Anomaly Event Detector
AssigneeAspenTech Corporation
InventorsJiangsheng You, Mikhail Noskov
Life of this patent
Estimating the term…
Abstract
Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and/or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning model to calculate a predicted behavior of the monitored component. Actual behavior of the component, captured by current sensor readings, is compared to the predicted behavior of the component, calculated by the machine learning model, to compute a divergence. The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state.
Claims20
A computer-implemented predictive method for identifying an anomalous state of a component in a real-world environment, the method comprising:
- receiving data from at least one sensor of a component in a real-world environment;
- executing a machine learning model to calculate, using the received data from the at least one sensor of the component in the real-world environment, a predicted behavior of the component;
- computing a divergence based on a difference between an actual behavior of the component and the predicted behavior of the component;
- and
- determining, using a statistic learning method, and indicating if the component in the real-world environment is in an anomalous state based upon (i) a scale of the divergence and (ii) a variation of the divergence.
A computer-based system for identifying an anomalous state of a component in a real-world environment, the system comprising:
- a processor;
- and
- a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions being configured to cause the system to:
- receive data from at least one sensor of a component in a real-world environment;
- execute a machine learning model to calculate, using the received data from the at least one sensor of the component in the real-world environment, a predicted behavior of the component;
- compute a divergence based on a difference between an actual behavior of the component and the predicted behavior of the component;
- and
- determine, using a statistic learning method, and indicate if the component in the real-world environment is in an anomalous state based upon (i) a scale of the divergence and (ii) a variation of the divergence.
A computer program product for identifying an anomalous state of a component in a real-world environment, the computer program product comprising:
- one or more non-transitory computer-readable storage devices and program instructions stored on at least one of the one or more storage devices, the program instructions, when loaded and executed by a processor, cause an apparatus associated with the processor to:
- receive data from at least one sensor of a component in a real-world environment;
- execute a machine learning model to calculate, using the received data from the at least one sensor of the component in the real-world environment, a predicted behavior of the component;
- compute a divergence based on a difference between an actual behavior of the component and the predicted behavior of the component;
- and
- determine, using a statistic learning method, and indicate if the component in the real-world environment is in an anomalous state based upon (i) a scale of the divergence and (ii) a variation of the divergence.
Dependencies are inferred from the claim text.