公开(公告)号：US11256562B2

公开(公告)日：2022-02-22

申请号：US16365884

申请日：2019-03-27

Applicant: HONEYWELL INTERNATIONAL INC.

Inventor： Prasun Das ,  Sreenivasan Govindillam K

IPC: G06F11/07 ,  G06N20/00 ,  B64D45/00 ,  G08G5/00 ,  G05B23/02 ,  G06F17/18

Abstract: A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node.

公开(公告)号：US20200310907A1

公开(公告)日：2020-10-01

申请号：US16365884

申请日：2019-03-27

Applicant: HONEYWELL INTERNATIONAL INC.

Inventor： Prasun Das ,  Sreenivasan Govindillam K

IPC: G06F11/07 ,  B64D45/00 ,  G06N20/00

Abstract: A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node.