Facility Monitoring Threshold Setting for Neural Anomaly Detection

The patent applies preliminary action by pre-training a neural network model offline with historical facility data before actual deployment. The model is prepared in advance to perform anomaly detection and remaining useful life prediction, eliminating the need for real-time expert analysis. This allows the system to automatically process sensor data and generate diagnostic results instantly, resolving the contradiction between maintaining high diagnostic accuracy and reducing time consumption.

The patent implements self-service by enabling the facility monitoring system to autonomously perform anomaly detection and remaining useful life prediction using sensor data and a pre-trained neural network model. The system automatically processes data, generates diagnostic results, and provides maintenance recommendations without requiring manual intervention. This maintains diagnostic thoroughness while significantly improving operational efficiency by eliminating time-consuming manual follow-up measures.

The patent applies preliminary action by performing model training offline before deployment. The neural network model is pre-trained using historical facility data to learn patterns of normal and abnormal operations. During actual monitoring, the pre-trained model automatically processes sensor data to detect anomalies and predict remaining useful life, providing advanced predictive capability while maintaining operational simplicity. The complex training process is separated from the deployment phase, allowing the monitoring system to remain simple during operation.