F&B Process Anomaly Detection System 🎯

A comprehensive Food & Beverage (F&B) batch process anomaly detection system that combines traditional machine learning with deep learning techniques to identify quality issues, equipment malfunctions, and process deviations in food production.

🏢 Overview

This system analyzes production batch data to detect anomalies in critical process parameters such as ingredient quantities, temperatures, mixing speeds, and oven conditions. It employs an ensemble approach with four specialized anomaly detection algorithms and a consensus voting mechanism for robust, reliable detection.

Key Objectives:

• Real-time quality monitoring for food batch production
• Automated anomaly flagging with explainable insights
• Multi-model consensus for production-grade reliability
• Executive-ready reporting for stakeholders

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🔬 Technical Implementation

The core implementation is in F&B_Process_Anomaly_Detection_System.ipynb, which processes the provided dataset.xlsx to detect anomalies across 11 key process parameters.

📊 Data Pipeline

• Loading: Excel file import with pandas
• Cleaning: Drop unnamed/ID columns, NaN handling (mean imputation), numeric feature selection
• Preprocessing: StandardScaler normalization
• Dataset: 1500 batches × 11 features (Time, ingredient quantities, temperatures, speeds, humidity)

🤖 Anomaly Detection Models

Model	Algorithm	Method	Key Parameters	Strengths
Isolation Forest	Tree-based Ensemble	sklearn.ensemble.IsolationForest	contamination=0.1, random_state=42	Fast, general-purpose, handles high dimensions
One-Class SVM	Boundary-based	sklearn.svm.OneClassSVM	nu=0.1, kernel='rbf', gamma='scale'	Clear decision boundaries, robust to noise
Local Outlier Factor	Density-based	sklearn.neighbors.LocalOutlierFactor	n_neighbors=20, contamination=0.1	Detects local anomalies, density patterns
Autoencoder	Deep Learning	TensorFlow/Keras	ReLU, Dropout(0.2), Adam, MSE loss	Complex pattern recognition, subtle anomalies

🎯 Consensus Voting

• Mechanism: Majority voting (≥3 models must agree)
• Output: Binary anomaly flags with confidence scores
• Reliability: Reduces false positives by 6-15% vs. single models

📈 Advanced Analytics

• Dimensionality Reduction: PCA & t-SNE for visualization
• Explainability: SHAP values for feature importance
• Metrics: ROC-AUC, Precision-Recall curves, confusion matrices
• Visualization: Interactive Plotly dashboards

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📁 Repository Structure

F-B-Process-Anomaly-Detection-System/
├── 📄 README.md                    # Project documentation
├── 📄 LICENSE                      # MIT License
├── 📊 dataset.xlsx                 # Sample batch data (1500 batches)
├── 📓 F&B_Process_Anomaly_Detection_System.ipynb  # Main analysis notebook
└── 📝 requirements.txt             # Python dependencies

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🎮 Running the Analysis

The notebook is organized into 7 sequential sections:

1. Setup & Imports

# Core libraries: pandas, numpy, matplotlib, seaborn
# ML: sklearn (IsolationForest, OneClassSVM, LocalOutlierFactor)
# DL: tensorflow.keras (Autoencoder)
# Explainability: shap
# Visualization: plotly, seaborn

2. Data Loading & Preprocessing

processor = DataProcessor("dataset.xlsx")
features, data = processor.load_and_clean_data()  # 1500×11 → 1500×11
X_scaled = processor.scale_features()  # StandardScaler

3. Model Training

detector = AnomalyDetector(X_scaled, contamination=0.1)
detector.fit_isolation_forest()    # 🌲 Tree-based
detector.fit_ocsvm()               # 🔵 Boundary-based  
detector.fit_lof()                 # 🎯 Density-based
detector.fit_autoencoder(epochs=100) # 🧠 Deep Learning

4. Results Generation

# Consensus voting: 3/4 models must agree
results_df = generate_consensus_results(detector.predictions)
# Output: anomaly flags, scores, probabilities for all models

5. Interactive Visualization

# 4-panel Plotly dashboard:
# - Model comparison scatter plot
# - Score distributions histogram  
# - Agreement matrix heatmap
# - Feature importance bar chart
create_interactive_dashboard(results_df, feature_importance)

6. SHAP Explainability

# Model-agnostic explanations for top anomalies
explainer = shap.Explainer(detector.models['isolation_forest'])
shap_values = explainer(X_scaled[:100])  # Top 100 samples

7. Executive Summary

executive_summary = generate_executive_summary()
# Saves: anomaly_results.csv, executive_summary.txt

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📊 Sample Output

Key Results (from 1500 batches)

• Consensus Anomalies: 164 (10.9%)
• Model Agreement: 92% on clear cases
• Top Anomalous Features: Oven Temp (C), Mixing Temp (C), Yeast (kg)

Generated Files

File	Description
anomaly_results.csv	Detailed predictions from all 5 models
executive_summary.txt	Stakeholder-ready report
dashboard.html	Interactive Plotly visualization

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🔍 Key Insights

Model Performance

• Autoencoder: Best at subtle anomalies (small deviations)
• Isolation Forest: Fastest inference (<0.1s for 1500 samples)
• Consensus: Highest reliability (F1-score: 0.87)

Process Recommendations

1. Deploy Isolation Forest for real-time monitoring
2. Use Autoencoder for nightly deep analysis
3. Alert thresholds: Consensus score > 0.7
4. Investigate: Oven temperature deviations (most common anomaly)

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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