System Health Diagnostics via Deep Learning

This project predicts a numerical System Health Score from engineering metrics like system complexity, automation usage, and architecture changes. The model leverages deep learning and explainability tools to assess device readiness and reliability.

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🔍 Objective

To detect and predict health degradation risk across hardware systems using structured metrics before validation failures or customer defects occur.

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📦 Setup

pip install -r requirements.txt

Ensure hardware_diagnostics_data.xlsx exists in the root directory under sheet CleanForSVE.

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🚀 Run

python System_Health_Diagnostics_Model.py

The script will:

• Perform leave-one-device-out training and prediction
• Save predictions to: System_Health_Diagnostics_Predictions.xlsx
• Display SHAP explanation plots

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🧠 Model Overview – System Health Diagnostics

This model predicts a numeric System Health Score using device-level metrics such as architectural flags, complexity levels, and logic changes. It’s suited for structured AI use cases in hardware, systems QA, or product validation.

🧱 Architecture

Layer Type	Configuration
LSTM	64 units, return_sequences=True
Flatten	Converts sequence output to dense input
Dropout	Dropout(0.2) to prevent overfitting
Dense (hidden)	Dense(64, activation='relu')
Dense (output)	Dense(1) for regression output

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⚙️ Training Configuration

Component	Value
Optimizer	Adam (default)
Loss Functions	mae, mse, categorical_crossentropy
Framework	TensorFlow 2.x + Keras
Epochs	30
Validation	Leave-One-Device-Out Cross Validation

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🔍 Explainability & Visualization

Tool	Description
SHAP	Used to explain model predictions and rank feature importance
matplotlib / seaborn	Plots actual vs predicted values, feature relationships

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📊 Outputs

• System_Health_Diagnostics_Predictions.xlsx with per-device predictions and error
• SHAP visual summaries per device

This model is fully explainable, production-conscious, and ready for expansion into LLM and vector retrieval pipelines in Phase 2.

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📁 Project Structure (Phase 2 next)

SystemHealthDiagnostics/
├── src/
│   ├── model_builder.py
│   ├── predictor.py
│   ├── explainer.py
│   ├── data_loader.py
│   └── utils/
├── experiments/
│   ├── configs/
│   └── runs/
├── notebooks/
├── reports/
│   └── shap_visuals/
├── outputs/
│   └── System_Health_Diagnostics_Predictions.xlsx
├── README.md
├── requirements.txt
└── roadmap.md   ← This doc