公开(公告)号：US20120215450A1

公开(公告)日：2012-08-23

申请号：US13402084

申请日：2012-02-22

Applicant: Pradeepkumar Ashok ,  Ganesh Krishnamoorthy ,  Delbert Tesar

Inventor： Pradeepkumar Ashok ,  Ganesh Krishnamoorthy ,  Delbert Tesar

IPC: G06F17/18 ,  G01R31/00 ,  G06F19/00

CPC classification number: G05B23/0262 ,  G05B23/0254

Abstract: A method, system and computer program product for distinguishing between a sensor fault and a process fault in a physical system and use the results obtained to update the model. A Bayesian network is designed to probabilistically relate sensor data in the physical system which includes multiple sensors. The sensor data from the sensors in the physical system is collected. A conditional probability table is derived based on the collected sensor data and the design of the Bayesian network. Upon identifying anomalous behavior in the physical system, it is determined whether a sensor fault or a process fault caused the anomalous behavior using belief values for the sensors and processes in the physical system, where the belief values indicate a level of trust regarding the status of its associated sensors and processes not being faulty.

Abstract translation: 一种用于区分传感器故障和物理系统中的过程故障的方法，系统和计算机程序产品，并使用获得的结果来更新模型。 贝叶斯网络被设计为概率地将包括多个传感器的物理系统中的传感器数据相关联。 收集物理系统中传感器的传感器数据。 基于收集的传感器数据和贝叶斯网络的设计导出条件概率表。 在识别物理系统中的异常行为时，确定传感器故障或过程故障是否使用物理系统中的传感器和过程的置信度引起异常行为，其中信念值表示关于状态的信任级别 其相关的传感器和过程不会发生故障。