IP Library Granted Patent US 11,328,583
Granted Patent B2
US 11,328,583 · App. 17/324,987 · Granted May 10, 2022

Dynamic gauges for displaying present and predicted machine status

Inventors: Samuel Friedman (Seattle, WA); Cosmo King (Bellevue, WA); Bhaskar Bhattacharyya (Seattle, WA)
Assignee: ioCurrents, Inc.
G08B31/00G06N5/04G06N20/00G06T11/206G08B21/182H04Q9/02H04Q2213/002
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Quick Facts
Patent No.
US 11,328,583
App. No.
17/324,987
Granted
May 10, 2022
Kind
B2
Abstract

A facility creates a dynamic gauge for indicating the status of equipment. The facility causes a gauge to be displayed with an initial satisfactory range and an initial unsatisfactory range. The facility accesses historical data describing the status of a sensor attached to equipment. The facility determines a new satisfactory range and a new unsatisfactory range, and alters the gauge to visually indicate the new satisfactory range and the new unsatisfactory range.

Claims (72)

1. A system to create a dynamic gauge for equipment, the system comprising:

a computing device;

a gauge array including a gauge, the gauge being capable of indicating a range of values and being adapted to visually indicate an initial satisfactory sub-range of the gauge's range of values, the initial satisfactory sub-range being based on an initial condition of the equipment;

a memory adapted to store historical data, wherein the historical data includes data describing operational history of the equipment;

the computing device being adapted to automatically determine a new satisfactory sub-range based on the historical data, and

the gauge being adapted to be updated automatically by the computing device to visually indicate the new satisfactory sub-range.

2. The system of claim 1 , wherein the computing device determines a new satisfactory sub-range by performing time-based statistical analysis of the historical data.

3. The system of claim 1 , wherein the computing device is further adapted to:

periodically determine the new satisfactory sub-range by:

receiving additional historical data; and

determining a new satisfactory sub-range based on at least the additional historical data.

4. The system of claim 1 , wherein the computing device is further adapted to:

receive current operational data of the equipment; and

determine a new satisfactory sub-range based on the historical data and current operational data.

5. The system of claim 1 , wherein the computing device is further adapted to:

receive current operational data of the equipment; and

update the gauge to visually indicate the current operational data of the equipment.

6. The system of claim 5 , wherein the computing device is further adapted to:

determine a new satisfactory sub-range based on the historical data and current operational data by:

applying to the current operational data a machine learning model trained to predict a satisfactory sub-range based on the historical data to generate the new satisfactory sub-range.

7. The system of claim 5 , wherein the computing device is further adapted to:

determine a new satisfactory sub-range based on the historical data and current operational data by:

training a machine learning model to predict a satisfactory range sub-range based on the historical data; and

applying to the current operational data the machine learning model trained to predict a satisfactory sub-range based on the historical data to generate the new satisfactory sub-range.

8. A method in a computing device, the method comprising:

causing a gauge array to be displayed, the gauge array including a gauge capable of indicating a range of values and being adapted to visually indicate an initial satisfactory sub-range of the gauge's range of values, the initial satisfactory sub-range being based on an initial condition of equipment;

accessing historical data, wherein the historical data includes data describing operational history of the equipment;

automatically determining a new satisfactory sub-range based on the historical data; and

automatically updating the gauge to visually indicate the new satisfactory sub-range.

9. The method of claim 8 , wherein determining a new satisfactory sub-range further comprises:

applying time-based statistical analysis to the historical data.

10. The method of claim 8 , further comprising:

periodically determining the new satisfactory sub-range by:

receiving additional historical data; and

determining a new satisfactory sub-range based on at least the additional historical data.

11. The method of claim 8 , further comprising:

receiving current operational data of the equipment; and

updating the gauge to visually indicate the current operational data of the equipment.

12. The method of claim 8 , further comprising:

receiving current operational data of the equipment; and

determining the new satisfactory sub-range based on the historical data and current operational data.

13. The method of claim 12 , further comprising:

determining the new satisfactory sub-range based on the historical data and current operational data by:

applying the current operational data to a machine learning model trained to predict a satisfactory sub-range based on the historical data to generate the new satisfactory sub-range.

14. The method of claim 12 , further comprising:

determining the new satisfactory sub-range based on the historical data and current operational data by:

training a machine learning model to predict a satisfactory sub-range based on the historical data; and

applying the current operational data to a machine learning model trained to predict a satisfactory range sub-range based on the historical data to generate the new satisfactory range sub-range.

15. One or more memories collectively configured to cause a computing device to perform a method, the method comprising:

causing a gauge array to be displayed, the gauge array including a gauge capable of indicating a range of values and being adapted to visually indicate an initial satisfactory sub-range of the gauge's range of values, the initial satisfactory sub-range being based on an initial condition of equipment;

accessing historical data, wherein the historical data includes data describing operational history of the equipment;

automatically determining a new satisfactory sub-range based on the historical data; and

automatically updating the gauge to visually indicate the new satisfactory sub-range.

16. The method of claim 15 , wherein determining a new satisfactory sub-range further comprises:

applying time-based statistical analysis to the historical data.

17. The method of claim 15 , further comprising:

periodically determining the new satisfactory sub-range by:

receiving additional historical data; and

determining a new satisfactory sub-range based on at least the additional historical data.

18. The method of claim 15 , further comprising:

receiving current operational data of the equipment; and

updating the gauge to visually indicate the current operational data of the equipment.

19. The method of claim 15 , further comprising:

receiving current operational data of the equipment; and

determining the new satisfactory sub-range based on the historical data and current operational data.

20. The method of claim 19 , further comprising:

determining the new satisfactory sub-range based on the historical data and current operational data by:

applying to the current operational data a machine learning model trained to predict a satisfactory sub-range based on the historical data to generate the new satisfactory sub-range.

21. The method of claim 19 , further comprising:

determining the new satisfactory sub-range based on the historical data and current operational data by:

training a machine learning model to predict a satisfactory sub-range based on the historical data; and

applying to the current operational data a machine learning model trained to predict a satisfactory sub-range based on the historical data to generate the new satisfactory sub-range.

Continuity (2)
Continuation 16883924 · May 26, 2020
Related Publication 20210375123A1 · Dec 2, 2021