Fiber Optic Cable Route Monitoring for Buried Cable Detection
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Solution Overview
Problem
Telecommunications service providers lack an efficient and reliable method to determine the location of buried cables, necessitating time-consuming and expensive manual inspections.
Innovation Solution
A distributed fiber optic sensing system integrated with AI/ML methodologies for real-time monitoring of optical fiber cable routes, automatically distinguishing buried and aerial cables without manual intervention or pre-training, and being insensitive to environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-person inspection methods are used to determine cable locations, then accuracy of determination is improved, but time consumption and operational costs increase
Solution Approach 1:
The patent replaces manual in-person inspection with an automated optical sensing system that uses light propagation through the fiber optic cable to detect environmental changes. The system substitutes mechanical field inspection with optical sensing and AI-based automated determination, achieving both high accuracy and rapid results without requiring service personnel to physically travel to cable locations.
Solution Approach 2:
The fiber optic cable itself serves as the sensing element, detecting environmental changes along its length without requiring external sensors or manual inspection. The cable performs self-monitoring by detecting changes in its own environment (temperature, vibration, acoustic signals) and transmitting this information back to the monitoring system for automated analysis.
2Reliability
If in-person inspection methods are used to determine cable locations, then reliability of determination is improved, but operational costs increase
Solution Approach 1:
The system replaces costly manual inspection operations with an automated optical sensing platform that continuously monitors cable routes. The AI-based analysis engine processes sensor data to reliably determine cable locations and status, eliminating the need for expensive field personnel deployment while maintaining high determination reliability through multiple sensing modalities and sophisticated algorithms.
Solution Approach 2:
The monitoring system operates continuously, providing ongoing surveillance of cable routes rather than periodic manual inspections. This continuous monitoring capability ensures reliable cable location determination and status assessment at any time, improving operational responsiveness and reducing costs by eliminating repeated dispatches of service personnel.
3Device complexity
If traditional monitoring methods are used, then system complexity is reduced, but automation level decreases
Solution Approach 1:
The fiber optic cable serves multiple functions simultaneously: it acts as both the telecommunications transmission medium and the sensing element for environmental monitoring. This multi-functionality reduces overall system complexity by eliminating the need for separate sensing infrastructure while enabling high-level automation through integrated data collection and AI-based analysis of cable location and status.
4Measurement precision
If manual data collection and classifier training are required for each route, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary sensing and data collection automatically during cable installation and initial operation, building training datasets without requiring manual field data collection. The AI classifiers are pre-trained on diverse cable route data, enabling rapid deployment to new routes with immediate accurate detection capability, eliminating the need for time-consuming manual data gathering and training for each new installation.
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
Enables rapid and autonomous identification of buried cable sections, reducing downtime and operational costs by providing instant and accurate cable location determination.
Implementation Method 1
distributed fiber optic sensing (DFOS) systems, methods, structures and machine learning (ML) technologies
Data Source
AI summary
A distributed fiber optic sensing (DFOS) system and method employing a fiber optic sensor cable that autonomously collects DFOS data and employs artificial intelligence/machine learning (AI/ML) to distinguish sections of the fiber optic sensor cable that are above ground (aerial), below ground (buried), and buried but occasionally above ground, in addition to any change(s) that occur with respect to the fiber optic sensor cable at such sections.