Hazardous scenarios emerging from complex system where number of functions are large and corresponding function coupling are humongous, are very difficult if not impossible to identify humanly. Today’s complex systems generate a very large dataset every minute and dynamic nature of the generated data makes it difficult to track such couplings. The Functional Resonance Analysis Method (FRAM) got success in recent years to understand hazards emerging from function couplings in complex systems, however, challenges remain to estimate aggregated couplings appropriately without quantitative analysis. The current study developed a data-driven approach to quantify function couplings using lift confidence intervals of association rules. Later, association rules were merged to identify the paths leading to a potential hazardous scenario. The paths were presented graphically and equipped with quantified coupling information and capable of providing guidance to prevent the emerging hazard scenario. The approach has been demonstrated with a case study of a polymerization process in process industry for which function couplings are represented by a very large dataset.

从复杂的系统中出现的危险场景非常困难，即使不是无法人工识别的情况，该复杂场景中的功能数量很大并且相应的功能耦合非常繁琐。当今复杂的系统每分钟都会生成一个非常大的数据集，并且生成的数据的动态性质使其很难跟踪此类耦合。近年来，功能共振分析方法（FRAM）取得了成功，可以理解复杂系统中功能耦合所产生的危害，但是，在不进行定量分析的情况下，如何正确估计聚合耦合仍然存在挑战。当前的研究开发了一种数据驱动的方法，使用关联规则的提升置信区间来量化功能耦合。后来，合并了规则，以识别导致潜在危险情况的路径。这些路径以图形方式显示，并配备了量化的耦合信息，并能够提供指导以防止出现新的危险情况。该方法已通过对过程工业中聚合过程的案例研究进行了证明，其中功能耦合由非常大的数据集表示。