Essential Maintenance: All Authorea-powered sites will be offline 4pm-6pm EDT Tuesday 28 May for essential maintenance.
We apologise for any inconvenience.

loading page

Multiscale Fusion for Abnormality Detection and Localization of Distributed Parameter Systems
  • Peng Wei ,
  • Han-Xiong Li
Peng Wei
City University of Hong Kong

Corresponding Author:[email protected]

Author Profile
Han-Xiong Li
Author Profile

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