  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.
