Heterogeneous recurrence representation and quantification of dynamic transitions in continuous nonlinear processes
Heterogeneous recurrence representation and quantification of dynamic transitions in continuous nonlinear processes
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DOI：
10.1140/epjb/e2016-60850-y
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发表时间：
2016-06-20
影响因子：
1.6
通讯作者：
Yang, Hui
中科院分区：
文献类型：
--
作者：
Chen, Yun;Yang, Hui
许多现实世界的系统随时间不断演变，并呈现出动态行为。为了应对系统的复杂性，通常会部署传感设备来监测系统动态。在线传感带来了大量非线性且非平稳的大数据。尽管关于非线性动力学有丰富的信息，但在实现传感数据对系统控制的全部潜力方面仍然存在重大挑战。本文提出了一种用于非线性动态过程在线监测和异常检测的异构递归分析新方法。首先引入一种名为Q - 树索引的划分方案，用于描绘多维连续状态空间中的局部递归区域。此外，我们设计了一种递归区域之间状态转换的新分形表示，然后开发了新的度量来量化异构递归模式。最后，我们开发了一种用于过程递归的在线监测和预测控制的多元检测方法。案例研究表明，所提出的方法不仅能捕捉变换空间中的异构递归模式，还能提供有效的在线控制图来监测和检测潜在非线性过程中的动态转换。
Many real-world systems are evolving over time and exhibit dynamical behaviors. In order to cope with system complexity, sensing devices are commonly deployed to monitor system dynamics. Online sensing brings the proliferation of big data that are nonlinear and nonstationary. Although there is rich information on nonlinear dynamics, significant challenges remain in realizing the full potential of sensing data for system control. This paper presents a new approach of heterogeneous recurrence analysis for online monitoring and anomaly detection in nonlinear dynamic processes. A partition scheme, named as Q-tree indexing, is firstly introduced to delineate local recurrence regions in the multi-dimensional continuous state space. Further, we design a new fractal representation of state transitions among recurrence regions, and then develop new measures to quantify heterogeneous recurrence patterns. Finally, we develop a multivariate detection method for on-line monitoring and predictive control of process recurrences. Case studies show that the proposed approach not only captures heterogeneous recurrence patterns in the transformed space, but also provides effective online control charts to monitor and detect dynamical transitions in the underlying nonlinear processes.