Optical Power Monitoring for Machine-Learning Fault Response
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Solution Overview
Problem
Conventional methods for detecting and remediating faults in optical networks are inefficient, time-consuming, and prone to human error, particularly in complex architectures with diverse components and measurement formats, leading to potential security vulnerabilities and prolonged downtime.
Innovation Solution
A system utilizing machine learning models to standardize and analyze optical network data, identify faults, and automatically generate corrective actions, including interfacing with a retrieval augmented generation endpoint for event resolution data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual inspection methods are used, then operators can physically verify network components, but the process is time-consuming and requires operators to travel across the network
Solution Approach 1:
The patent replaces manual physical inspection with automated optical time domain reflectometry (OTDR) testing and machine learning analysis. The system automatically sends test signals through optical fibers and analyzes returned signals to detect faults, eliminating the need for operators to physically travel to network locations for inspection.
Solution Approach 2:
The system enables self-diagnostic capability by automatically detecting faults, determining their locations, and generating repair recommendations without requiring operator intervention. The machine learning model autonomously analyzes measurement data from multiple components and topologies to identify issues and propose corrective actions.
2Measurement precision
If operators manually review power levels at each component using different standards and units, then they can identify potentially faulty components, but the process is complex and error-prone
Solution Approach 1:
The patent standardizes measurements by converting all optical power levels to a common reference frame (dBm scale) and applying unified thresholds for fault detection. The machine learning model processes standardized parameters from multiple components and topologies consistently, eliminating the complexity of handling different measurement standards and units manually.
Solution Approach 2:
The machine learning model acts as an intermediary that processes raw measurements from diverse components and topologies. It transforms varied measurement data into standardized fault assessments, handling the complexity of multiple standards and units through automated conversion and analysis rather than manual operator intervention.
3Productivity
If legacy fault detection methods are used, then operators can review measurements from different components, but they cannot quickly resolve faults involving multiple components
Solution Approach 1:
The patent segments the network into manageable topological units and analyzes each segment independently using machine learning. By dividing the complex network into discrete components and their relationships, the system can efficiently process and diagnose faults in specific segments without being overwhelmed by the entire network's complexity.
Solution Approach 2:
The machine learning model continuously monitors network measurements and provides feedback when anomalies are detected. It analyzes patterns across multiple components and topologies, generating real-time recommendations for fault resolution. This feedback mechanism enables rapid identification and response to faults involving multiple components through automated decision-making.
Data Source
AI summary
Methods and systems are described herein for monitoring fault events at a fiber optical network. In particular, a system may receive, from components of an optical network, corresponding component data structures comprising optical measurements. The system may extract, from a component data structure, a set of component metrics for light transmission signals being transmitted or received via fiber optic transmission lines at a corresponding component and input the component data structure into a first machine learning model to obtain an indication of an occurrence of an event at one or more components. The system may generate a prompt for input into a second machine learning model configured to identify corrective actions for addressing any events within optical networks to obtain one or more corrective actions for addressing the occurrence of the event.