Fleet anomaly detection method
First Claim
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1. A method for determining whether an operational metric representing the performance of a target machine has an anomalous value, the method comprising:
- collecting operational data from at least one machine; and
calculating at least one exceptional anomaly score from said operational data.
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Abstract
A method for determining whether an operational metric representing the performance of a target machine has an anomalous value is provided. The method includes collecting operational data from at least one machine, and calculating at least one exceptional anomaly score from the obtained operational data.
53 Citations
20 Claims
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1. A method for determining whether an operational metric representing the performance of a target machine has an anomalous value, the method comprising:
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collecting operational data from at least one machine; and calculating at least one exceptional anomaly score from said operational data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for determining whether an operational metric representing the performance of a target machine has an anomalous value, the method comprising:
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collecting operational data from at least one machine; calculating at least one exceptional anomaly score from said operational data; aggregating said operational data; creating at least one sensitivity setting for said at least one exceptional anomaly score; creating at least one alert, said at least one alert based on, at least one of, said at least one exceptional anomaly score and said operational data; and creating at least one heatmap, said at least one heatmap visually illustrating at least one of said at least one exceptional anomaly score and said operational data. - View Dependent Claims (10, 11, 12, 13, 14)
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15. A method for determining whether an operational metric representing the performance of a target machine has an anomalous value, the method comprising:
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collecting operational data from at least one machine; calculating at least one exceptional anomaly score from said operational data; aggregating said operational data; creating at least one sensitivity setting for said at least one exceptional anomaly score; creating at least one alert, said at least one alert based on, at least one of, said at least one exceptional anomaly score and said operational data; and creating at least one heatmap, said at least one heatmap visually illustrating at least one of said at least one exceptional anomaly score and said operational data. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification