An improved method for the real time sensitivity analysis in large scale complex systems is proposed in this paper. The method borrows principles from the event tracking of interrelated causal events and deploys clustering methods to automatically measure the relevance and contribution made by each input event data (ED) on system outputs. The ethos of the proposed event modeling (EM) technique is that the behavior or the state of a system is a function of the knowledge acquired about events occurring in the system and its wider operational environment. As such it builds on the theoretical and the practical foundation for the engineering of knowledge and data in modern and complex systems. The proposed EM platform EventiC filters noncontributory ED sources and has the potential to include information that was initially thought irrelevant or simply not considered at the design stage. The real-time ability to group and rank relevant input–output ED in order of its importance and relevance will not only improve the data quality, but leads to an improved higher level of mathematical formulization in the modern complex systems. The contribution of the approach to systems’ modeling is in the automation of data analysis, control, and plant process modeling. EventiC has been validated as the monitoring and the control system for a cement factory. In addition to the previously known parameters, the proposed EventiC identified new influential parameters that were previously unknown. It also filtered 18% of the input data without compromising the data quality or the integrity. The solution has improved the quality of input variable selection and simplify plant control strategies.

中文翻译：

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EventiC：一种用于复杂系统的实时无偏基于事件的学习技术

本文提出了一种改进的大规模复杂系统实时灵敏度分析方法。该方法借鉴了相互关联的因果事件的事件跟踪原理，并部署了聚类方法来自动测量每个输入事件数据 (ED) 对系统输出的相关性和贡献。所提议的事件建模 (EM) 技术的精神是，系统的行为或状态是所获得的关于系统及其更广泛的操作环境中发生的事件的知识的函数。因此，它建立在现代和复杂系统中知识和数据工程的理论和实践基础之上。提议的 EM 平台 EventiC 可过滤非贡献性 ED 源，并有可能包含最初被认为不相关或在设计阶段根本没有考虑的信息。按照重要性和相关性对相关输入输出 ED 进行分组和排序的实时能力不仅会提高数据质量，还会导致现代复杂系统中数学公式化水平的提高。该方法对系统建模的贡献在于数据分析、控制和工厂过程建模的自动化。EventiC 已被验证为水泥厂的监控和控制系统。除了先前已知的参数之外，提议的 EventiC 还识别了先前未知的新的有影响的参数。它还过滤了 18% 的输入数据，而不会影响数据质量或完整性。该解决方案提高了输入变量选择的质量并简化了工厂控制策略。