Computer System and Method for Creating a Supervised Failure Model
First Claim
1. A computing system comprising:
- a network interface configured to facilitate communication with a plurality of assets in a given fleet;
at least one processor;
a non-transitory computer-readable medium; and
program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to create a supervised failure model for assets in the given fleet that is configured to receive operating data as inputs and output a prediction as to the occurrence of a given failure type at the asset, wherein program instructions that are executable to cause the computing system to create the supervised failure model comprise program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to;
identify a first subset of assets in the given fleet that have known prior failure occurrences;
for each asset in the first subset, obtain a respective set of cleaned historical operating data for the asset and then use the respective set of cleaned historical operating data for the asset to create a respective unsupervised failure model for the asset;
use the respective unsupervised failure model for each asset in the first subset to determine a set of deviation bounds for the first subset of assets;
for each remaining asset in the given fleet, obtain a respective set of historical operating data for the asset and then use the respective set of historical operating data for the asset to create a respective unsupervised failure model for the asset;
based on the respective unsupervised failure model for each remaining asset in the given fleet and the set of deviation bounds for the first subset of assets, classify each remaining asset in the given fleet into either a second subset of assets that have suspected prior failure occurrences or a third subset of assets that do not have known or suspected prior failure occurrences;
use the respective unsupervised failure model for each asset in the third subset to determine a set of deviation bounds for the third subset of assets;
based on the respective sets of deviation bounds for the first subset of assets and the third subset of assets, define a set of anomaly thresholds for the given fleet;
apply the set of anomaly thresholds to historical operating data for each of a selected group of assets in the given fleet and thereby detect a set of anomalies that are each suggestive of a prior failure occurrence;
from the set of anomalies, identify a subset of anomalies that are each suggestive of a prior occurrence of the given failure type; and
create the supervised failure model using failure data for the identified subset of anomalies.
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Accused Products
Abstract
The example systems, methods, and devices disclosed herein generally relate to generating create a supervised failure model for assets in the given fleet that is configured to receive operating data as inputs and output a prediction as to the occurrence of a given failure type at the asset. In some instances, a data analytics platform may create and use an unsupervised failure model for a subset of the assets, use the respective unsupervised failure models to detect a set of anomalies that are each suggestive of a prior failure occurrence, from the set of anomalies, identify a subset of anomalies that are each suggest of a prior failure occurrence of the given failure type, and create the supervised failure model using failure data for the identified subset of anomalies.
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Citations
20 Claims
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1. A computing system comprising:
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a network interface configured to facilitate communication with a plurality of assets in a given fleet; at least one processor; a non-transitory computer-readable medium; and program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to create a supervised failure model for assets in the given fleet that is configured to receive operating data as inputs and output a prediction as to the occurrence of a given failure type at the asset, wherein program instructions that are executable to cause the computing system to create the supervised failure model comprise program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to; identify a first subset of assets in the given fleet that have known prior failure occurrences; for each asset in the first subset, obtain a respective set of cleaned historical operating data for the asset and then use the respective set of cleaned historical operating data for the asset to create a respective unsupervised failure model for the asset; use the respective unsupervised failure model for each asset in the first subset to determine a set of deviation bounds for the first subset of assets; for each remaining asset in the given fleet, obtain a respective set of historical operating data for the asset and then use the respective set of historical operating data for the asset to create a respective unsupervised failure model for the asset; based on the respective unsupervised failure model for each remaining asset in the given fleet and the set of deviation bounds for the first subset of assets, classify each remaining asset in the given fleet into either a second subset of assets that have suspected prior failure occurrences or a third subset of assets that do not have known or suspected prior failure occurrences; use the respective unsupervised failure model for each asset in the third subset to determine a set of deviation bounds for the third subset of assets; based on the respective sets of deviation bounds for the first subset of assets and the third subset of assets, define a set of anomaly thresholds for the given fleet; apply the set of anomaly thresholds to historical operating data for each of a selected group of assets in the given fleet and thereby detect a set of anomalies that are each suggestive of a prior failure occurrence; from the set of anomalies, identify a subset of anomalies that are each suggestive of a prior occurrence of the given failure type; and create the supervised failure model using failure data for the identified subset of anomalies. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A implemented-method comprising:
creating a supervised failure model for assets in a given fleet of assets that is configured to receive operating data as inputs and output a prediction as to the occurrence of a given failure type at the asset, wherein creating the supervised failure model comprises; identifying a first subset of assets in the given fleet that have known prior failure occurrences; for each asset in the first subset, obtaining a respective set of cleaned historical operating data for the asset and then use the respective set of cleaned historical operating data for the asset to create a respective unsupervised failure model for the asset; using the respective unsupervised failure model for each asset in the first subset to determine a set of deviation bounds for the first subset of assets; for each remaining asset in the given fleet, obtaining a respective set of historical operating data for the asset and then use the respective set of historical operating data for the asset to create a respective unsupervised failure model for the asset; based on the respective unsupervised failure model for each remaining asset in the given fleet and the set of deviation bounds for the first subset of assets, classifying each remaining asset in the given fleet into either a second subset of assets that have suspected prior failure occurrences or a third subset of assets that do not have known or suspected prior failure occurrences; using the respective unsupervised failure model for each asset in the third subset to determine a set of deviation bounds for the third subset of assets; based on the respective sets of deviation bounds for the first subset of assets and the third subset of assets, defining a set of anomaly thresholds for the given fleet; applying the set of anomaly thresholds to historical operating data for each of a selected group of assets in the given fleet and thereby detect a set of anomalies that are each suggestive of a prior failure occurrence; from the set of anomalies, identifying a subset of anomalies that are each suggestive of a prior occurrence of the given failure type; and creating the supervised failure model using failure data for the identified subset of anomalies. - View Dependent Claims (15, 16, 17)
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18. A non-transitory computer-readable medium comprising programs instructions stored thereon that are executable to cause a computing system to:
create a supervised failure model for assets in the given fleet that is configured to receive operating data as inputs and output a prediction as to the occurrence of a given failure type at the asset, wherein program instructions that are executable to cause the computing system to create the supervised failure model comprise program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to; identify a first subset of assets in the given fleet that have known prior failure occurrences; for each asset in the first subset, obtain a respective set of cleaned historical operating data for the asset and then use the respective set of cleaned historical operating data for the asset to create a respective unsupervised failure model for the asset; use the respective unsupervised failure model for each asset in the first subset to determine a set of deviation bounds for the first subset of assets; for each remaining asset in the given fleet, obtain a respective set of historical operating data for the asset and then use the respective set of historical operating data for the asset to create a respective unsupervised failure model for the asset; based on the respective unsupervised failure model for each remaining asset in the given fleet and the set of deviation bounds for the first subset of assets, classify each remaining asset in the given fleet into either a second subset of assets that have suspected prior failure occurrences or a third subset of assets that do not have known or suspected prior failure occurrences; use the respective unsupervised failure model for each asset in the third subset to determine a set of deviation bounds for the third subset of assets; based on the respective sets of deviation bounds for the first subset of assets and the third subset of assets, define a set of anomaly thresholds for the given fleet; apply the set of anomaly thresholds to historical operating data for each of a selected group of assets in the given fleet and thereby detect a set of anomalies that are each suggestive of a prior failure occurrence; from the set of anomalies, identify a subset of anomalies that are each suggestive of a prior occurrence of the given failure type; and create the supervised failure model using failure data for the identified subset of anomalies. - View Dependent Claims (19, 20)
Specification