This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …

The development of cloud manufacturing enables data-driven process monitoring methods
to reflect the real industrial process states accurately and timely. However, traditional …

Data-driven process monitoring methods have attracted many attentions and gained wide
applications. However, the real industrial process data are much more complex which is …

Most industrial systems frequently switch their operation modes due to various factors, such
as the changing of raw materials, static parameter setpoints, and market demands. To …

In a typical industrial process, there may exist both linear and nonlinear relationships among
process variables. Besides, the existence of process dynamics poses challenges to process …

Industrial process data have the characteristics of less label, multimode, high dimension,
containing noise, and mixing with outliers, which increase the difficulty of mode identification …

The working condition evaluation of the aluminum reduction cell is the basis of formulating
operation strategy, ensuring production safety and realizing stable and optimized operation …

Due to record errors, transmission interruptions, etc., low-quality process data, including
outliers and missing data, commonly exist in real industrial processes, challenging the …

In practice, because of complex mechanism processes, such as heating process, volume
heterogeneity, and various chemical reaction characteristics, there is a nonlinear …

In order to conduct efficient process monitoring of modern industrial system featured with
complexity, distributed and high-dimensional, a distributed dictionary learning is proposed …