FLUID FLOW DETECTION AND ANALYSIS DEVICE AND SYSTEM

• US 20140366612A1
• Filed: 12/04/2013
• Published: 12/18/2014
• Est. Priority Date: 12/04/2012
• Status: Active Grant

• Current Assignee: AquaSeca, Inc.
• Original Assignee: Robert L. Macdonald, Stephen J. Horne
• Inventors: Horne, Stephen J., MacDonald, Robert L.
• Granted Patent: US 9,664,589 B2
• Time in Patent Office: Days
• US Class Current: 73/40.5A
• CPC Class Codes: F17D 5/06 using electric or acoustic means
• CPC Class Codes: G01F 1/66 by measuring frequency, phase shift or propagation time of electromagnetic or other waves, e.g. using ultrasonic flowmeters
• CPC Class Codes: G01M 3/243 for pipes
• CPC Class Codes: G01M 3/2807 for pipes G01M3/2892, G01M3/30 take precedence

Abstract

A fluid monitoring system has a plurality of sensors, the sensors being acoustically coupled to a pipe system containing interconnected fluid-transporting pipes. The sensors collect sensor data that is analyzed by an analysis system to provide leak identification on the pipe system. Each sensor in the plurality of sensors is configured to be programmed adaptively by the analysis system to improve system performance parameters. A method for analyzing fluid flow is also described in which data from a plurality of acoustic sensing devices is analyzed using learning algorithms.

Claims

1. A fluid flow monitoring system comprising:

a plurality of sensors, the sensors being acoustically coupled to a pipe system containing interconnected fluid-transporting pipes, the sensors collecting sensor data; and

an analysis system housed with a central processing unit, the analysis system analyzing the sensor data from the plurality of sensors to provide leak identification in the interconnected fluid-transporting pipes;

wherein each sensor in the plurality of sensors is configured to be programmed adaptively to improve system performance parameters.

2. The system of claim 1 wherein the sensors are battery powered.

3. The system of claim 1 wherein the sensors are coupled non-invasively to the pipe system.

4. The system of claim 1 wherein the system performance parameters include battery life, acoustic sensitivity, leak localization and false alarm rate.

5. The system of claim 1 wherein at least one sensor is programmable.

6. The system of claim 5 wherein the at least one sensor is programmable to improve a sensitivity of the leak identification.

7. The system of claim 5 wherein the at least one sensor is battery powered, the at least one sensor being programmable to increase an operating time of the battery.

8. The system of claim 7 wherein the increase in operating time includes reducing the duty cycle during extended periods of low fluid flow.

9. The system of claim 5 wherein the at least one sensor is at least partially directed by instructions from the central processing unit.

10. A method for analyzing fluid flow, the method comprising:

providing a plurality of acoustic sensing devices, the acoustic sensing devices having i) a microphone or accelerometer, ii) an analog to digital converter, and iii) a microcontroller;

collecting data from the plurality of acoustic sensing devices; and

analyzing the data using learning algorithms, wherein the learning algorithms improve accuracy and completeness of information about a distribution of flow rates in a pipe system by at least one of;

a) comparing processed acoustic signatures with stored templates and historical data, b) altering parameters in the sensing devices according to results of comparisons of processed acoustic signatures with the stored templates and historical data, c) altering parameters in the sensing devices based upon acoustic characteristics proximal to the sensors, and d) altering parameters in the sensing devices based upon analyses of the operation of other similarly instrumented pipe systems;

wherein the collecting and analyzing are performed on a computer hardware processor.

11. The method of claim 10 wherein the learning algorithms operate on inaccurate and incomplete data.

12. The method of claim 10 wherein the learning algorithms are specifically trained to characteristics of the pipe system.

13. A fluid leak detection apparatus comprising:

a plurality of sensing devices, each sensing device comprising;

an acoustic sensor, wherein the acoustic sensor is capable of detecting acoustic signals from a pipe used to transport a fluid;

a battery electrically connected to the acoustic sensor, the battery having capacity less than 1000 mAh;

a housing containing the acoustic sensor and the battery; and

a non-invasive coupling element configured to couple the housing (i) to the pipe or (ii) to a location near the pipe; and

a processing component in communication with the plurality of sensing devices, the processing component being capable of performing analysis on data captured from the plurality of sensing devices, wherein a system leak detection sensitivity analyzed by the processing component is higher than a leak detection sensitivity of the sensing devices.

14. The system of claim 13 wherein the acoustic sensor comprises an accelerometer with sensitivity below 4000 mV/g, or a microphone with sensitivity less than −

30 dBV/Pa.

15. The system of claim 14 wherein the microphone comprises at least one of a piezoelectric material and a microelectromechanical systems apparatus.

16. The system of claim 13 wherein the housing comprises a conformal material, wherein the conformal material accommodates a variety of pipe sizes while maintaining acoustic contact with the pipe.

17. The system of claim 13 wherein the coupling element comprises a strap, a clamp, an adhesive or a pressure sensitive adhesive.

18. The system of claim 13 wherein the analysis is programmable.

19. The system of claim 18 wherein the programmability includes selecting certain time periods to record and process signals, and certain time periods to adopt a mode of low power dormancy.

20. The system of claim 19 wherein the programmability includes selecting times of high fluid flow and times of negligible fluid flow.

21. The system of claim 18 wherein the programmability is at least partially directed by a central controller.