Device health estimation by combining contextual information with sensor data
First Claim
1. A computer-executable method for detecting fault in a machine, comprising:
- obtaining a control signal associated with controlling the machine and sensor data that indicates a condition of the machine during a time period when the control signal controls the machine;
determining consistent time intervals for the control signal, wherein during a consistent time interval the standard a standard deviation of the control signal is less than a predetermined threshold;
mapping the consistent time intervals to the sensor data to determine a plurality of time interval segments for the sensor data;
generating a plurality of training features based on the sensor data, wherein each respective feature is generated in association with from a time interval segment;
providing the plurality of training features as input to a classifier to train the classifier to classify abnormal sensor data during a respective consistent time interval;
generating new features for the classifier with same conditions as in classifier training by determining time intervals of a primary control signal that have same values for the primary control signal as a value of the primary control signal when generating the training features; and
detecting a machine fault by providing new features associated with additional machine sensor data as input to the classifier to detect abnormal sensor data during a respective consistent time interval.
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Accused Products
Abstract
A method and system for detecting fault in a machine. During operation, the system obtains control signals and corresponding sensor data that indicates a condition of the machine. The system determines consistent time intervals for each of the control signals. During a consistent time interval the standard deviation of a respective control signal is less than a respective predetermined threshold. The system aggregates the consistent time intervals to determine aggregate consistent intervals. The system then maps the aggregate consistent intervals to the sensor data to determine time interval segments for the sensor data. The system may generate features based on the sensor data. Each respective feature is generated from a time interval segment of the sensor data. The system trains a classifier using the features, and applies the classifier to additional sensor data indicating a condition of the machine over a period of time to detect a machine fault.
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Citations
20 Claims
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1. A computer-executable method for detecting fault in a machine, comprising:
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obtaining a control signal associated with controlling the machine and sensor data that indicates a condition of the machine during a time period when the control signal controls the machine; determining consistent time intervals for the control signal, wherein during a consistent time interval the standard a standard deviation of the control signal is less than a predetermined threshold; mapping the consistent time intervals to the sensor data to determine a plurality of time interval segments for the sensor data; generating a plurality of training features based on the sensor data, wherein each respective feature is generated in association with from a time interval segment; providing the plurality of training features as input to a classifier to train the classifier to classify abnormal sensor data during a respective consistent time interval; generating new features for the classifier with same conditions as in classifier training by determining time intervals of a primary control signal that have same values for the primary control signal as a value of the primary control signal when generating the training features; and detecting a machine fault by providing new features associated with additional machine sensor data as input to the classifier to detect abnormal sensor data during a respective consistent time interval. - View Dependent Claims (2, 3, 4, 5, 6, 15, 16)
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7. A non-transitory computer-readable storage medium storing instructions which when executed by a computer cause the computer to perform a method for detecting fault in a machine, the method comprising:
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obtaining a control signal associated with controlling the machine and sensor data that indicates a condition of the machine during a time period when the control signal controls the machine; determining consistent time intervals for the control signal, wherein during a consistent time interval a standard deviation of the control signal is less than a predetermined threshold; mapping the consistent time intervals to the sensor data to determine a plurality of time interval segments for the sensor data; generating a plurality of training features based on the sensor data, wherein each respective feature is generated in association with a time interval segment; providing the plurality of training features as input to a classifier to train the classifier to classify abnormal sensor data during a respective consistent time interval; generating new features for the classifier with same conditions as in classifier training by determining time intervals of a primary control signal that have same values for the primary control signal as a value of the primary control signal when generating the training features; and detecting a machine fault by providing new features associated with additional machine sensor data as input to the classifier to detect abnormal sensor data during a respective consistent time interval. - View Dependent Claims (8, 9, 10, 11, 17, 18)
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12. A computing system comprising:
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one or more processors; a memory; and a non-transitory computer-readable medium coupled to the one or more processors storing instructions stored that, when executed by the one or more processors, cause the computing system to perform a method comprising; obtaining a control signal associated with controlling the machine and sensor data that indicates a condition of the machine during a time period when the control signal controls the machine; determining consistent time intervals for the control signal, wherein during a consistent time interval a standard deviation of the control signal is less than a predetermined threshold; mapping the consistent time intervals to the sensor data to determine a plurality of time interval segments for the sensor data; generating a plurality of training features based on the sensor data, wherein each respective feature is generated in association with from a time interval segment; providing the plurality of training features as input to a classifier to train the classifier to classify abnormal sensor data during a respective consistent time interval; generating new features for the classifier with same conditions as in classifier training by determining time intervals of a primary control signal that have same values for the primary control signal as a value of the primary control signal when generating the training features; and detecting a machine fault by providing new features associated with additional machine sensor data as input to the classifier to detect abnormal sensor data during a respective consistent time interval. - View Dependent Claims (13, 14, 19, 20)
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Specification