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

该项目旨在为工程系统和生物系统即将发生的关键过渡或灾难性故障的预警指标制定一种可能具有变革性的方针和方法。这些方法将使用各种形式的熵(衡量系统无序性)和独立的信号分量等不确定性指标来预测即将发生的向故障的关键转变。预警系统指标的提供将对广泛的工程和自然系统产生深远影响。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、电子设备、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。具体地说，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包括停电在内的突发故障是至关重要的。研究的重点是基于熵的动态预测指标，它在灾难性关键转变开始之前发出警告信号。对于工程系统，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来进行预测推理。对于自然系统，例如，从脑电测量中预测即将到来的癫痫发作，该方法将自适应地识别可行的模型，同时提取用于预测的指标。在这两个领域，目标是从测量配置文件中提取状态信号的独立分量及其统计指标的集合，以推断即将发生的向故障的灾难性转变。这种方法有望发展一种新的基于熵的核心状态独立分量的变换框架，并将其融合到卡尔曼和非线性预测思想以及从临界转变到分叉和混沌的动力系统概念中。因此，它提出并整合了(I)统计独立分量分析、(Ii)动力系统分叉和(Iii)递归预测系统理论的不同学科，包括卡尔曼预报器和非线性预报器。目标是开发预警预测系统的方法和方法，并在具体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.