Multiscale Fusion for Abnormality Detection and Localization of
Distributed Parameter Systems
Abstract
Numerous industrial thermal processes and fluid processes can be
described by distributed parameter systems (DPSs), wherein many process
parameters and variables vary in space and time. Early internal
abnormalities in the DPS may develop into uncontrollable thermal
failures, causing serious safety incidents. In this study, the
multiscale information fusion is proposed for internal abnormality
detection and localization of DPSs under different scenarios. We
introduce the dissimilarity statistic as a means to identify anomalies
for lumped variables, whereas spatial and temporal statistic measures
are presented for the anomaly detection for distributed variables.
Through appropriate parameter optimization, these statistic functions
are integrated into the comprehensive multiscale detection index, which
outperforms traditional single-scale detection methods. The proposed
multiscale statistic has good physical interpretability from the system
disorder degree. Experiments on the internal short circuit (ISC) of a
battery system have demonstrated that our proposed method can swiftly
identify ISC abnormalities and accurately pinpoint problematic battery
cells under various working conditions.Â