Condition-Driven Data Analytics and Monitoring for Wide-Range Nonstationary and Transient Continuous Processes
Condition-Driven Data Analytics and Monitoring for Wide-Range Nonstationary and Transient Continuous Processes
复制标题
适用于大范围非平稳和瞬态连续过程的条件驱动数据分析和监控
DOI：
10.1109/tase.2020.3010536
复制
发表时间：
2021-10
影响因子：
5.6
通讯作者：
Hua Jing
中科院分区：
文献类型：
--
作者：
Chunhui Zhao;Junhao Chen;Hua Jing
在真实的过程工业中，运行工况变化频繁且幅度大，导致系统在时间方向上具有大范围的非平稳和瞬态特性。因此，相当大的挑战是，如何解决学习模型的准确性和变化的复杂性之间的冲突，分析和监测的非平稳和瞬态连续过程。在这项工作中，开发了一种新的条件驱动的数据分析方法来处理这个问题。设计了一种条件驱动的数据重组策略，将时间上的非平稳瞬态过程灵活地还原到不同的条件切片中，在同一条件切片中揭示出相似的过程特征。然后可以对新的分析单元进行过程分析。一方面，通过慢特征分析实现粗粒度的状态模式自动划分，以跟踪沿着状态维变化的运行特征;另一方面，细粒度的分布评估进行每一个条件模式与高斯混合模型。贝叶斯推理为基础的距离（BID）监测指标的定义，可以清楚地表明故障的影响，区分不同的操作方案与有意义的物理解释。通过对一个真实的工业过程的仿真研究，验证了该方法的可行性，并将其推广到具有典型的大范围非平稳和沿着时间方向的瞬态特性的连续过程。从业者注意--工业过程一般具有非平稳特性，这些特性在真实的世界数据中普遍存在，通常由各种因素引起的时变均值、时变自协方差或两者反映。本研究的重点是发展一个通用的分析和监测方法，为大范围的非平稳和瞬态连续过程。条件驱动的概念取代了时间驱动的思想。第一次认识到，在相同的条件切片内存在相似的过程特性，并且过程相关性的变化可能与其条件模式有关。此外，该方法可以同时分析静态和动态信息，从而为监测结果提供更好的物理解释，静态和动态信息分别承载不同的信息，类似于物理学中的“位置”和“速度”概念。静态信息可以告诉当前的操作条件，而动态信息可以澄清过程状态是否在不同的稳态之间切换。值得注意的是，条件驱动的概念是通用的，并可以扩展到其他应用程序的工业制造应用。
Frequent and wide changes in operation conditions are quite common in real process industry, resulting in typical wide-range nonstationary and transient characteristics along time direction. The considerable challenge is, thus, how to solve the conflict between the learning model accuracy and change complexity for analysis and monitoring of nonstationary and transient continuous processes. In this work, a novel condition-driven data analytics method is developed to handle this problem. A condition-driven data reorganization strategy is designed which can neatly restore the time-wise nonstationary and transient process into different condition slices, revealing similar process characteristics within the same condition slice. Process analytics can then be conducted for the new analysis unit. On the one hand, coarse-grained automatic condition-mode division is implemented with slow feature analysis to track the changing operation characteristics along condition dimension. On the other hand, fine-grained distribution evaluation is performed for each condition mode with Gaussian mixture model. Bayesian inference-based distance (BID) monitoring indices are defined which can clearly indicate the fault effects and distinguish different operation scenarios with meaningful physical interpretation. A case study on a real industrial process shows the feasibility of the proposed method which, thus, can be generalized to other continuous processes with typical wide-range nonstationary and transient characteristics along time direction. Note to Practitioners—Industrial processes in general have nonstationary characteristics which are ubiquitous in real world data, often reflected by a time-variant mean, a time-variant autocovariance, or both resulting from various factors. The focus of this study is to develop a universal analytics and monitoring method for wide-range nonstationary and transient continuous processes. Condition-driven concept takes the place of time-driven thought. For the first time, it is recognized that there are similar process characteristics within the same condition slice and changes in the process correlations may relate to its condition modes. Besides, the proposed method can provide enhanced physical interpretation for the monitoring results with concurrent analysis of the static and dynamic information which carry different information, analogous to the concepts of “position” and “velocity” in physics, respectively. The static information can tell the current operation condition, while the dynamic information can clarify whether the process status is switching between different steady states. It is noted that the condition-driven concept is universal and can be extended to other applications for industrial manufacturing applications.