct the structure of the causal network. Since the CCM based on deterministic systems theory, it can handle nonlinearity and does not rely on the sample distribution. Finally, the weight of the edges in the graph is calculated to obtain the causal network which describes the process causality and serves as the basis for subsequent root causes tracing of alarms. The effectiveness of the proposed method is illustrated via a real industrial case study.
