Edge Sensor Monitoring for Predictive Power Equipment Maintenance

The patent applies universality by creating a single integrated monitoring system that can diagnose multiple different power system components (generators, transformers, motors, etc.) using one platform. The system uses universal sensors and algorithms that adapt to different equipment types, eliminating the need for separate custom monitoring systems for each component while maintaining high diagnostic capability.

The patent employs copying by using sensor data and signal patterns from one equipment type to create diagnostic models that can be applied to similar equipment. The system learns from training data and creates reusable diagnostic algorithms that can be copied across different power system components, reducing implementation complexity while preserving diagnostic accuracy.

The patent applies segmentation by dividing the data processing workflow into distinct stages: data collection, training phase, and deployment phase. During training, extensive data is processed to create accurate classification models, but once trained, the models are deployed to edge devices where they operate with minimal data processing requirements. This segmentation allows high classification accuracy to be achieved during training while maintaining scalability during deployment.

The patent implements preliminary action by performing compute-intensive data training and model development beforehand, before deployment to field equipment. The system pre-processes extensive training data and creates finalized classification models that can then be deployed to various power system components without requiring ongoing extensive computational resources, thereby enabling scalability across multiple devices.