Automated self-learning diagnostic system
First Claim
1. A self-learning diagnostic system for a population of networked machines, comprising:
- a nominal diagnostic threshold setting module for initiating self-learning, wherein initiating self-learning includes setting initial machine component thresholds based on a composite model of the machines within the population of networked machines;
a threshold adjustment module for continuously analyzing real time machine performance data received from each of the networked machines to identify performance trends among the networked machines, wherein said performance trends are utilized to develop new machine component thresholds;
a service records evaluation module; and
an adjustment module.
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Abstract
A self-learning diagnostic system provides diagnostics capabilities which may be applied to a population of networked machines or components and assemblies in a product. The self-learning diagnostic system uses both the components'"'"' own historical data and the data for an entire population of networked machines of a given product in the field as the training set to adjust critical threshold parameters for detection and diagnosis. The system includes a nominal diagnostic threshold setting module which sets initial thresholds and an adjustment module, which adjusts thresholds continuously based on machine performance data. A service records evaluation module checks service records periodically for correlations and an adjustment module adjust service strategies based on correlation data.
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Citations
20 Claims
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1. A self-learning diagnostic system for a population of networked machines, comprising:
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a nominal diagnostic threshold setting module for initiating self-learning, wherein initiating self-learning includes setting initial machine component thresholds based on a composite model of the machines within the population of networked machines; a threshold adjustment module for continuously analyzing real time machine performance data received from each of the networked machines to identify performance trends among the networked machines, wherein said performance trends are utilized to develop new machine component thresholds; a service records evaluation module; and an adjustment module. - View Dependent Claims (2, 3, 4, 5, 6, 7, 18)
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8. A method for operating a self-learning diagnostic system for a population of networked machines, comprising:
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receiving initial machine data; determining a nominal set of diagnostic set points and thresholds, wherein said nominal set of thresholds is based on a composite model of the machines within the population of networked machines; perfonning self-learning diagnostics comprising; collecting machine performance data from the population of networked machines; analyzing real time performance data from the networked machines to identity performance trends; utilizing said performance trends to develop new machine component thresholds; updating set points with said new machine component; checking machine service records for undetected failures; updating or adding machine service rules; and adjusting diagnostic thresholds; repeating performance of self-learning diagnostics continuously during operation of the population of networked machines; and providing feedback to engineering divisions. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16, 17)
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19. A method for operating a self-learning diagnostic system for a population of networked machines, comprising:
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receiving initial machine data; determining a nominal set of diagnostic set points and thresholds, wherein said nominal set of thresholds is based on a composite model of the machines within the population of networked machines; collecting machine performance data from field machines; analyzing real time performance data from the networked machines to identify performance trends; utilizing said performance trends to develop new machine component thresholds; updating said nominal set points and thresholds based on said new machine component thresholds; and providing feedback to engineering divisions.
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20. An article of manufacture comprising a computer usable medium having computer readable program code embodied in said medium which, when said program code is executed by said computer causes said computer to perform method steps for operating a self-learning diagnostic system for a population of networked machines, said method comprising:
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receiving initial machine data; determining a nominal set of diagnostic set points and thresholds, wherein said nominal set of thresholds is based on a composite model of the machines within the population of networked machines; performing self-learning diagnostics comprising; collecting machine performance data from the population of networked machines; analyzing real time performance data from the networked machines to identity performance trends; utilizing said performance trends to develop new machine component thresholds; updating set points with said new machine component thresholds; checking machine service records for undetected failures; updating or adding machine service rules; and adjusting diagnostic thresholds; repeating performance of self-learning diagnostics continuously during operation of the population of networked machines; and providing feedback to engineering divisions.
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Specification