Utility Pole Wire Anomaly Localization With Fiber Sensing AI
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Solution Overview
Problem
Existing distributed fiber optic sensing systems lack the capability to efficiently and accurately identify and report dynamic anomalies on utility pole wires/cables in real-time, particularly in distinguishing between power and telecommunications wires, which is critical for quick response and minimizing service interruptions.
Innovation Solution
Combining distributed fiber optic sensing with a machine learning model using linear model trees to analyze real-time data from existing telecommunication fiber optic cables, enabling the identification of dynamic events and precise localization of affected wires/cables by determining their type and GPS coordinates, and automatically reporting to responsible authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed fiber optic sensing is used to monitor utility pole wires, then real-time anomaly detection capability is improved, but the ability to accurately identify and distinguish wire types (power vs. telecommunications) deteriorates
Solution Approach 1:
The patent introduces an intermediary classification system that acts as a mediator between the fiber optic sensing data and the anomaly detection output. This intermediary layer uses machine learning models (Random Forest, SVM, Neural Networks) trained on wire type characteristics to classify detected anomalies into specific wire types, thereby resolving the contradiction by adding a specialized identification layer without compromising the original detection capability
Solution Approach 2:
The patent transforms the monitoring approach by changing from generic anomaly detection to type-specific detection. It does this by training multiple classification models with different parameters optimized for different wire types (power lines, telecom cables, etc.), allowing the system to maintain high reliability in detection while achieving precision in wire type identification through parameter-specific analysis
2Adaptability or versatility
If multiple wire types on utility poles are monitored, then comprehensive coverage is improved, but the complexity of identifying which specific wire is affected worsens
Solution Approach 1:
The patent segments the monitoring task by creating separate classification models for different wire types (power lines, telecommunications cables, etc.). Each model is specialized in identifying specific wire types, which simplifies the overall identification process. When an anomaly occurs, the system uses the appropriate segmented model based on the detected wire type, reducing complexity compared to a single monolithic identification system
Solution Approach 2:
The patent implements a dynamic classification system that adapts its identification approach based on the detected anomaly characteristics. The system dynamically selects which classification model to use and adjusts its analysis depth based on the wire type and anomaly severity, making the complex multi-wire monitoring system more manageable and efficient in practice
3Speed
If real-time data collection from fiber optic cables is implemented, then response speed is improved, but the difficulty of processing and analyzing the data worsens
Solution Approach 1:
The patent applies preliminary action by pre-training multiple classification models (Random Forest, SVM, Neural Networks) with extensive wire type data before deployment. These pre-trained models have learned wire type characteristics and patterns in advance, so when real-time data arrives, the system can quickly classify anomalies without needing to perform complex analysis from scratch, thus maintaining fast response speed while managing data processing difficulty
Solution Approach 2:
The patent creates simplified copies or representations of complex wire type data through feature extraction and dimensionality reduction. Instead of processing all raw fiber optic data in real-time, the system extracts key features and creates compressed representations that retain essential wire type information, making real-time processing feasible while maintaining accuracy
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
This solution allows for accurate and timely identification of affected wires/cables, achieving 97% training and 95.3% testing accuracy in wire type prediction, facilitating rapid response and minimizing service disruptions by pinpointing the exact geographic location of anomalies.
Implementation Method 1
distributed fiber optic sensing utilizes existing telecommunication fiber optic cables as a distributed sensor to capture the dynamic response of the wires
Data Source
AI summary
Systems and methods for performing the dynamic anomaly localization of utility pole aerial/suspended/supported wires/cables by distributed fiber optic sensing. In sharp contrast to the prior art, our inventive systems and methods according to aspects of the present disclosure advantageously identify a “location region” on a utility pole supporting an affected wire/cable, thereby permitting the identification and reporting of service personnel that are uniquely responsible for responding to such anomalous condition(s).