公开(公告)号：US11170314B2

公开(公告)日：2021-11-09

申请号：US16166417

申请日：2018-10-22

Applicant: General Electric Company

Inventor： Masoud Abbaszadeh ,  Fernando D'Amato

IPC: G06F7/60 ,  G06N7/00 ,  G06K9/62 ,  G06F9/54 ,  G06F17/16 ,  H04L12/26 ,  G06F17/18

Abstract: A cyber-physical system may have a plurality of monitoring nodes each generating a series of current monitoring node values over time that represent current operation of the cyber-physical system. According to some embodiments, a features extraction computer platform may receive the series of current monitoring node values over time and generate current feature vectors based on the series of current monitoring mode values. A system mode estimation computer platform may provide the current feature vectors to a probabilistic graphical model to generate an estimated system mode. The system mode estimation computer platform may then compare the estimated system mode with a currently reported system mode output by the cyber-physical system and generate a system mode status indication based on a result of said comparison. According to some embodiments, the system mode status indication can be used to override the currently reported system mode of the cyber-physical system.

公开(公告)号：US11146579B2

公开(公告)日：2021-10-12

申请号：US16138408

申请日：2018-09-21

Applicant: General Electric Company

Inventor： Masoud Abbaszadeh ,  Fernando D'Amato

IPC: H04L29/06 ,  G06F17/18 ,  H04L12/26 ,  G06N20/00

Abstract: A cyber-physical system may have a plurality of monitoring nodes each generating a series of current monitoring node values over time representing current operation of the system. A data-driven features extraction computer platform may receive the series of current monitoring node values and generate current data-driven feature vectors based on the series of current monitoring node values. A residual features extraction computer platform may receive the series of current monitoring node values, execute a system model and utilize a stochastic filter to determine current residual values, and generate current residual-driven feature vectors based on the current residual values. An abnormal detection platform may then receive the current data-driven and residual-driven feature vectors and compare the current data-driven and residual-driven feature vectors with at least one decision boundary associated with an abnormal detection model. An abnormal alert signal may then be transmitted when appropriate based on a result of said comparison.