Anomaly Event Detection for Seasonal and Drifting Equipment Data

The patent applies dynamics by transitioning from static operating state analysis to dynamic behavior prediction. The machine learning model learns temporal patterns and evolves predictions over time, enabling the system to adapt to slow progressive changes, seasonal trends, and high oscillations that static methods cannot capture. This dynamic approach allows the system to handle diverse operating conditions while maintaining reliable anomaly detection.

The patent employs parameter changes by using machine learning models that can adapt to varying data characteristics. The system adjusts its prediction parameters based on learned patterns from historical data, enabling it to handle different types of anomalies including unknown anomalous states. This flexibility in parameter adaptation resolves the contradiction between versatility and reliability.

The patent applies preliminary action by pre-training machine learning models on historic operating data before deployment. This offline training phase prepares the model to make accurate predictions during operation, reducing real-time computational requirements. The system performs the complex learning task in advance, allowing for high detection accuracy without excessive ongoing computational complexity.

The patent uses copying by creating a virtual model of the component's normal behavior through machine learning. This digital twin or predictive model replicates expected component behavior, allowing the system to compare actual behavior against the copied model without requiring complex real-time analysis of all possible operating conditions. This approach improves detection accuracy while managing computational complexity.