Fluid flow rate assessment by a non-intrusive sensor in a fluid transfer pump system

Patent metadata

Field	Value
Publication number	US-10041844-B1
Application number	US-201715482597-A
Country	US
Kind code	B1
Filing date	Apr 7, 2017
Priority date	Apr 7, 2017
Publication date	Aug 7, 2018
Grant date	Aug 7, 2018

Abstract

Embodiments for assessing energy in a fluid transfer pump system in a cloud computing environment by a processor. A fluid flow rate may be cognitively determined according to a tracer stimulus, injected into the fluid transfer pump system, and adequately detected by one or more Internet of Things (IoT) sensors located at one or more selected positions of a piping network in the fluid transfer pump system.

First claim

The invention claimed is:

1. A method for assessing fluid flow rate in a fluid transfer pump system in a cloud computing environment by a processor, comprising:
cognitively determining a fluid flow rate according to a tracer stimulus, injected into the fluid transfer pump system, by one or more non-intrusive Internet of Things (IoT) sensors located at one or more selected positions of a piping network in the fluid transfer pump system; and
initializing a machine learning mechanism using the feedback information from the one or more non-intrusive IoT sensors to provide a cognitive estimate of an energy output of the fluid transfer pump system.

2. The method of claim 1 , further including detecting the tracer stimulus, injected into the fluid transfer pump system at a selected location and at a selected time period, by the one or more non-intrusive IoT sensors located on one or more pipes of a fluid return section of the piping network in the fluid transfer pump system, wherein the one or more non-intrusive IoT sensors are in an IoT computing network.

3. The method of claim 1 , further including cognitively estimating the fluid flow rate according to the detected tracer stimulus based on a first timestamp and a second timestamp.

4. The method of claim 1 , further including implementing a series of rules and parameters for injecting the tracer stimulus into the fluid transfer pump system and setting one or more parameters of the one or more non-intrusive IoT sensors, wherein the tracer stimulus is an adjustable tracer stimuli.

5. The method of claim 1 , further including determining a health state of the fluid transfer pump system using the one or more non-intrusive IoT sensors.

6. The method of claim 1 , further including:
defining one or more settings of the one or more non-intrusive IoT sensors to enable the one or more non-intrusive IoT sensors to detect a temperature injection tracer stimuli, wherein the one or more non-intrusive IoT sensors are coupled to the fluid transfer pump system at one or more defined distances from an alternative non-intrusive sensor;
detecting the temperature injection tracer stimuli by the one or more non-intrusive IoT sensors, wherein the alternative non-intrusive sensor measures a start time of the temperature injection tracer stimuli and the one or more non-intrusive IoT sensors detects an arrival time of the temperature injection tracer stimuli;
cognitively determining the fluid flow rate and estimating an energy output of the fluid transfer pump system based on the detected temperature injection tracer stimuli; and
providing the energy output or fluid flow rate to a user via an interactive graphical user interface (GUI).

7. The method of claim 1 , further including using a single data point sampled over a selected time period by the one or more non-intrusive IoT sensors in the fluid transfer pump system associated with a heating service, a cooling service, or a combination thereof.

8. A system for assessing fluid flow rate in a fluid transfer pump system in a cloud computing environment, comprising:
one or more computers with executable instructions that when executed cause the system to:
cognitively determine a fluid flow rate according to a tracer stimulus, injected into the fluid transfer pump system, by one or more non-intrusive Internet of Things (IoT) sensors located at one or more selected positions of a piping network in the fluid transfer pump system; and
initialize a machine learning mechanism using the feedback information from the one or more non-intrusive IoT sensors to provide a cognitive estimate of an energy output of the fluid transfer pump system.

9. The system of claim 8 , wherein the executable instructions further detect the tracer stimulus, injected into the fluid transfer pump system at a selected location and at a selected time period, by the one or more non-intrusive IoT sensors located on one or more pipes of a fluid return section of the piping network in the fluid transfer pump system.

10. The system of claim 8 , wherein the executable instructions further cognitively estimate the fluid flow rate according to the detected tracer stimulus based on a first timestamp and a second timestamp.

11. The system of claim 8 , wherein the executable instructions further implement a series of rules and parameters for injecting the tracer stimulus into the fluid transfer pump system and setting one or more parameters of the one or more non-intrusive IoT sensors, wherein the tracer stimulus is an adjustable tracer stimuli.

12. The system of claim 8 , wherein the executable instructions further determine a health state of the fluid transfer pump system using the one or more non-intrusive IoT sensors.

13. The system of claim 8 , wherein the executable instructions further:
define one or more settings of the one or more non-intrusive IoT sensors to enable the one or more non-intrusive IoT sensors to detect a temperature injection tracer stimuli, wherein the one or more non-intrusive IoT sensors are coupled to the fluid transfer pump system at one or more defined distances from an alternative non-intrusive sensor;
detect the temperature injection tracer stimuli by the one or more non-intrusive IoT sensors, wherein the alternative non-intrusive sensor measures a start time of the temperature injection tracer stimuli and the one or more non-intrusive IoT sensors detects an arrival time of the temperature injection tracer stimuli;
cognitively determine the fluid flow rate and estimate an energy output of the fluid transfer pump system based on the detected temperature injection tracer stimuli; and
provide the energy output or fluid flow rate to a user via an interactive graphical user interface (GUI).

14. The system of claim 8 , wherein the executable instructions further use a single data point sampled over a selected time period by the one or more non-intrusive IoT sensors in the fluid transfer pump system associated with a heating service, a cooling service, or a combination thereof.

15. A computer program product for assessing fluid flow rate in a fluid transfer pump system in a cloud computing environment by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:
an executable portion that cognitively determines a fluid flow rate according to a tracer stimulus, injected into the fluid transfer pump system, by one or more non-intrusive Internet of Things (IoT) sensors located at one or more selected positions of a piping network in the fluid transfer pump system; and
an executable portion that initializes a machine learning mechanism using the feedback information from the one or more non-intrusive IoT sensors to provide a cognitive estimate of an energy output of the fluid transfer pump system.

16. The computer program product of claim 15 , further including an executable portion that detects the tracer stimulus, injected into the fluid transfer pump system at a selected location and at a selected time period, by the one or more non-intrusive IoT sensors located on one or more pipes of a fluid return section of the piping network in the fluid transfer pump system.

17. The computer program product of claim 15 , further including an executable portion that cognitively estimates the fluid flow rate according to the detected tracer stimulus based on a first timestamp and a second timestamp.

18. The computer program product of claim 15 , further in

Assignees

• IBM

Inventors

• Brady Niall
• Harpur Liam
• Palmes Paulito P

Classifications

• G01F1/684

  Structural arrangements; Mounting of elements, e.g. in relation to fluid flow · CPC title

• G01K17/06 Primary

  Measuring quantity of heat conveyed by flowing media, e.g. in heating systems (G01K17/02, G01K17/04 take precedence){e.g. the quantity of heat in a transporting medium, delivered to or consumed in an expenditure device} · CPC title

• G01F1/6842

  with means for influencing the fluid flow · CPC title

• G01K17/08

  based upon measurement of temperature difference {or of a temperature} · CPC title

• G01F1/6847

  where sensing or heating elements are not disturbing the fluid flow, e.g. elements mounted outside the flow duct · CPC title