Base-Station Alarm Correlation for Early Failure Prediction
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Solution Overview
Problem
Current base-station failure prediction methods are inadequate as they fail to effectively analyze correlated diagnostic indicators, leading to delayed maintenance and increased costs, as they primarily focus on isolated analysis of individual indicators rather than their interrelated nature.
Innovation Solution
A base-station failure predictor system that includes an interface to sub-units, a diagnostic indicator memory, and an evaluator to analyze the frequency and type of diagnostic indicators, using weighted combinations to assess the likelihood of failure and provide early warnings, allowing for more accurate and timely maintenance planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If isolated analysis of individual diagnostic indicators is used, then the system complexity is low, but the failure prediction accuracy is insufficient
Solution Approach 1:
The patent combines multiple diagnostic indicators into a unified analysis framework. The failure prediction system integrates correlations between different diagnostic indicators (such as temperature, voltage, current, and error logs) to assess overall system health, rather than analyzing each indicator independently. This merging approach improves prediction accuracy by capturing interrelationships that single-indicator analysis misses.
Solution Approach 2:
The diagnostic indicator evaluator is designed to handle multiple types of diagnostic indicators simultaneously through a single unified analysis mechanism. The system can process various indicator types (thermal, electrical, operational) using the same correlation-based evaluation framework, making the system multi-functional without requiring separate analysis paths for each indicator type.
2Reliability
If correlated diagnostic indicators are analyzed together, then the failure prediction accuracy improves, but the data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing correlation relationships between diagnostic indicators and storing them in a database. When failure prediction is needed, the system retrieves pre-defined correlation patterns rather than computing relationships in real-time. This preliminary preparation reduces the complexity of real-time data processing while maintaining high prediction reliability.
Solution Approach 2:
The patent introduces a diagnostic indicator evaluator as an intermediary component that mediates between raw diagnostic data and failure predictions. This evaluator applies predefined correlation rules and algorithms to transform complex multi-indicator data into simplified risk assessments, reducing the processing burden on the overall system while maintaining prediction reliability.
3Loss of time
If frequency analysis of diagnostic indicators is performed, then the timeliness of failure detection improves, but the computational resources required increase
Solution Approach 1:
The system applies partial action by selectively analyzing only those diagnostic indicators that show frequency changes beyond predefined thresholds. Rather than continuously analyzing all indicators at full depth, the system focuses computational resources on indicators exhibiting abnormal frequency patterns, reducing overall energy consumption while maintaining timely detection capability for actual failures.
Data Source
AI summary
A base-station failure predictor comprises an interface to at least one sub-unit of a base-station of a mobile communications network, an alarm memory, and an alarm evaluator. The base-station failure predictor is adapted to receive a plurality of alarms from the at least one sub-unit of the base-station via the interface. The plurality of alarms are stored in the alarm memory, and the alarm evaluator is adapted to analyze the frequency of the plurality of alarms to assess a likelihood of failure of the at least one sub-unit concerned. A base-station failure prediction method is also proposed. Furthermore, a computer program product with instructions for the manufacture and a computer program product enabling a processor to carry out the base-station prediction method are also proposed.