- Number
- 20240169215
- Published
- 2024-05-23
- Filed
- 2022-11-21
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Onofre; Thiago Borba et al.
- CPC
- G06F11/0721; G06F11/0793; G06N20/00; G06N3/08; G06N5/022
- Verdict
- Set aside IT anomaly detection, generic
- Source
- Google Patents · FreePatentsOnline
Abstract
A system includes a processor and a memory storing software code and a machine learning (ML) model. The software code is executed to receive contextual data samples each including raw data and a descriptive label, for each contextual data sample: search a database for a data pattern matching the raw data, determine, when the data pattern is detected, whether the data pattern is correlated with an anomalous event, and generate, when the correlation is determined, training data including a label identifying the anomalous event, and the raw data, the data pattern, or both, to provide one of multiple training data samples, wherein the training data samples describe anomalous events corresponding respectively to the raw data, the data pattern, or both. The software code is further executed to train the ML model, using the training data samples, to provide a trained predictive ML model configured to predict the anomalous events.
Background
BACKGROUND
Industrial control systems may be used to monitor the performance of hundreds or thousands of machines based on data received from hundreds of thousands of digital and analog sensors. A conventional approach to making use of this abundance of data is to set “normal” or expected ranges for each sensor, and to compare the sensor data being received to those acceptable ranges. When sensor data strays outside of such acceptable ranges, a fault condition may be flagged automatically. Alternatively, or in addition, system operators trained to look for irregularities in sensor data may monitor the data being received from the sensors and either proactively initiate a maintenance inspection of a machine or override an automated fault flag.
Both conventional approaches have their drawbacks. Flagging mechanical faults automatically based on the comparison of sensor data to predetermined ranges tends undesirably to produce many false positives, resulting in unnecessary equipment shutdowns, maintenance inspections, and their attendant delays. In addition, automated range based fault flags are merely reactive, and offer no means to preemptively avoid the fault condition. Relying on human expertise, while more forward looking than responding to automated fault flags, is expensive due to the training and experience required for a system operator to achieve competence. Moreover, when a trained system operator retires or leaves one company to work for another, the exp
Claims
1. A system comprising: a hardware processor and a system memory storing a software code and a machine learning (ML) model; the hardware processor configured to execute the software code to: receive a plurality of contextual data samples, each of the plurality of contextual data samples including first raw data and a descriptive label; for each of the plurality of contextual data samples: search a database, using a predetermined matching criterion, for a first data pattern matching the first raw data; determine, when searching detects the first data pattern, whether there is a correlation between the first data pattern and an anomalous event; generate, when determining determines the correlation, training data including a label identifying the anomalous event, and at least one of the first raw data or the first data pattern, to provide one of a plurality of training data samples, wherein the plurality of training data samples describe a plurality of anomalous events corresponding respectively to the at least one of the first raw data or the first data pattern; and train the ML model, using the plurality of training data samples, to provide a trained predictive ML model configured to predict the plurality of anomalous events. ||
8. A method for use by a system including a hardware processor and a system memory storing a software code and a machine learning (ML) model, the method comprising: receiving, by the software code executed by the hardware processor, a plurality of contextual data samples, each of the plurality of contextual data samples including first raw data and a descriptive label; for each of the plurality of contextual data samples: searching a database, by the software code executed by the hardware processor and using a predetermined matching criterion, for a first data pattern matching the first raw data; determining, by the software code executed by the hardware processor when searching detects the first data pattern, whether there is a correlation between the first data pattern and an anomalous event; generating, by the software code executed by the hardware processor when determining determines the correlation, training data including a label identifying the anomalous event, and at least one of the first raw data or the first data pattern, to provide one of a plurality of training data samples, wherein the plurality of training data samples describe a plurality of anomalous events corresponding respectively to the at least one of the first raw data or the first data pattern; and training the ML model, by the software code executed by the hardware processor, using the plurality of training data samples, to provide a trained predictive ML model configured to predict the plurality of anomalous events. ||
15. A computer-readable non-transitory storage medium having stored thereon a software code, which when executed by a hardware processor performs a method comprising: receiving a plurality of contextual data samples, each of the plurality of contextual data samples including first raw data and a descriptive label; for each of the plurality of contextual data samples: searching a database, using a predetermined matching criterion, for a first data pattern matching the first raw data; determining, when searching detects the first data pattern, whether there is a correlation between the first data pattern and an anomalous event; generating, training data including a label identifying the anomalous event, and at least one of the first raw data or the first data pattern, to provide one of a plurality of training data samples, wherein the plurality of training data samples describe a plurality of anomalous events corresponding respectively to the at least one of the first raw data or the first data pattern; and training a machine learning (ML) model, using the plurality of training data samples, to provide a trained predictive ML model configured to predict the plurality of anomalous events.