Process for the monitoring and diagnostics of data from a remote asset
First Claim
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1. A method for processing monitored data from a remote asset to optimize maintenance and operation schedules, the method comprising:
- collecting data from the remote asset;
building a data set based on the data collected;
applying statistical scripts to the data set to create a statistical model;
comparing the statistical model to the data set;
creating a standardization model from the compared statistical model and the data set;
applying a trending algorithm to the data;
deriving statistical based control limits;
applying the control limits to a new set of collected data;
trending information using a time series modeling optimization technique for determining the remote asset'"'"'s maintenance and operation schedules;
developing optimized maintenance and operation schedules based on the trended information;
reporting the schedules to a user.
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Abstract
A method for processing monitored data from a remote asset to optimize maintenance and operation schedules, the method comprising the steps of collecting data from the remote asset, building a data set based on the data collected, applying statistical scripts to the data set to create a statistical model, comparing the statistical model to the data set, creating a standardization model from the compared statistical model and the data set, applying a trending algorithm to the data, deriving statistical based control limits, and applying the control limits to a new set of collected data.
67 Citations
23 Claims
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1. A method for processing monitored data from a remote asset to optimize maintenance and operation schedules, the method comprising:
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collecting data from the remote asset;
building a data set based on the data collected;
applying statistical scripts to the data set to create a statistical model;
comparing the statistical model to the data set;
creating a standardization model from the compared statistical model and the data set;
applying a trending algorithm to the data;
deriving statistical based control limits;
applying the control limits to a new set of collected data;
trending information using a time series modeling optimization technique for determining the remote asset'"'"'s maintenance and operation schedules;
developing optimized maintenance and operation schedules based on the trended information;
reporting the schedules to a user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
creating an anomaly definition; and
identifying variables to monitor.
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4. The method of claim 1 wherein the applying statistical scripts further comprises:
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using a statistical script for centering the data at a predetermined variable;
using a statistical script for running a stepwise regression script on the centered data;
using a statistical script for un-centering the data.
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5. The method of claim 1 further comprising eliminating extraneous variables discovered during comparing the statistical model to the data set.
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6. The method of claim 1 wherein applying a trending algorithm to the data further comprises determining control chart type limits.
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7. The method of claim 6 wherein determining control chart type limits comprises applying a time series modeling optimization technique to determine control chart type limits.
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8. The method of claim 1 wherein applying the control limits to a new set of data further comprising the steps of:
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recording the standardization model and data thresholds for implementation in an automated monitoring and diagnostic system; and
determining a remote asset'"'"'s maintenance and operation schedule.
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9. The method of claim 8 further comprising the step of notifying a user of results after determining a remote asset'"'"'s maintenance and operation schedule.
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10. A system for processing monitored data from a remote asset to evaluate and determine a status of the remote asset with minimum user interface, the system comprising:
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a remote asset;
a data gathering module located at the remote asset to collect data about the remote asset;
a monitoring and diagnostic service center;
respective network interfaces located at the remote asset and the monitoring and diagnostic service center;
a processor to manage sending, evaluating, and receiving the data;
a process residing in the processor which uses algorithms to build a data set based on the data gathered uses statistical scripts on the data to determine a statistical model, and uses a time series modeling optimization technique to trend information to determine the remote asset'"'"'s maintenance and operation schedules. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
a customer facility;
a repair depot;
respective network interfaces located at said customer facility and repair depot;
wherein the remote asset'"'"'s maintenance and operation schedules determined by the process are communicated from the monitoring and diagnostic service center to the customer facility and repair depot.
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13. The system of claim 10 wherein the process comprises a sub-process to compare the statistical model to the collected data.
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14. The system of claim 10 wherein the time series modeling optimization technique used in the process is an Exponentially Weighted Moving Average technique.
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15. The system of claim 14 wherein an Auto-Regressive Integrated Moving Average technique is used to calculate a value in the Exponentially Weighted Moving Average technique.
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16. The system of claim 10 wherein the processor further comprises a diagnostic compute engine to determine whether the data is within a specified operating range as determined by the process.
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17. The system of claim 10 wherein the results determined by the process are communicated to a user.
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18. A method for processing monitored data from a remote asset to determine whether the data is within a predetermined operating threshold, the method comprising:
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collecting monitored data from the remote asset;
providing a processor;
supplying the monitored data to the processor;
creating an anomaly definition which comprises acceptable parameters;
identifying a first variable;
identifying a second set of variables which are used to monitor the first variable;
deleting data that is outside of a specified parameter;
centering data about a specified variable;
running a stepwise regression algorithm;
determining whether data is within acceptable parameters;
un-centering data if data are within acceptable parameters;
running the stepwise regression algorithm on the un-centered data;
calculating residuals based on a calculated first variable compared to a predicted first variable;
building a standardized variable based on calculated residuals;
assessing data before and after standardization to determine whether results are within a predetermined limit;
trending the data to determine whether the assessed data is within the predetermined operating threshold;
developing optimized maintenance and operation schedules based on the trended data;
reporting the schedules to a user. - View Dependent Claims (19, 20, 21, 22, 23)
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Specification