METHOD AND SYSTEM FOR DIAGNOSING FAULTS IN A PARTICULAR DEVICE WITHIN A FLEET OF DEVICES
First Claim
1. A method for diagnosing faults in a particular device within a fleet of devices, the method comprising:
- receiving performance data related to one or more parameters associated with a fleet of devices;
processing the performance data to detect one or more trend shifts in the one or more parameters;
detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device;
generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices, wherein the fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices;
computing one or more scaling factors for the particular parameter associated with the particular device;
scaling the one or more of fuzzy rules defined for the one or more parameters in the fleet-based diagnostic model, based on the one or more scaling factors, to generate a personalized diagnostic model for the particular parameter associated with the particular device; and
evaluating the personalized diagnostic model against the one or more trend shifts detected for the one or more parameters, to diagnose a fault associated with the particular device.
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Abstract
A method for diagnosing faults in a particular device within a fleet of devices is provided. The method comprises receiving performance data related to one or more parameters associated with a fleet of devices and processing the performance data to detect one or more trend shifts in the one or more parameters. The method then comprises detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device. The method further comprises generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices. The fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices. The method then comprises computing one or more scaling factors for the particular parameter associated with the particular device and scaling the one or more of fuzzy rules defined for the one or more parameters in the fleet-based diagnostic model, based on the one or more scaling factors, to generate a personalized diagnostic model for the particular parameter associated with the particular device. The method finally comprises evaluating the personalized diagnostic model against the one or more trend shifts detected for the one or more parameters, to diagnose a fault associated with the particular device.
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Citations
20 Claims
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1. A method for diagnosing faults in a particular device within a fleet of devices, the method comprising:
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receiving performance data related to one or more parameters associated with a fleet of devices; processing the performance data to detect one or more trend shifts in the one or more parameters; detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device; generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices, wherein the fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices; computing one or more scaling factors for the particular parameter associated with the particular device; scaling the one or more of fuzzy rules defined for the one or more parameters in the fleet-based diagnostic model, based on the one or more scaling factors, to generate a personalized diagnostic model for the particular parameter associated with the particular device; and evaluating the personalized diagnostic model against the one or more trend shifts detected for the one or more parameters, to diagnose a fault associated with the particular device. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A system for diagnosing faults in a particular device within a fleet of devices, the system comprising:
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a trend shift detection component, configured to receive performance data related to one or more parameters associated with a fleet of devices and process the performance data to detect one or more trend shifts in the one or more parameters; a data detrending component configured to detrend the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device; a fleet-based diagnostic model configured to generate trend patterns and data characteristics associated with the fleet of devices, wherein the fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices; a scaling component configured to compute one or more scaling factors for the particular parameter associated with the particular device; a personalized diagnostic model component configured to scale the one or more of fuzzy rules defined for the one or more parameters in the fleet-based diagnostic model, based on the one or more scaling factors, to generate a personalized diagnostic model for the particular parameter associated with the particular device; and a diagnosis component configured to evaluate the personalized diagnostic model against the one or more trend shifts detected for the one or more parameters, to diagnose a fault associated with the particular device. - View Dependent Claims (14, 15, 16, 17, 18, 19)
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20. A method for diagnosing faults for a subset of devices in a fleet of devices, the method comprising:
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receiving performance data related to one or more parameters associated with a fleet of devices; processing the performance data to detect one or more trend shifts in the one or more parameters; detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a subset of devices, wherein the subset of devices comprise devices in the fleet of devices having similar data characteristics and similar fault data; generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices, wherein the fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices; computing one or more scaling factors for the particular parameter associated with the subset of devices; scaling the one or more of fuzzy rules defined for the one or more parameters in the fleet-based diagnostic model, based on the one or more scaling factors to generate a personalized diagnostic model for the particular parameter associated with the subset of devices; and evaluating the personalized diagnostic model against the one or more trend shifts detected for the one or more parameters, to diagnose a fault associated with the subset of devices.
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Specification