Condition-Driven Data Analytics and Monitoring for Wide-Range Nonstationary and Transient Continuous Processes

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.

Automated summary

The paper introduces a novel condition-driven data analytics method to address the challenge of frequent changes in operation conditions in real process industry. By neatly restoring the time-wise nonstationary and transient process into different condition slices, the method effectively solves the issue of analysis and monitoring of nonstationary and transient characteristics.