Microwave Link Data Classification for Fault Detection
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Solution Overview
Problem
There is a need for improved identification of operating conditions in point-to-point wireless microwave links to reduce unnecessary maintenance and resource allocation, as current methods often misclassify conditions and lead to unnecessary site visits and equipment replacement.
Innovation Solution
A controller entity that classifies microwave link data by obtaining performance affecting values and estimating probability values using subsets of performance affecting properties, employing models such as convolutional neural networks to accurately identify operating conditions and reduce misclassification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional alarm-based monitoring is used to detect link disturbances, then alarms are sent to network operators, but this leads to unnecessary site visits and equipment replacement since a significant fraction of equipment does not actually suffer from impaired operation
Solution Approach 1:
The patent segments the classification process by dividing operating conditions into distinct categories (weather-related, equipment-related, other) and using separate classification models for each segment. This allows more accurate identification of the specific cause of performance degradation, reducing false alarms and unnecessary site visits.
Solution Approach 2:
The patent adds a new dimension to the classification process by estimating probability values for multiple operating conditions simultaneously across different categories. This multi-dimensional probability estimation enables more accurate differentiation between weather-related and equipment-related issues, reducing unnecessary maintenance actions.
2Measurement precision
If network operators perform site visits to inspect link equipment upon receiving alarms, then they can identify actual equipment faults, but this consumes significant resources including time and money
Solution Approach 1:
The classification system performs self-service by automatically analyzing microwave link data and identifying operating conditions without requiring human intervention for initial assessment. The system autonomously estimates probability values for different operating conditions and can trigger appropriate maintenance actions only when truly necessary, reducing resource consumption.
Solution Approach 2:
The patent changes parameters by using multiple performance affecting values (such as attenuation, received power, signal quality metrics) and transforming them into probability estimates for different operating conditions. This parameter transformation enables more precise identification of actual equipment faults versus weather-related issues, reducing unnecessary maintenance resources.
3Measurement precision
If classification models use all available performance affecting properties for each operating condition, then comprehensive analysis is performed, but this increases computational complexity and reduces classification accuracy due to irrelevant data
Solution Approach 1:
The patent extracts only the relevant performance affecting properties for each specific operating condition category. Instead of using all available data for every classification, the system identifies and extracts the subset of properties most relevant to each condition type (e.g., weather-related properties for weather conditions, signal properties for equipment conditions), reducing computational complexity while maintaining or improving accuracy.
Solution Approach 2:
The classification model applies local quality by using different sets of performance affecting properties for different operating condition categories. Each category is analyzed with the specific properties most relevant to that condition type, rather than applying a uniform comprehensive analysis to all conditions. This localized approach improves classification accuracy while reducing unnecessary computational complexity.
Data Source
AI summary
There is provided mechanisms for classifying microwave link data of a microwave system comprises a point-to-point wireless microwave link. The method is performed by a controller entity. The method comprises obtaining microwave link data in terms of a set of performance affecting values for the point-to-point wireless microwave link. Each performance affecting value in the set pertains to a respective performance affecting property of the point-to-point wireless microwave link. The method comprises classifying the microwave link data to operating conditions in a set of operating conditions, where each operating condition is associated with its own subset of the performance affecting properties, by for each operating condition, estimating a probability value using the performance affecting values of the subset of the performance affecting properties associated with that operating condition.