Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring
Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring
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DOI：
10.1109/tii.2020.3007152
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发表时间：
2021-02-01
影响因子：
12.3
通讯作者：
Zhang, Yu-Dong
中科院分区：
文献类型：
--
作者：
Martinez-Garcia, Miguel;Zhang, Yu;Zhang, Yu-Dong
这篇文章的目的是开发一种方法来测量动态系统的不可预测性与记忆的程度，即，响应依赖于过去状态历史的系统。所提出的模型是通用的，并可以在各种设置，虽然它的适用性在这里检查在特定的背景下的工业环境：燃气涡轮机发动机。给定的方法包括近似的概率分布的输出系统与深度递归神经网络，这样的网络能够利用系统中的记忆增强预测能力。一旦检索到概率分布，就计算关于底层进程的熵或缺失信息，其被解释为关于系统行为的不确定性。因此，该模型识别系统动态与其典型响应的距离，以评估系统可靠性并预测系统故障和/或正常事故。该模型的有效性进行了验证与传感器数据记录从调试燃气轮机，属于正常和故障状态。
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 systemx0027;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.