EAGER: Predictive Entropy-based State-Space Methodologies For Early-Warning of Critical System Transitions

该项目的目的是为工程和生物系统中即将发生的关键转变或灾难性故障的预警指标制定一种可能具有变革意义的方法和方法。该方法将采用不确定性度量，例如各种形式的熵（系统中无序的度量）和独立的信号分量来预测即将发生的关键过渡到故障。预警系统指标的可用性将对广泛的工程和自然系统产生深远的影响。工程系统的相关例子包括电力和通信网络、飞机发动机、工业机器人、电子、机器和桥梁基础设施；自然系统的例子包括癫痫发作、器官衰竭、心脏病发作和脑中风。具体来说，基于感官测量的电力系统即将发生故障的预测指标对于采取措施防止突然故障（包括停电）至关重要。研究的重点是基于熵的动态预测指标，在灾难性关键转变发生之前给出警告信号。在工程系统的情况下，例如，发电机和电力系统，该方法将利用可靠的紧凑模型的存在进行预测推理。对于自然系统，例如，从脑电图测量预测即将发生的癫痫发作，该方法将自适应地识别一个可行的模型，同时提取其预测的指标。在这两个领域中，目标是从测量剖面中提取状态信号的独立分量及其统计指标的集合，以推断即将发生的灾难性过渡到故障。该方法有望开发一种新的基于熵的核心状态独立分量的转换框架，并将其融合到卡尔曼和非线性预测思想的既定领域以及向分岔和混沌的临界过渡的动力系统概念中。因此，它推进并整合了(i)统计独立成分分析，（ii）动力系统分岔，以及（iii）递归预测系统理论，包括卡尔曼和非线性预测器。目标是开发早期预警预测系统的方法和方法，并在特定的原型工程模型以及自然（生物）系统上验证其性能。

The project aims at developing a potentially transformative approach and methodologies for early-warning indicators for impending critical transitions or catastrophic failure in engineered and biological systems. The methodologies will employ uncertainty measures, such as various forms of entropy ( a measure of disorder in systems), and independent signal components to predict the impending onset of critical transitions to failure. The availability of early-warning system indicators would profoundly impact a broad range of engineering and natural systems. Relevant examples of engineered systems include power and communication networks, aircraft engines, industrial robots, electronics, machines and bridge infrastructures; examples of natural systems include epileptic seizures, organ failures, heart attacks and brain strokes. Specifically, predictive indicators for the impending onset of power system failures, based on sensory measurements, is crucial for taking steps to prevent sudden failures, including blackouts.The focus of the research is on entropy-based dynamically predictive indicators that give warning signals prior to the onset of catastrophic critical transitions. In the case of engineered systems, e.g., power generators and power systems, the approach would exploit the existence of reliable compact models for its predictive inference. For natural systems, e.g., predicting the impending epileptic seizures from EEG measurements, the approach would adaptively identify a viable model while simultaneously extracting indicators for its prediction. In both domains, the goal is to extract ensembles of the state signal's independent components and their statistical indicators from measurement profiles to infer the impending catastrophic transition to failure. The approach is expected to develop a new transformative framework of entropy-based core state independent components and fuse it into the established realm of Kalman and nonlinear prediction ideas as well as the dynamical systems concepts of critical transitions to bifurcations and chaos. It thus advances and integrates the disparate disciplines of (i) statistical independent component analysis, (ii) dynamical systems bifurcations, and (iii) recursive prediction system theory including the Kalman and nonlinear predictors. The goal is to develop the approach and methodologies for early-warning predictive systems, and validate their performance on specific prototype engineering models as well as natural (biological) systems.