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1. (WO2018200064) IDENTIFYING MULTIPLE CAUSAL ANOMALIES IN POWER PLANT SYSTEMS BY MODELING LOCAL PROPAGATIONS

Pub. No.:    WO/2018/200064    International Application No.:    PCT/US2018/018347
Publication Date: Fri Nov 02 00:59:59 CET 2018 International Filing Date: Fri Feb 16 00:59:59 CET 2018
IPC: H04L 12/24
H04L 12/26
G06F 17/30
Applicants: NEC LABORATORIES AMERICA, INC
Inventors: CHENG, Wei
CHEN, Haifeng
Title: IDENTIFYING MULTIPLE CAUSAL ANOMALIES IN POWER PLANT SYSTEMS BY MODELING LOCAL PROPAGATIONS
Abstract:
A system identifies multiple causal anomalies in a power plant having multiple system components. The system includes a processor. The processor constructs an invariant network model having (i) nodes, each representing a respective system component and (ii) invariant links, each representing a stable component interaction. The processor constructs a broken network model having (i) the invariant network model nodes and (ii) broken links, each representing an unstable component interaction. The processor ranks causal anomalies in node clusters in the invariant network model to obtain anomaly score results. The processor generates, using a joint optimization clustering process applied to the models, (i) a model clustering structure and (ii) broken cluster scores. The processor performs weighted fusion ranking on the anomaly score results and broken cluster scores, based on the clustering structure and implicated degrees of severity of any abnormal system components, to identify the multiple causal anomalies in the power plant.