IP Library Granted Patent US 11,836,571
Granted Patent B2
US 11,836,571 · App. 17/558,811 · Granted Dec 5, 2023

Systems and methods for enabling user selection of components for data collection in an industrial environment

Inventors: Charles Howard Cella (Pembroke, MA); Gerald William Duffy, Jr. (Philadelphia, PA); Jeffrey P. McGuckin (Philadelphia, PA); Mehul Desai (Oak Brook, IL)
Assignee: Strong Force IoT Portfolio 2016, LLC
G06N3/006G01M13/028G01M13/04G01M13/045G05B13/028G05B19/4183G05B19/4184G05B19/4185G05B19/41845G05B19/41865G05B19/41875G05B23/024G05B23/0221G05B23/0229G05B23/0264G05B23/0283G05B23/0286G05B23/0289G05B23/0291G05B23/0294G05B23/0297G06F18/2178G06N3/02G06N3/044G06N3/045G06N3/047G06N3/084G06N3/088G06N5/046G06N7/01G06N20/00G06Q10/04G06Q10/0639G06Q30/02G06Q30/0278G06Q30/06G06Q50/00G06V10/7784G16Z99/00H02M1/12H03M1/12H04B17/23H04B17/309H04B17/318H04B17/345H04L1/0002H04L1/0041H04L1/18H04L1/1874H04L67/1097H04L67/12H04W4/38H04W4/70B62D5/0463G05B19/042G05B23/02G05B23/0208G05B2219/32287G05B2219/35001G05B2219/37337G05B2219/37351G05B2219/37434G05B2219/37537G05B2219/40115G05B2219/45004G05B2219/45129G06F17/18G06F18/21G06F18/217G06F18/25G06N3/126H04B17/29H04B17/40H04L1/0009H04L5/0064H04L67/306Y02P80/10Y02P90/02Y02P90/80Y04S50/00Y04S50/12Y10S707/99939
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 11,836,571
App. No.
17/558,811
Granted
Dec 5, 2023
Kind
B2
Abstract

Systems and methods for data collection in an industrial environment are disclosed. An expert graphical user interface showing representations of components of an industrial machine to which sensors are attach is disclosed. The user interface may enable a user to select at least one of the components resulting in a search of a database of industrial machine failure modes for modes that correspond to the selected component. The corresponding failure mode may be presented to the user. The selection of the component may cause a controller to reference and implement a data collection template for configuring the system to automatically collect data from sensors associated with the selected component to detect at least one of the corresponding failure modes.

Claims (47)

1. A system comprising:

an expert graphical user interface comprising representations of a plurality of components of an industrial machine from an industrial environment in which a plurality of sensors is deployed and from which a data routing and collection system collects data for the system, wherein the expert graphical user interface enables interaction, and wherein at least one representation of the plurality of components is selectable by a user of the expert graphical user interface;

a database of industrial machine failure modes; and

a database searching facility configured to search the database of industrial machine failure modes for one or more industrial machine failure modes that correspond to a user selection of a component of the plurality of components via the selection, by the user, of the at least one representation;

wherein:

the user selection of the component causes a controller to reference and implement a data collection template for configuring the data routing and collection system, thereby configuring the data routing and collection system to automatically collect data from one or more sensors, of the plurality of sensors, associated with the selected component to detect the corresponding one or more industrial machine failure modes; and

the expert graphical user interface is further configured to provide the corresponding one or more industrial machine failure modes to the user.

2. The system of claim 1 , further comprising a database of a plurality of conditions associated with the one or more industrial machine failure modes.

3. The system of claim 2 , wherein the database of the plurality of conditions comprises a list of one or more sensors, of the plurality of sensors, in the industrial environment associated with the plurality of conditions.

4. The system of claim 3 , wherein the database searching facility is further configured to search the database of the plurality of conditions for one or more sensors, of the plurality of sensors, that correspond to at least one condition of the plurality of conditions, and wherein the expert graphical user interface is further configured to indicate the corresponding one or more sensors.

5. The system of claim 1 , wherein the expert graphical user interface is further configured to present a list of reliability measures of the industrial machine, and at least one reliability measure from the list of reliability measures is selected by the user.

6. The system of claim 5 , wherein the list of reliability measures comprises at least one of: industry average data, manufacturer's specifications, manufacturer's material specifications, or manufacturer's recommendations.

7. The system of claim 5 , wherein the list of reliability measures correlates to at least one failure including at least one of: stress, vibration, heat, wear, ultrasonic signature, or operational deflection shape effect, and wherein the at least one failure is associated with the one or more industrial machine failure modes.

8. A computer-implemented method comprising:

providing representations of a plurality of components of an industrial machine from an industrial environment via an expert graphical user interface;

deploying a plurality of sensors for collecting data related to the plurality of components of the industrial machine from the industrial environment;

selecting at least one representation of the plurality of components by a user;

searching a database of industrial machine failure modes for one or more industrial machine failure modes that correspond to the user selection of the at least one representation of the plurality of components;

responsive to the user selection of the at least one representation of the plurality of components, causing a controller to reference and implement a data collection template for configuring a data routing and collection system;

automatically collecting, via the configured data routing and collection system, data from one or more sensors, of the plurality of sensors, associated with the selected at least one representation of the plurality of components to detect the corresponding one or more industrial machine failure modes; and

providing the corresponding one or more industrial machine failure modes to the user via the expert graphical user interface.

9. The computer-implemented method of claim 8 , further comprising associating a database of a plurality of conditions with the one or more industrial machine failure modes.

10. The computer-implemented method of claim 9 , wherein the database of the plurality of conditions comprises a list of one or more sensors, of the plurality of sensors, in the industrial environment associated with the plurality of conditions.

11. The computer-implemented method of claim 10 , further comprising:

searching the database of the plurality of conditions for one or more sensors, of the plurality of sensors, that correspond to at least one condition of the plurality of conditions; and

indicating the corresponding one or more sensors via the expert graphical user interface.

12. The computer-implemented method of claim 8 , further comprising:

presenting a list of reliability measures of the industrial machine; and

selecting at least one reliability measure from the list of reliability measures.

13. The computer-implemented method of claim 12 wherein the list of reliability measures comprises at least one of: industry average data, manufacturer's specifications, manufacturer's material specifications, or manufacturer's recommendations.

14. The computer-implemented method of claim 12 , wherein the list of reliability measures correlates to at least one failure including at least one of: stress, vibration, heat, wear, ultrasonic signature, or operational deflection shape effect, and wherein the at least one failure is associated with the one or more industrial machine failure modes.

15. A non-transitory computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising:

providing representations of a plurality of components of an industrial machine from an industrial environment via an expert graphical user interface;

deploying a plurality of sensors for collecting data related to the plurality of components of the industrial machine from the industrial environment;

selecting at least one representation of the plurality of components by a user;

searching a database of industrial machine failure modes for one or more industrial machine failure modes that correspond to the user selection of the at least one representation of the plurality of components;

responsive to the user selection of the at least one representation of the plurality of components, configuring based at least in part on a data collection template, a data routing and collection system to automatically collect data from one or more sensors, of the plurality of sensors, associated with the selected at least one representation to detect the corresponding one or more industrial machine failure modes; and

providing the corresponding one or more industrial machine failure modes to the user via the expert graphical user interface.

16. The non-transitory computer readable storage medium of claim 15 , wherein the operations performed by the one or more processors further comprise associating a database of a plurality of conditions with the one or more industrial machine failure modes.

17. The non-transitory computer readable storage medium of claim 15 , wherein the operations performed by the one or more processors further comprise:

presenting a list of reliability measures of the industrial machine; and

selecting at least one reliability measure from the list of reliability measures.

18. The system of claim 1 , wherein the data collection template is structured to cause the controller to perform smart-band signal analysis of the one or more sensors; and, in response to a result of the smart-band signal analysis, configure the data and routing collection system to automatically collect data from two or more sensors, of the plurality of sensors, concurrently, wherein the two or more sensors include the one or more sensors associated with the selected component to detect the corresponding one or more industrial machine failure modes.

19. The system of claim 18 , wherein the data collection template is structured to cause the controller to configure a memory device of the data and routing collection system to repeatedly sample each of the two or more sensors at least one of synchronously or based at least in part on a known offset.

20. The system of claim 19 , wherein the data collection template comprises at least one of:

one or more identifiers for the two or more sensors; or

one or more identifiers for the data automatically collected by the two or more sensors.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 12, 2022
From: CELLA, CHARLES HOWARD; DUFFY, GERALD WILLIAM, JR; MCGUCKIN, JEFFREY P.; DESAI, MEHUL
To: STRONG FORCE IOT PORTFOLIO 2016, LLC
Reel/Frame 059575/0368 →
Continuity (15)
Division 16224727 · Dec 18, 2018
Continuation 16143286 · Sep 26, 2018
Continuation PCTUS2018045036 · Aug 2, 2018
Continuation 15973406 · May 7, 2018
Continuation 15973406 · May 7, 2018
Continuation In Part PCTUS2017031721 · May 9, 2017
Provisional Application 62583487 · Nov 8, 2017
Provisional Application 62562487 · Sep 24, 2017
Provisional Application 62540557 · Aug 2, 2017
Provisional Application 62540513 · Aug 2, 2017
Provisional Application 62427141 · Nov 28, 2016
Provisional Application 62412843 · Oct 26, 2016
Provisional Application 62350672 · Jun 15, 2016
Provisional Application 62333589 · May 9, 2016
Related Publication 20220187822A1 · Jun 16, 2022
Cited By (2)
US 12,372,946 US 12,663,771