Distributed Acoustic Sensing for Utility Pole Work Conflicts
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Solution Overview
Problem
Utility pole maintenance scheduling conflicts arise due to lack of real-time notification among different utility companies, leading to significant delays as multiple parties are not informed of each other's scheduled work, causing inefficiencies.
Innovation Solution
A distributed acoustic sensing (DAS) system utilizing a vibration sensing system and a trained neural network to detect and classify vibrational signals from optical fibers on utility poles, identifying the location and type of field work, and communicating this information to relevant parties through a graphical user interface, thereby preventing scheduling conflicts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional scheduling notification methods are used among utility companies, then communication between parties is simple and direct, but real-time information sharing is insufficient leading to scheduling conflicts and work delays
Solution Approach 1:
The system implements real-time feedback by continuously monitoring utility pole conditions through optical sensors and immediately notifying relevant utility companies of detected work activities. This closed-loop feedback mechanism ensures that all parties receive timely information about ongoing work, enabling them to adjust their schedules and avoid conflicts, thereby reducing information loss and work delays
Solution Approach 2:
The system performs preliminary detection and classification of work activities before scheduling conflicts occur. By proactively identifying ongoing work through vibration analysis and acoustic sensing, the system enables utility companies to plan their field work in advance, preventing scheduling conflicts before they happen and reducing both information loss and time loss
2Ease of operation
If multiple utility companies perform field work on the same utility poles without coordination, then each company can independently schedule and execute their maintenance tasks, but scheduling conflicts arise causing significant delays
Solution Approach 1:
The system introduces an intermediary platform that acts as a neutral mediator between multiple utility companies. This platform collects real-time data from optical sensors on utility poles, classifies work activities using machine learning models, and distributes information to all relevant parties. This intermediary mechanism allows companies to maintain independent scheduling while avoiding conflicts, preserving ease of operation while improving overall productivity
Solution Approach 2:
The system creates a universal notification platform that serves multiple utility companies simultaneously. The optical sensing system and machine learning classification engine are designed to handle diverse work types from different companies (telecommunications, electricity, cable TV) through a single integrated system, enabling universal information sharing that improves productivity without compromising the ease of operation for any individual company
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively reduces work conflicts by providing real-time information on field work activities, allowing utilities to reschedule maintenance and improve operational efficiency by avoiding concurrent work on the same poles.
Implementation Method 1
Optical fiber can include a core and a cladding layer having different refractive indexes to provide for internal reflection
Data Source
AI summary
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.