Patent application EP19179160A (EP), filed 2019-06-07
Publication number: EP3582153A1
Title: GENERATING HYBRID MODELS OF PHYSICAL SYSTEMS
Abstract: Systems and methods described receive a set of experimental data of connection points of an unknown component of a partially known physical system. The systems and methods set feasibility constraints for an untrained model of the unknown component and simulate the partially known system using the untrained model of the unknown component to generate simulated data at the connection points of the unknown component. Systems and methods then optimize the untrained model based on the feasibility constraints and a comparison of the simulated data and the experimental data to generate a trained model of the unknown component.
IPC codes: G06N  20/    00            A I
Claims: number: 1
text: A method comprising:
number: 2
text: The method of claim 1, wherein optimizing the untrained model comprises:
number: 3
text: The method of claim 1, further comprising setting passivity constraints for the untrained model of the unknown component.
number: 4
text: The method of claim 1, further comprising generating a model-based diagnostic output for the physical system using a system model of the physical system, wherein the system model comprises the trained model of the unknown component.
number: 5
text: The method of claim 1, wherein the untrained model comprises an untrained neural network.
number: 6
text: The method of claim 1, wherein the partially known physical system comprises an electromechanical system and the unknown component comprises one of an electrical or mechanical component.
number: 7
text: The method of claim 1, further comprising:
number: 8
text: The method of claim 1, wherein the experimental data comprises data measured from one or more connection points or nodes of the physical system in different conditions.
number: 9
text: The method of claim 1, wherein simulating the partially known system comprises:
number: 10
text: A system comprising:
number: 11
text: The system of claim 10, wherein to optimize the untrained model the processing device is further to:
number: 12
text: The system, of claim 10, wherein the processing device is further to generate passivity constraints for the untrained model of the unknown component.
number: 13
text: The system of claim 10, wherein the processing device is further to generate a model-based diagnostic output for the physical system using a system model of the physical system, wherein the system model comprises the trained model of the unknown component.
number: 14
text: The system of claim 10, wherein the processing device is further to:
number: 15
text: The system of claim 10, wherein to simulate the partially known system, the processing device is to:
number: 16
text: A non-transitory computer-readable medium having instructions stored thereon that, when executed by a processing device, cause the processing device to:
number: 17
text: The non-transitory computer-readable medium of claim 16, wherein to optimize the untrained model the instructions further cause the processing device to:
number: 18
text: The non-transitory computer-readable medium of claim 16, wherein the instructions further cause the processing device to generate passivity constraints for the untrained model of the unknown component.
number: 19
text: The non-transitory computer-readable medium of claim 16, wherein the instructions further cause the processing device to:
number: 20
text: The non-transitory computer-readable medium of claim 16, wherein to simulate the partially known system the instructions further cause the processing device to:
Office action: search_report, dated 2019-12-18
Prior art cited: ref_id: - ION MATEI ET AL: "The Case for a Hybrid Approach to Diagnosis: A Railway Switch Design Space Exploration View project The International Diagnostic Competition View project The Case for a Hybrid Approach to Diagnosis: A Railway Switch", 1 August 2015 (2015-08-01), XP055632016, Retrieved from the Internet <URL:https://www.researchgate.net/profile/Tomonori_Honda/publication/305731351_The_Case_for_a_Hybrid_Approach_to_Diagnosis_A_Railway_Switch/links/579e780e08ae802facbded77/The-Case-for-a-Hybrid-Approach-to-Diagnosis-A-Railway-Switch.pdf> [retrieved on 20191015]
ref_type: npl
severity: background
ref_id: - Ion Matei ET AL, "The Case for a Hybrid Approach to Diagnosis: A Railway Switch Design Space Exploration View project The International Diagnostic Competition View project The Case for a Hybrid Approach to Diagnosis: A Railway Switch", (20150801), URL: https://www.researchgate.net/profile/Tomonori_Honda/publication/305731351_The_Case_for_a_Hybrid_Approach_to_Diagnosis_A_Railway_Switch/links/579e780e08ae802facbded77/The-Case-for-a-Hybrid-Approach-to-Diagnosis-A-Railway-Switch.pdf, (20191015), XP055632016 [I] 1-20 * abstract * * figure 1 * * page  225, column  left, line  1  - page  229, column  left, line  37 *
ref_type: npl
severity: background
Final disposition: pending (2019-12-18)