Knowledge Graph Fault Identification for Wireless Network Components

The system performs preliminary actions by training machine learning models with historical and synthetic fault data before actual faults occur. Knowledge graphs are constructed in advance to represent component dependencies and data lineage. When faults occur, the pre-trained models and pre-built knowledge graphs enable rapid fault identification without requiring complex real-time analysis infrastructure.

The patent introduces intermediate components including machine learning models that act as mediators between raw network data and fault identification. Knowledge graphs serve as intermediaries to represent and reason about component relationships. Simulators act as intermediaries to generate synthetic training data. These intermediaries bridge the gap between simple monitoring and complex fault analysis.

The system implements self-service through automated machine learning models that independently analyze network data and identify faults without requiring manual intervention. The knowledge graph automatically reasons about component relationships and data lineage. The simulator autonomously generates synthetic training data. These self-service capabilities eliminate the need for complex manual monitoring operations while maintaining high detection accuracy.

The patent replaces manual monitoring and analysis operations with automated machine learning systems. Instead of operators manually analyzing network data and tracing component relationships, ML models automatically perform these tasks. The knowledge graph replaces manual reasoning about system dependencies with automated graph-based inference, significantly improving ease of operation.

The knowledge graph serves multiple functions simultaneously: it stores component dependency relationships, tracks data lineage, represents contextual information about network operations, and provides a reasoning framework for fault analysis. This multi-functionality ensures comprehensive information retention while improving fault identification reliability through various analytical perspectives.

The system performs preliminary actions by constructing knowledge graphs that capture complete data lineage and component relationships before faults occur. Historical data is enriched with contextual information from the knowledge graph in advance. When faults occur, this pre-prepared contextual information is immediately available, preventing information loss that would otherwise occur with raw historical data alone.

The system performs preliminary fault prevention actions by training machine learning models with synthetic data generated from knowledge graphs before actual faults occur. The simulator pre-generates diverse fault scenarios and trains models in advance. This preliminary training enables the deployed models to make accurate predictions with lower computational energy during actual network operations, as the heavy lifting of pattern learning has already been completed.

The patent uses copying by creating synthetic copies of fault scenarios through the simulator. Instead of requiring extensive real-world fault data for training, the system generates synthetic copies of various fault conditions. This reduces the need for energy-intensive collection and processing of real fault data while maintaining model training effectiveness, thereby improving the reliability-energy tradeoff.