INDUSTRIAL ASSET TEMPORAL ANOMALY DETECTION WITH FAULT VARIABLE RANKING
First Claim
1. A method of temporal anomaly detection, the method comprising:
- accessing sensor data readings obtained at a monitored industrial asset;
performing a data cleanup operation on at least a portion of the accessed sensor data readings;
transforming at least the cleaned-up portion of the accessed sensor data readings to time series feature space sensor data;
applying a multi-kernel-based projection algorithm to the time series feature space sensor data;
computing a respective anomaly score and a respective ranking for one or more variables of the sensor data readings; and
providing at least the computed respective anomaly score or the respective ranking for at least one of the one or more variables to a user.
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Abstract
A method of temporal anomaly detection includes accessing sensor data readings obtained at a monitored industrial asset, performing a data cleanup operation on at least a portion of the accessed sensor data readings, transforming at least the cleaned-up portion of the accessed sensor data readings to time series feature space sensor data, applying a multi-kernel-based projection algorithm to the time series feature space sensor data, computing a respective anomaly score and a respective ranking for one or more variables of the sensor data readings, and providing at least the computed respective anomaly score or the respective ranking for at least one of the one or more variables to a user. Ranking the anomaly score includes comparing each anomaly score to a threshold and then assigning a ranking to scores with a magnitude greater than the threshold based on its magnitude. A system and a non-transitory computer-readable medium are also disclosed.
2 Citations
15 Claims
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1. A method of temporal anomaly detection, the method comprising:
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accessing sensor data readings obtained at a monitored industrial asset; performing a data cleanup operation on at least a portion of the accessed sensor data readings; transforming at least the cleaned-up portion of the accessed sensor data readings to time series feature space sensor data; applying a multi-kernel-based projection algorithm to the time series feature space sensor data; computing a respective anomaly score and a respective ranking for one or more variables of the sensor data readings; and providing at least the computed respective anomaly score or the respective ranking for at least one of the one or more variables to a user. - View Dependent Claims (2, 3, 4, 5)
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6. A non-transitory computer-readable medium having stored thereon instructions which when executed by a control processor cause the control processor to perform a method of temporal anomaly detection, the method comprising:
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accessing sensor data readings obtained at a monitored industrial asset; performing a data cleanup operation on at least a portion of the accessed sensor data readings; transforming at least the cleaned-up portion of the accessed sensor data readings to time series feature space sensor data; applying a multi-kernel-based projection algorithm to the time series feature space sensor data; computing a respective anomaly score and a respective ranking for one or more variables of the sensor data readings; and providing at least the computed respective anomaly score or the respective ranking for at least one of the one or more variables to a user. - View Dependent Claims (7, 8, 9, 10)
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11. A system for temporal anomaly detection, the system comprising:
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a control processor in communication with a data store across an electronic communication network, the control processor including a processor unit; the data store including executable instructions and sensor data records representing monitored conditions of one or more components of an industrial asset; the executable instructions when executed by the processor unit cause the processor unit to perform a method, the method comprising; accessing sensor data readings obtained at a monitored industrial asset; performing a data cleanup operation on at least a portion of the accessed sensor data readings; transforming at least the cleaned-up portion of the accessed sensor data readings to time series feature space sensor data; applying a multi-kernel-based projection algorithm to the time series feature space sensor data; computing a respective anomaly score and a respective ranking for one or more variables of the sensor data readings; and providing at least the computed respective anomaly score or the respective ranking for at least one of the one or more variables to a user. - View Dependent Claims (12, 13, 14, 15)
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Specification