US 11,853,049 B2
Integrity monitoring in automation systems
Steffen Fries, Baldham (DE); and Rainer Falk, Poing (DE)
Assigned to SIEMENS AKTIENGESELLSCHAFT
Appl. No. 16/623,480
Filed by Siemens Aktiengesellschaft, Munich (DE)
PCT Filed Jun. 7, 2018, PCT No. PCT/EP2018/065003
§ 371(c)(1), (2) Date Dec. 17, 2019,
PCT Pub. No. WO2019/011539, PCT Pub. Date Jan. 17, 2019.
Claims priority of application No. 17180526 (EP), filed on Jul. 10, 2017.
Prior Publication US 2020/0183374 A1, Jun. 11, 2020
Int. Cl. G06F 9/4401 (2018.01); G06F 8/61 (2018.01); G06F 9/445 (2018.01); G06F 21/12 (2013.01); G06F 9/455 (2018.01); G05B 23/02 (2006.01); G06N 20/00 (2019.01); G06F 21/55 (2013.01); G06F 21/57 (2013.01); G06N 5/04 (2023.01)
CPC G05B 23/0254 (2013.01) [G06F 21/552 (2013.01); G06F 21/57 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06F 2221/034 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A method, comprising:
obtaining state data of an industrial automation system, wherein the state data describes a current operating state of a respective actuator of the industrial automation system and is recorded in one or more log files;
obtaining sensor data describing an environmental influence of the industrial automation system, wherein the sensor data quantifies a physical measurement;
obtaining control data for one or a plurality of actuators of the industrial automation system which bring about the environmental influence, wherein the control data describes a manner and/or an intensity of the environmental influence of the respective actuator; and
carrying out a comparison between the state data, the sensor data, and the control data using a model to output a result signal indicative of a probability of an impairment to an integrity of the industrial automation system, wherein the model is empirically determined by means of techniques of machine learning;
wherein the comparison takes account of a deviation of the environmental influence from a reference obtained on a basis of the model.