This image illustrates the end-to-end process of a digital twin system used for real-time anomaly detection and early warning. The flow is divided into three sections: Input, Processing, and Output. It begins with sensor data being collected and stored in databases, then processed using applied machine learning algorithms. The system detects anomalies and displays alerts on a digital dashboard, ultimately sending early warnings to operators. Supporting this process is a detailed breakdown of steps including data decoding, filtering, pattern recognition, and forecasting. The image underscores how digital twin technology leverages AI and sensor data to ensure operational safety and efficiency.