  For large-scale industrial processes under closed-loop control, process
dynamics directly resulting from control action are typical characteristics and
may show different behaviors between real faults and normal changes of
operating conditions. However, conventional distributed monitoring approaches
do not consider the closed-loop control mechanism and only explore static
characteristics, which thus are incapable of distinguishing between real
process faults and nominal changes of operating conditions, leading to
unnecessary alarms. In this regard, this paper proposes a distributed
monitoring method for closed-loop industrial processes by concurrently
exploring static and dynamic characteristics. First, the large-scale
closed-loop process is decomposed into several subsystems by developing a
sparse slow feature analysis (SSFA) algorithm which capture changes of both
static and dynamic information. Second, distributed models are developed to
separately capture static and dynamic characteristics from the local and global
aspects. Based on the distributed monitoring system, a two-level monitoring
strategy is proposed to check different influences on process characteristics
resulting from changes of the operating conditions and control action, and thus
the two changes can be well distinguished from each other. Case studies are
conducted based on both benchmark data and real industrial process data to
illustrate the effectiveness of the proposed method.
