Gallifrey: Advanced Structural Health Monitoring (SHM) Framework

Gallifrey is a state-of-the-art Structural Health Monitoring (SHM) and predictive maintenance platform designed for high-stakes infrastructure like bridges. It integrates non-stationary signal processing, deep learning, and robust infrastructure monitoring to provide a "Digital Twin" perspective on structural integrity.

🌟 Vision

To bridge the gap between raw sensor telemetry and actionable structural engineering insights using a Multi-Scale Analysis approach.

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🔬 Core Methodology & Modules

1. Advanced Signal Processing (ml/algos/wavelet_transform)

Traditional FFT analysis often fails on transient or non-stationary vibration data. Gallifrey implements research-grade Wavelet transforms for precise time-frequency localization:

• CWT (Continuous Wavelet Transform): Provides high-resolution scalograms using Morlet basis.
• Wavelet Spectral Density: Estimating Local Power Spectra (LPS) to understand energy distribution over time.
• Structural Response: Solving MDOF (Multi-Degree-of-Freedom) system responses via Duhamel Integral integration.
• Reliability Metrics: Automated calculation of the First Passage Criterion and Up-crossing rates for failure probability.

2. Machine Learning Pipeline (ml/data/)

The system employs a layered approach to detect and predict degradation:

• Phase A: Anomaly Detection:
  • Isolation Forests: Statistical outlier detection on sensor streams.
  • VAE (Variational Autoencoders): Learning a latent "healthy state" representation to identify reconstruction errors in compromised structures.
• Phase B: Forecasting:
  • LSTM (Long Short-Term Memory): Modeling temporal dependencies in strain and vibration data.
  • SHI Prediction: Regressing the Structural Health Index to forecast remaining useful life.
• Phase C: Optimization:
  • Bayesian Optimization (Optuna): Automated hyperparameter tuning for LSTM and RF architectures.

3. Monitoring Infrastructure (infra/)

A enterprise-grade monitoring stack ensures data persistency and observability:

• TimescaleDB: High-performance SQL for time-series, handling millions of sensor metrics with hypertables.
• Prometheus: Internal system monitoring and metric scraping.
• Grafana: Real-time dashboards displaying vibration trends, anomaly scores, and sensor health.

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📁 Detailed Directory Map

gallifrey-ml/
├── infra/                      # Cloud-native Infrastructure
│   ├── docker-compose.yml      # Service orchestration
│   ├── prometheus.yml          # Scraping rules
│   └── grafana/                # (Optional) Dashboard JSON configs
├── ml/                         # Research & Development
│   ├── algos/                  
│   │   └── wavelet_transform/  # Paper-based core implementations
│   ├── data/                   
│   │   ├── figures/            # Visual proof of analysis
│   │   ├── models/             # Compressed weights & serialized estimators
│   │   ├── Analysis.ipynb      # Signal processing playground
│   │   └── Risk_Prediction.ipynb # Deep learning training & evaluation
│   ├── tests/                  # Integration & Unit tests
│   ├── main.py                 # Core CLI entry
│   └── pyproject.toml          # Modern dependency management
└── README.md                   # Primary Documentation

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🛠️ Installation & Execution

1. Python Environment (Powered by uv)

Ensure uv is installed, then run:

cd ml
uv sync

This will automate the installation of PyWavelets, PyTorch, Scikit-Learn, and our optimization dependencies.

2. Infrastructure Deployment

Launch the "Monitoring Digital Twin" environment:

cd infra
docker-compose up -d

Visit localhost:3000 to access the Grafana Dashboard.

3. Verification Suite

Run the scientific validation pipeline:

cd ml
uv run pytest ml/tests/tests_wavelets

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📈 Result Highlights

• Dynamic Reliability: Capable of estimating bridge failure probability under non-stationary excitation (e.g., traffic loads, wind).
• Scalogram Analysis: Visual identification of fatigue-related frequency shifts.
• Anomaly Logic: VAE-based reconstruction helps distinguish between sensor noise and real structural changes.

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Maintained by the Gallifrey Team. Aiming for Resilient Infrastructure through AI.