OTDR Fault Location with Database Error Correction
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Solution Overview
Problem
Current methods for locating faults in optical fiber networks using OTDR measurements are inefficient when GIS or database data contains errors, leading to laborious and costly maintenance processes due to inaccuracies in identifying the source of signal losses.
Innovation Solution
A method that accesses OTDR measurement data and database data to perform initial matching and iterative optimization, correcting segment lengths and identifying the most likely position of signal loss events by minimizing gaps, even with erroneous data entry, using an automated algorithm to accurately locate faults in optical fiber networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual matching of OTDR events with junctions is performed using existing GIS data, then fault location can be identified, but the process becomes laborious and costly when GIS data contains errors
Solution Approach 1:
The system performs self-correction by automatically detecting inconsistencies between OTDR measurements and GIS data, then adjusting the GIS database to resolve mismatches without requiring manual intervention. The algorithm identifies when measured segment lengths differ from recorded values and autonomously corrects the database, eliminating the need for laborious manual verification and reducing maintenance time while maintaining accurate fault location.
Solution Approach 2:
The system uses OTDR measurement results as feedback to verify and correct GIS data accuracy. By comparing measured segment lengths with recorded values and using the discrepancies to refine the database, the system creates a closed-loop verification process that improves both the speed and accuracy of fault location, transforming static GIS data into a dynamically validated reference.
2Productivity
If automated algorithms are used to match OTDR events with junctions, then processing speed increases, but accuracy decreases when significant database errors exist
Solution Approach 1:
The automated algorithm performs self-correction by detecting inconsistencies between measured and recorded segment lengths, then autonomously adjusting the GIS database to resolve mismatches. This self-service capability allows the system to maintain high processing speed while automatically correcting for database errors, eliminating the need for manual intervention and preserving both productivity and accuracy simultaneously.
Solution Approach 2:
The system dynamically adjusts database parameters (segment lengths, junction positions) based on OTDR measurement feedback. When discrepancies are detected, the algorithm modifies the recorded values to match actual measurements, transforming static erroneous data into corrected parameters that maintain both automated processing speed and accurate event position identification.
3Reliability
If comprehensive manual verification of each segment length is performed, then database accuracy improves, but the complexity and cost of the maintenance process increases
Solution Approach 1:
The system achieves database accuracy through self-service automation rather than manual verification. The algorithm automatically compares OTDR measurements with GIS data, identifies discrepancies, and corrects the database autonomously. This eliminates the need for complex manual verification procedures while maintaining high reliability, reducing both process complexity and operational cost.
Solution Approach 2:
The patent replaces manual mechanical verification processes with automated optical measurement and algorithmic correction. By substituting human operators with OTDR-based automated measurement and intelligent algorithms, the system achieves comparable or superior database accuracy while dramatically reducing process complexity and eliminating the need for labor-intensive segment-by-segment verification.
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 method efficiently corrects database errors and identifies fault locations in optical fiber networks, reducing the time and cost of maintenance by accurately pinpointing signal loss events, even with significant data entry discrepancies, and displaying the results on a map for maintenance teams.
Implementation Method 1
measuring reflected light and backward-scattered light over the length of the OF generated within the OF by an optical pulse
Implementation Method 2
measuring reflected light and backward-scattered light over the length of the OF generated within the OF by an optical pulse
Data Source
AI summary
An optical fiber cable monitoring method and an optical fiber cable monitoring system are able to link information obtained from a measurement result with information stored in a database containing an erroneous entry while still identifying a signal loss event location in an optical fiber cable. The optical fiber cable monitoring method can use an automated algorithm to identify the network element corresponding to the loss event of the optical measurement (OTDR). Thus, an operator can recognize an actual location of a fault that is linked to a location of a point of abnormality on the optical fiber cable when an abnormality in the network is detected.