Machine Learning Cable Detection Without Traffic Disruption
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Solution Overview
Problem
Existing network management systems struggle to accurately detect faulty cables, particularly when cables are partially plugged in or degraded, leading to difficulties in distinguishing between cable issues and other network problems, and conventional testing methods disrupt network traffic and require control of both cable ends.
Innovation Solution
A network management system uses machine learning models to analyze performance data from network interfaces, determining the type of cable and applying a specific model to detect potential bad cable issues, allowing for automated detection, traffic rerouting, and diagnostic recommendations without disrupting network traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional cable testing methods are used to detect faulty cables, then detection capability is improved, but network traffic is disrupted and control of both cable ends is required
Solution Approach 1:
The patent introduces machine learning models as an intermediary between network performance data and cable fault detection. These models analyze multiple network parameters (packet loss, error rates, signal quality) to infer cable faults without requiring direct cable testing, thus avoiding traffic disruption while maintaining detection accuracy
Solution Approach 2:
The patent replaces mechanical cable testing methods (which require physical access to both cable ends and interrupt traffic) with a software-based machine learning system that analyzes network performance data to detect cable faults, eliminating the need for physical intervention and traffic disruption
2Measurement precision
If machine learning models are used to detect faulty cables, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the cable detection problem by creating separate machine learning models for different cable types (copper, fiber optic, coaxial). Each model is trained on specific characteristics of its cable type, improving accuracy while allowing the system to handle complexity through modular, type-specific models rather than a single complex universal model
Solution Approach 2:
The patent changes the parameters used for cable detection from simple binary fault/non-fault indicators to multiple continuous parameters including packet loss rate, error rate, signal quality metrics, and cable characteristics. This enables more nuanced detection accuracy while the machine learning models automatically manage the complexity of processing these multiple parameters
3Measurement precision
If multiple machine learning models are used for different cable types, then detection accuracy is improved, but model selection complexity increases
Solution Approach 1:
The patent performs preliminary classification of cable types before applying specific detection models. The system first identifies the cable type (copper, fiber optic, coaxial) and then selects the appropriate pre-trained machine learning model for that specific type, streamlining the process and reducing selection complexity through a clear two-stage approach
Solution Approach 2:
The patent introduces a cable type classification mechanism as an intermediary layer between the input data and the multiple specialized machine learning models. This intermediary automatically routes data to the appropriate model based on detected cable characteristics, eliminating the need for manual model selection and reducing complexity while maintaining high accuracy across different cable types
Data Source
AI summary
Example systems, devices, and techniques are described for inferring a potential bad cable issue associated with a cable. An example system includes processing circuitry configured to determine a class of a cable associated with one of a plurality of network interfaces. The processing circuitry is configured to select, based on the class of the cable, a first machine learning model of a plurality of machine learning models. The processing circuitry is configured to determine, based on the class of the cable and the one of the plurality of network interfaces, a first feature set of the performance data. The processing circuitry is configured to execute the first machine learning model to infer, based on the first feature set, a potential bad cable issue associated with the cable and output an indication of the potential bad cable issue associated with the cable.