Fiber optic sensing for reduced field work conflicts
Systems and methods for reducing work conflicts is provided. The method includes receiving a vibrational signal from a utility pole; identifying a location and type of field work on the utility pole from one or more features of the vibrational signal utilizing a trained neural network; and communicating the location and type of field work to a third party.
1. A method for reducing work conflicts, comprising:
receiving a vibrational signal from a utility pole;
identifying a location and type of field work on the utility pole from one or more features of the vibrational signal utilizing a trained neural network; and
communicating the location and type of field work to a third party.
2. The method as recited in claim 1 , wherein the vibrational signal includes optical phase shifts of backscattered light relative to a local optical oscillator.
3. The method as recited in claim 2 , wherein the vibrational signal is received along a fiber optic tap connected to and in optical communication with at least one of one or more optical fibers making up a fiber optic cable.
4. The method as recited in claim 3 , wherein the trained neural network utilizes a Random Forest Classification model to identify the location and the type of field work.
5. The method as recited in claim 4 , wherein the Random Forest Classification model outputs a working zone value that identifies a work zone region on a utility pole.
6. The method as recited in claim 5 , further comprising the third party altering a work schedule based on the received location and type of field work being conducted on the utility pole.
7. The method as recited in claim 5 , wherein the identified work zone region is a lower support region, telecommunications region, or electrical power distribution region.
8. A system for reducing work conflicts, comprising:
a distributed acoustic sensing (DAS) system, including a vibration sensing system configured to detect and measure dynamic strain changes along an optical fiber received as a vibrational signal from a utility pole, a trained neural network configured to identify a location and type of field work from one or more features of the vibrational signal, and a graphical user interface (GUI) configured to display the dynamic strain changes to a user as a vibrational pattern and communicate the location and type of field work to a third party.
9. The system as recited in claim 8 , wherein the vibrational signal includes optical phase shifts of backscattered light relative to a local optical oscillator.
10. The system as recited in claim 9 , wherein the vibrational signal is received along a fiber optic tap connected to and in optical communication with at least one of one or more optical fibers making up a fiber optic cable.
11. The system as recited in claim 10 , wherein the trained neural network utilizes a Random Forest Classification model to identify a location and a type of field work being done on at least one of a plurality of utility poles.
12. The system as recited in claim 11 , wherein the Random Forest Classification model outputs a working zone value that identifies a work zone region on the at least one of the plurality of utility poles.
13. The system as recited in claim 12 , wherein the GUI interface provides the location and type of field work to the third party in real time.
14. The method as recited in claim 13 , wherein the identified work zone region is a lower support region, telecommunications region, or electrical power distribution region.
15. A non-transitory computer readable storage medium comprising a computer readable program for reducing work conflicts, wherein the computer readable program when executed on a computer causes the computer to perform the steps of:
identifying a location and type of field work on at least one of a plurality of utility poles from one or more features of a received vibrational signal utilizing a trained neural network; and
communicating the location and type of field work to a third party.
16. The computer readable program as recited in claim 15 , wherein the received vibrational signal includes optical phase shifts of backscattered light relative to a local optical oscillator.
17. The computer readable program as recited in claim 16 , wherein the vibrational signal is received along a fiber optic tap connected to and in optical communication with at least one of one or more optical fibers making up a fiber optic cable.
18. The computer readable program as recited in claim 17 , wherein the trained neural network utilizes a Random Forest Classification model to identify the location and the type of field work.
19. The computer readable program as recited in claim 18 , wherein the Random Forest Classification model outputs a working zone value that identifies a work zone region on a utility pole.
20. The computer readable program as recited in claim 19 , wherein the identified work zone region is a lower support region, telecommunications region, or electrical power distribution region of the utility pole.