Hierarchical density decompositions for abnormal event diagnosis in serially correlated non-Gaussian systems. (March 2020) - Ying, Anni; Zeng, Jiusun; Kruger, Uwe; Luo, Shihua; Xie, Lei
Abstract: Processes in industrial practice often represent dynamic systems, which is a result of controller feedback, the presence of process noise and measured or unmeasured disturbances. To monitor dynamic systems that produce serially/time-based correlated data records, the literature has proposed numerous multivariate techniques that rely on (i) time-lagged arrangements of the recorded variables and (ii) subspace-based approaches. Proposed techniques, however, may not accurately and reliably describe to what extent process variables are affected by abnormal events, which compromises the identification of potential root causes. To address this, this paper employs a state space model to describe the inherent serial correlation and introduces a joint probability density function for this model. The density function can be hierarchically decomposed into the product of multiple low-dimensional conditional densities to reduce complexity and to detect and preanalyze anomalous behavior. Using the low-dimensional densities, a correction scheme to optimally describe the effect of an abnormal event upon particular process variables is proposed. A second important benefit of the hierarchical decomposition is that no assumptions are imposed on the distribution of the random error component of the state space models. Application studies to a simulation process and recorded data from two industrial processes demonstrate that, compared to conventional methods, the hierarchical decomposition can substantially improve the diagnosis of abnormal process behavior.