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| title: Anomaly Agent | |
| emoji: 📡 | |
| colorFrom: indigo | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.9.1 | |
| python_version: "3.11" | |
| app_file: app.py | |
| pinned: true | |
| license: mit | |
| short_description: Real-Time Turbine Monitoring | |
| tags: | |
| - anomaly-detection | |
| - industrial-ai | |
| - scada | |
| - turbine | |
| - iot | |
| # Anomaly Agent | |
| **Real-Time Turbine Monitoring for SCADA Systems** | |
| Detect anomalies in gas turbine sensor streams with automated root cause analysis. | |
| ## Quick Start | |
| 1. Click **"Demo: Bearing Degradation"** to see anomaly detection in action | |
| 2. Click **"Demo: Normal Operations"** to see healthy equipment baseline | |
| 3. Or upload your own CSV telemetry data | |
| ## How It Works | |
| ``` | |
| SCADA Sensors -> Feature Scaling -> Isolation Forest -> Root Cause Analysis -> Alert | |
| ``` | |
| ## Resources | |
| - **Model**: [turbine-anomaly-detector](https://huggingface.co/davidfertube/turbine-anomaly-detector) | |
| - **Dataset**: [turbine-sensor-streams](https://huggingface.co/datasets/davidfertube/turbine-sensor-streams) | |
| - **GitHub**: [anomaly-agent](https://github.com/davidfertube/anomaly-agent) | |
| - **Portfolio**: [davidfernandez.dev](https://davidfernandez.dev) | |
| ## Author | |
| **David Fernandez** - Industrial AI Engineer | LangGraph Contributor | |
| - [LinkedIn](https://linkedin.com/in/davidfertube) | |
| - [GitHub](https://github.com/davidfertube) | |
| - [HuggingFace](https://huggingface.co/davidfertube) | |