Physics-Informed ML for Jet Engine R&D & Anomaly Detection
Accelerating R&D cycles and enhancing reliability are critical for modern airpower. This challenge tasks developers with creating a cutting-edge system for propulsion system health monitoring and R&D optimization. The solution must integrate physics-based models with advanced machine learning techniques to predict component degradation and detect anomalies in real-time telemetry data. Furthermore, participants will leverage Claude 3.5 Haiku, orchestrated with Marvin, for intelligent interpretation of unstructured maintenance logs and R&D reports. Pinecone will serve as a semantic knowledge base, enabling efficient retrieval of historical data to inform predictive models and accelerate the generation of actionable insights for faster, more effective propulsion system development and maintenance decisions.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a system for jet engine health monitoring and R&D optimization, combining physics-informed machine learning with Claude 3.5 Haiku, Marvin, and Pinecone for advanced anomaly detection, remaining useful life prediction, and semantic insights from operational data.
How work is evaluated
The evaluation will assess the system's accuracy in detecting injected anomalies, predicting remaining useful life (RUL) of components, and its capability to provide relevant semantic insights from historical data for R&D optimization. The system will process synthetic telemetry and maintenance log data.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Master the principles of Physics-Informed Neural Networks (PINNs) or similar techniques to build models that respect known thermodynamic and mechanical constraints of jet engines for more robust degradation prediction.
Implement advanced time-series anomaly detection algorithms (e.g., LSTM autoencoders, Isolation Forests, or transformer-based models) on synthetic or real (provided) propulsion telemetry data.
Design and Build an integration layer using Marvin to connect Claude 3.5 Haiku with unstructured text data from maintenance logs, enabling extraction of structured insights (e.g., failure modes, component wear patterns, environmental factors).
Orchestrate a data pipeline to convert historical R&D reports, test specifications, and incident logs into vector embeddings suitable for storage and efficient semantic search in Pinecone.
Develop a Remaining Useful Life (RUL) prediction model for critical engine components (e.g., turbine blades, bearings) leveraging both physics-informed ML outputs and historical failure data.
Integrate the anomaly detection and RUL prediction systems into a dashboard or alerting mechanism that provides real-time health assessments of a simulated engine fleet.
Optimize the system's ability to identify correlations between detected anomalies, predicted degradation, and past R&D parameters stored in Pinecone, to accelerate future R&D cycles.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the system's accuracy in detecting injected anomalies, predicting remaining useful life (RUL) of components, and its capability to provide relevant semantic insights from historical data for...
Challenge input
{ 'telemetry_data': [{'timestamp': T1, 'sensor_1': V1, ..., 'sensor_N': VN}], 'historical_rul_data': [{'component_id': C1, 'failure_time': FT1, 'te...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ 'anomaly_alerts': [{'timestamp': T_a, 'component': 'C', 'severity': 'high', 'explanation': 'ML-generated insight'}], 'rul_predictions': [{'compon...
- Ensures the anomaly detection system processes telemetry without crashes.
- Verifies successful connection and query execution with Pinecone.
- Anomaly Detection F1 Score target: 0.85
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Ensures the anomaly detection system processes telemetry without crashes.
- Verifies successful connection and query execution with Pinecone.
Metrics
- Anomaly Detection F1-Score · Target 0.85
- RUL Prediction RMSE (Hours) · Target 50
- Semantic Search Relevance (MRR) · Target 0.7
Workflow tasks
- Task 1
Input: { 'telemetry_data': [{'timestamp': T1, 'sensor_1': V1, ..., 'sensor_N': VN}], 'historical_rul_data': [{'component_id': C1, 'failure_time': FT1, 'telemetry_snapshot': [...]}], 'engine_physics_model': {'params': {...}} }
Output: { 'anomaly_alerts': [{'timestamp': T_a, 'component': 'C', 'severity': 'high', 'explanation': 'ML-generated insight'}], 'rul_predictions': [{'component': 'C', 'predicted_rul_hours': H}] }
Execution harness: Python
- Task 2
Input: { 'query': 'Explain sudden drop in thrust efficiency and similar past incidents for CFM56-7B engine', 'maintenance_logs': ['text_log_1', 'text_log_2'], 'rd_reports_embeddings': ['vec_1', 'vec_2'] }
Output: { 'semantic_results': [{'source': 'log_file_X', 'summary': 'Relevant info', 'score': 0.9}], 'claude_explanation': 'Generated comprehensive explanation' }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.