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重大转变或灾难性故障制定预警指标，制定一种可能具有变革性的办法和方法。这些方法将采用不确定性度量，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临界故障。预警系统指标的可用性将深刻影响广泛的工程和自然系统。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。具体而言，预测指标即将发生的电力系统故障，传感器测量的基础上，是至关重要的，采取措施，以防止突然的故障，包括blackouts.The研究的重点是基于熵的动态预测指标，给出警告信号之前发生的灾难性的关键转变。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，其预测推理。对于自然系统，例如，从EEG测量预测即将发生的癫痫发作，该方法将自适应地识别可行的模型，同时提取用于其预测的指标。在这两个域中，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，以推断即将发生的灾难性故障过渡。该方法有望开发一个新的基于熵的核心状态独立组件的变革框架，并将其融合到卡尔曼和非线性预测思想的既定领域，以及临界过渡到分叉和混沌的动力系统概念。因此，它的进步和整合不同的学科（i）统计独立成分分析，（ii）动力系统分叉，（iii）递归预测系统理论，包括卡尔曼和非线性预测。目标是开发预警预测系统的方法和手段，并验证其在特定原型工程模型以及自然（生物）系统上的性能。

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.