According to numerous statistical analysis studies on chemical accidents, it can be concluded that human error is the main cause of chemical accidents. However, safety accidents still occur every year, resulting in major losses of property and human lives. With the newly proposed carbon peaking and carbon neutrality goals, governments have strengthened the supervision on production safety, including establishing regulations and carrying out technical training. Finally, the challenges and possible solutions for actual industrial applications are discussed.Ĭhemical process accidents have become a critical threat to sustainable development in our society. State of art of process monitoring methods corresponding to common industrial data characteristics are then reviewed. 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.

Real time process monitoring software