This thesis develops a systematic, data-based dynamic modeling
framework for industrial processes in keeping with the slowness
principle. Using said framework as a point of departure, it then
proposes novel strategies for dealing with control monitoring and
quality prediction problems in industrial production contexts. The
thesis reveals the slowly varying nature of industrial production
processes under feedback control, and integrates it with process
data analytics to offer powerful prior knowledge that gives rise to
statistical methods tailored to industrial data. It addresses
several issues of immediate interest in industrial practice,
including process monitoring, control performance assessment and
diagnosis, monitoring system design, and product quality
prediction. In particular, it proposes a holistic and pragmatic
design framework for industrial monitoring systems, which delivers
effective elimination of false alarms, as well as intelligent
self-running by fully utilizing the information underlying the
data. One of the strengths of this thesis is its integration of
insights from statistics, machine learning, control theory and
engineering to provide a new scheme for industrial process modeling
in the era of big data.
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