The aim of this article is to develop a methodology for measuring the degree of unpredictability in dynamical systems with memory, i.e., systems with responses dependent on a history of past states. The proposed model is generic, and can be employed in a variety of settings, although its applicability here is examined in the particular context of an industrial environment: gas turbine engines. The given approach consists in approximating the probability distribution of the outputs of a system with a deep recurrent neural network; such networks are capable of exploiting the memory in the system for enhanced forecasting capability. Once the probability distribution is retrieved, the entropy or missing information about the underlying process is computed, which is interpreted as the uncertainty with respect to the system's behavior. Hence, the model identifies how far the system dynamics are from its typical response, in order to evaluate the system reliability and to predict system faults and/or normal accidents . The validity of the model is verified with sensor data recorded from commissioning gas turbines, belonging to normal and faulty conditions.

中文翻译：

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系统可靠性监测的深度递归熵自适应模型

本文的目的是开发一种方法来测量 不可预测的程度在带有记忆的动态系统中，即响应取决于过去状态历史的系统。所提出的模型是通用的，并且可以在各种环境中使用，尽管此处的适用性是在特定的工业环境中检验的：燃气轮机。给定的方法包括用深度递归神经网络近似系统输出的概率分布。这样的网络能够利用系统中的存储器来增强预测能力。检索到概率分布后，熵 要么 丢失的信息计算有关基础过程的信息，这被解释为相对于系统行为的不确定性。因此，该模型可以识别系统动力学距离其典型响应有多远，以便评估系统可靠性并预测系统故障和/或正常事故 。该模型的有效性通过调试燃气轮机记录的属于正常和故障情况的传感器数据进行了验证。