DFOS DAS Wood Pole Decay Detection ML
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Solution Overview
Problem
Current methods for inspecting wooden utility poles, such as visual, sound-based, and bore-based inspections, are either subjective, intrusive, or time-consuming, failing to effectively detect internal decay and requiring frequent monitoring to prevent service disruptions and costly repairs.
Innovation Solution
A distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) system combined with machine learning, where audio signals from hammer impacts on wooden utility poles are used to train models for real-time condition evaluation, enabling automatic and cost-effective monitoring of pole conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual, sound-based, or bore-based inspection methods are used, then inspection can be performed, but the methods are subjective, intrusive, or time-consuming and fail to effectively detect internal decay
Solution Approach 1:
The patent replaces traditional mechanical inspection methods (visual inspection, sound-based inspection, bore-based inspection) with an optical sensing system. A fiber optic sensor is embedded within the utility pole to continuously monitor internal conditions, substituting manual inspection with automated optical detection that can identify internal decay through changes in light propagation characteristics.
Solution Approach 2:
The patent introduces an intermediary optical fiber sensor system that acts as a mediator between the utility pole structure and the monitoring system. The fiber optic sensor detects internal changes in the pole and transmits this information to a remote monitoring location, eliminating the need for direct physical inspection while providing continuous data about the pole's internal condition.
2Reliability
If frequent monitoring is performed to prevent service disruptions and costly repairs, then reliability improves, but cost and time consumption increase
Solution Approach 1:
The patent implements continuous monitoring of utility pole conditions through an embedded fiber optic sensor system. The sensor continuously detects internal changes in real-time, providing ongoing information about the pole's structural health without requiring repeated manual inspections, thereby maintaining service reliability while reducing overall monitoring costs.
Solution Approach 2:
The patent enables early detection of internal decay through continuous monitoring, allowing utility companies to take preliminary actions before critical failures occur. By detecting degradation trends early, maintenance can be scheduled proactively, preventing service disruptions and avoiding costly emergency repairs or complete pole replacements.
3Ease of operation
If manual inspection methods are used, then inspection can be performed, but they require subjective evaluation and are time-consuming
Solution Approach 1:
The patent replaces subjective manual evaluation with automated optical sensing. The fiber optic sensor objectively measures physical changes in the utility pole's internal structure through light propagation characteristics, eliminating human subjectivity and providing consistent, quantifiable data about the pole's condition.
Solution Approach 2:
The utility pole essentially monitors itself through the embedded fiber optic sensor that detects internal changes autonomously. The system requires no human intervention for data collection or initial analysis, as the sensor continuously self-monitors the pole's structural integrity and transmits data for automated analysis.
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 provides efficient and accurate real-time evaluation of wooden utility pole conditions, reducing the need for intrusive methods and minimizing damage, while enabling continuous and automatic monitoring, thus preventing further damage and reducing maintenance costs.
Implementation Method 1
audio signals are obtained using DFOS/DAS when a service technician/inspector strikes the wooden utility poles with an impact tool such as a hammer
Data Source
AI summary
Aspects of the present disclosure describe distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) systems, methods, and structures that employ machine learning and provide for the automatic remote inspection and condition evaluation of wooden utility poles. Operationally, audio (acoustic) signals are obtained using DFOS/DAS when a service technician/inspector strikes the wooden utility poles with an impact tool such as a hammer. Historical audio DFOS/DAS signals that include signals resulting from hollow (decayed) utility poles and solid (good) poles are used to train one or more machine learning models and the trained machine learning models are subsequently used to evaluate real-time impact data collected from DFOS/DAS and determine utility pole condition in real-time.