Disney patents method to train AI on simulated machine faults
Disney describes a technique for creating training data for machine learning models by simulating faults in a digital copy of a physical system. The process feeds fake sensor data, generated from these simulated errors, into a test control system to record how it reacts. This labeled data is then used to teach an AI model to recognize real-world equipment failures during actual operation.
In the filing’s own words
A method for training a control system model includes introducing a simulated fault into a software simulation of a physical system and generating emulated sensor data based on the simulated fault, where the emulated sensor data emulates output from one or more sensors of the physical system. The method further includes obtaining output data from a test control system provided with the emulated sensor data, where the test control system emulates a control system of the physical system and tagging the output data with the simulated fault to create training data. The method further includes utilizing the training data to train the control system model, where the control system model is a machine learning model for use with the control system of the physical system during operation of the physical system.
What exactly they claim (claim 1)
1. A computer-implemented method for generating a control system model, comprising: introducing, by a processor, a simulated fault into a software simulation of a physical system, the simulated fault comprising a plurality of fault instances distributed over a simulation time; executing, by the processor, the software simulation to generate emulated sensor data, the emulated sensor data emulating output from at least one physical sensor of the physical system in response to the simulated fault; providing, by the processor, the emulated sensor data to a test control system, wherein the test control system emulates a control system of the physical system; executing, by the processor, the test control system to generate control system output signals in response to the emulated sensor data; generating, by the processor, training data by associating the test control system output signals with the simulated fault instances that produced the emulated sensor data; and training, by the processor, a control system model using the training data, wherein the control system model comprises a machine learning model trained to identify physical fault conditions in the physical system during operation of the physical system.
Facts
- Company
- Disney (Disney Enterprises, Inc.)
- Official title
- TRAINING OF MACHINE LEARNING MODELS WITH HARDWARE-IN-THE-LOOP SIMULATIONS
- Publication number
- US20260278473A1
- Application number
- 19680899
- Filed
- May 18, 2026
- Published
- Sep 17, 2026
- Inventors
- 2
- Claims
- 20
Classified under
G06N 20/00 Physics & computingG06F 9/455 Physics & computingG05B 2219/23446 Physics & computingMore from Disney in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.