The implementation framework of data-driven process monitoring methods is first introduced. In this work, classical and recent research on data-driven process monitoring methods is reviewed from the perspective of characterizing and mining industrial data. Although many promising FDD methods have been proposed from both academia and industry, challenges remain due to the complex characteristics of industrial data.

After decades of development, data-driven process monitoring technologies have gradually attracted attention from process industries. Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation. Safe and stable operation plays an important role in the chemical industry. Finally, the effects of the proposed algorithm are demonstrated by a mathematical model and the penicillin fermentation process. Third, the global neighborhood preserving embedding method is used to extract structures fully after the dynamic and nonlinear characteristics are processed.

Second, a nonlinear method named constructive polynomial mapping is used to avoid unnecessary redundancy and reduce computational complexity. First, the time-lagged window is used to remove the auto-correlation in time series of process variables. Compared with the basic kernel mapping method which provides an infinite-order nonlinear mapping, the proposed method considers the dynamic and nonlinear characteristics with many physical constraints and preserves the global and local structures concurrently. For the nonlinear batch process monitoring, kernel mapping is widely used to eliminate nonlinearity by projecting the data into high-dimensional space, but the nonlinear relationships between batch process variables are limited by many physical constraints, and the infinite-order mapping is inefficient and redundant.

Aiming at the dynamic and nonlinear characteristics of batch process, a multiway dynamic nonlinear global neighborhood preserving embedding algorithm is proposed.

Process monitor mac mpls