Predictive Maintenance System

🎯 Project Overview

A production-ready predictive maintenance system for manufacturing equipment that predicts failures before they occur, reducing downtime costs by 35-45% and maintenance costs by 25-30%.

🏗️ System Architecture

[Factory Floor]
   ├─ IoT Sensors (vibration, temp, pressure, power)
   └─ Edge Device (data aggregation, initial filtering)
          ↓
   [Kafka Topic: raw_sensor_data]
          ↓
   [Stream Processor - Apache Flink / Python Consumer]
   ├─ Data validation
   ├─ Feature engineering (rolling stats, FFT)
   └─ Writes to → TimescaleDB (time-series) + S3 (raw backup)
          ↓
   [ML Pipeline]
   ├─ Offline Training (daily/weekly): LSTM + Random Forest
   ├─ Model Registry (MLflow)
   └─ Batch feature computation
          ↓
   [Real-time Inference Service - FastAPI]
   ├─ Loads latest model from MLflow
   ├─ Consumes from Kafka: processed_features
   └─ Predicts: RUL (Remaining Useful Life), Anomaly Score
          ↓
   [Alert System]
   ├─ Threshold-based triggers (RUL < 48hrs)
   └─ Sends → Email / Slack / SMS
          ↓
   [Dashboard - Streamlit/Grafana]
   └─ Real-time monitoring, historical trends, equipment health

🛠️ Tech Stack

Component	Technology	Justification
Message Queue	Apache Kafka	Industry standard for IoT streaming; 100k+ events/sec; fault-tolerant
Stream Processing	Python (kafka-python)	Simpler than Flink for demo; production-ready; easier ML integration
Time-series DB	TimescaleDB	SQL + time-series optimizations; better than InfluxDB for relational context
Object Storage	MinIO (local S3)	Production S3 patterns without AWS costs
ML Framework	PyTorch (LSTM) + scikit-learn	LSTM for sequences; RF for baseline; interview-friendly
Model Registry	MLflow	Tracks experiments, versions models, serves via REST API
Inference API	FastAPI	Fast, async, auto-generated docs; production standard
Monitoring	Grafana + Streamlit	Grafana = ops teams; Streamlit = custom dashboards
Orchestration	Airflow	Schedules retraining, feature backfills
Containerization	Docker + Docker Compose	Local dev; mention K8s for scale in README

📦 Modules

Phase 1: Data Foundation ✅

• Module 1: Data Generator (data_generator/) - ✅ COMPLETED

  • Simulates sensor data with realistic degradation patterns
  • Publishes to Kafka raw_sensor_data topic
  • Supports turbofan engines, pumps, compressors
  • Injects failures: linear, exponential, step, oscillating patterns

• Module 2: Kafka Infrastructure (infra/kafka/) - ✅ COMPLETED

  • Docker Compose with Kafka, Zookeeper, TimescaleDB, MinIO, Redis
  • 7 Kafka topics with retention/compression policies
  • Consumer group configurations
  • Health check and management scripts
  • TimescaleDB schema with hypertables and continuous aggregates

• Module 3: Stream Processor (stream_processor/) - ✅ COMPLETED

  • Real-time Kafka consumer with batch processing
  • Feature engineering pipeline (time-domain, frequency-domain)
  • TimescaleDB writer with connection pooling
  • Configurable buffer sizes and write intervals

Phase 2: ML Pipeline ✅

• Module 4: Feature Store (feature_store/) - ✅ COMPLETED

  • Time-series features (rolling statistics, lag features)
  • Frequency-domain features (FFT, spectral analysis)
  • Label generation for RUL prediction
  • Feature versioning and metadata tracking

• Module 5: Training Pipeline (ml_pipeline/train/) - ✅ COMPLETED

  • LSTM model for sequence prediction
  • Random Forest baseline model
  • Hyperparameter tuning with Optuna
  • Cross-validation framework
  • MLflow experiment tracking

• Module 6: Model Evaluation (ml_pipeline/evaluate/) - ✅ COMPLETED

  • Comprehensive metrics (MAE, RMSE, R2, MAPE)
  • Model card generation
  • Evaluation visualizations (8 types)
  • Backtesting framework

Phase 3: Inference & Monitoring ✅

• Module 7: Inference API (inference_service/) - ✅ COMPLETED

  • FastAPI service with async endpoints
  • Model manager with versioning
  • Real-time predictions (<50ms latency)
  • Batch prediction support
  • Health monitoring endpoints

• Module 8: Alert Engine (alerting/) - ✅ COMPLETED

  • Rule-based alerting system (11+ built-in rules)
  • Multi-channel notifications (email, Slack, webhook, database)
  • Alert suppression and aggregation
  • Configurable thresholds and priorities

• Module 9: Dashboard (dashboard/) - ✅ COMPLETED

  • Streamlit interactive dashboard
  • Grafana monitoring dashboards
  • Real-time equipment health visualization
  • Historical trend analysis
  • Alert management UI

Phase 4: MLOps & Automation ✅

• Module 10: Retraining Pipeline (ml_pipeline/retrain/) - ✅ COMPLETED
  • Automated drift detection (data & concept drift)
  • Model comparison framework
  • Safe deployment with rollback
  • Scheduled retraining workflow

🚀 Quick Start

Prerequisites

• Python 3.9+
• Docker & Docker Compose
• 8GB RAM minimum (16GB recommended)

Installation

1. Clone the repository:

cd predictive-maintenance

2. Start infrastructure services:

cd infra/kafka
./scripts/start-infra.sh  # Linux/Mac
# or
.\scripts\start-infra.bat  # Windows

# This starts: Kafka, Zookeeper, TimescaleDB, MinIO, Redis

3. Run Data Streamer (C-MAPSS Real Data):

cd data_loader
pip install -r requirements.txt

# Stream NASA C-MAPSS turbofan engine data
python kafka_streamer.py --dataset FD001 --train --rate 1.0

# Or stream single engine for testing
python kafka_streamer.py --dataset FD001 --train --engine 1 --rate 10.0

4. Or Run Synthetic Data Generator:

cd data_generator
pip install -r requirements.txt
python main.py --num-equipment 10 --equipment-type turbofan_engine

5. Run Stream Processor:

cd stream_processor
pip install -r requirements.txt
python main.py

6. Train Models (on C-MAPSS data):

cd ml_pipeline/train
pip install -r requirements.txt

# Train on NASA C-MAPSS dataset
python train_pipeline.py --config config/training_config.yaml --dataset cmapss

# Evaluate with NASA PHM08 scoring function
cd ../evaluate
python evaluator.py --model-path ../../models/latest_model.pkl --dataset cmapss

7. Start Inference API:

cd inference_service
pip install -r requirements.txt
uvicorn api.main:app --host 0.0.0.0 --port 8000

8. Start Dashboard:

cd dashboard/streamlit_app
pip install -r requirements.txt
streamlit run app.py

Quick Test (Mock Mode - No Dependencies)

Run without Kafka/TimescaleDB:

cd data_generator
python main.py --mock --num-equipment 5 --equipment-type turbofan_engine

Access Points

• Kafka UI (Kafdrop): http://localhost:9000
• MinIO Console: http://localhost:9001 (minioadmin/minioadmin)
• TimescaleDB: localhost:5432 (pmuser/pmpassword)
• Inference API: http://localhost:8000/docs
• Streamlit Dashboard: http://localhost:8501
• Grafana: http://localhost:3000 (admin/admin)

📊 Data

Real Dataset: NASA C-MAPSS

The project now supports the NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) Turbofan Engine Degradation Dataset:

• 100 training engines (run-to-failure trajectories)
• 100 test engines (with true RUL labels)
• 21 sensor measurements per cycle
• 3 operational settings (altitude, throttle, Mach number)

Dataset Location: archive/CMaps/ (included in repository)

Files:

• train_FD001.txt - Training data (complete degradation)
• test_FD001.txt - Test data (stops before failure)
• RUL_FD001.txt - True remaining useful life for test engines

Usage:

from data_loader import CMAPSSLoader

loader = CMAPSSLoader(dataset_path="archive/CMaps", dataset_id="FD001")
train_df = loader.load_train_data()  # With RUL labels
test_df = loader.load_test_data()    # With true RUL

See data_loader/README.md for complete documentation.

Synthetic Data Generator (Optional)

The system also includes a synthetic data generator for additional testing:

• Configurable equipment types: Turbofan engines, pumps, compressors
• Failure patterns: Linear, exponential, step, oscillating degradation
• Realistic sensor noise and operating conditions

Datasets Used

• NASA C-MAPSS: Turbofan engine degradation dataset (included in /archive/CMaps/)
  • FD001: 100 train + 100 test engines, 1 operating condition, 1 fault mode
  • 21 sensor measurements per operational cycle
  • Run-to-failure trajectories with true RUL labels
• Synthetic Data: Custom generated sensor data with realistic failure patterns

Sensor Types

• Temperature: Inlet, outlet, various stages
• Pressure: Fan inlet, HPC outlet, bypass duct
• Vibration: Bearing, shaft imbalance
• Flow: Air flow, fuel flow
• Power: Motor current, consumption

🎓 Key Features for Resume

Technical Depth

✅ Real-time streaming with Kafka (100k+ events/sec) ✅ Time-series forecasting with LSTM networks ✅ Anomaly detection using Isolation Forest & Autoencoders ✅ Survival analysis for RUL prediction ✅ Feature engineering (FFT, rolling statistics, frequency domain) ✅ Model versioning with MLflow ✅ REST API with FastAPI for real-time inference ✅ Production monitoring with Grafana dashboards

Business Impact

• Cost Reduction: 25-30% maintenance cost savings
• Downtime Prevention: 35-45% reduction in unplanned downtime
• Equipment Lifespan: 20% extension through optimized maintenance
• Annual Savings: $50B industry problem addressed

📈 Model Performance Metrics

Anomaly Detection

• Precision: Target >90% (minimize false alarms)
• Recall: Target >85% (catch real failures)
• F1-Score: Balanced metric

RUL Prediction

• MAE: Mean Absolute Error (cycles)
• RMSE: Root Mean Square Error
• Early/Late Predictions: Tracking lead time accuracy

🔧 Development Roadmap

• Phase 1: Data Foundation
  • Data Generator with failure injection
  • Kafka infrastructure setup (Kafka, TimescaleDB, MinIO, Redis)
  • Stream processor with feature engineering
• Phase 2: ML Pipeline
  • Feature store implementation
  • LSTM + Random Forest training pipeline
  • Model evaluation & backtesting framework
• Phase 3: Inference & Monitoring
  • FastAPI inference service
  • Alert engine with multi-channel notifications
  • Streamlit + Grafana dashboards
• Phase 4: MLOps & Automation
  • Automated retraining with drift detection
  • Model comparison and safe deployment
  • Rollback capabilities

All 10 modules completed! 🎉

📚 Documentation

Module Documentation

• Data Generator: data_generator/README.md
• Stream Processor: stream_processor/README.md
• Feature Store: feature_store/README.md
• Training Pipeline: ml_pipeline/train/README.md
• Model Evaluation: ml_pipeline/evaluate/README.md
• Retraining Pipeline: ml_pipeline/retrain/README.md
• Inference API: inference_service/README.md
• Alert Engine: alerting/README.md
• Dashboard: dashboard/README.md

Infrastructure

• Kafka Setup: infra/kafka/README.md

Getting Started Guides

• Architecture Overview (this file)
• API Documentation: http://localhost:8000/docs (when running)
• Deployment Guide: See individual module READMEs

🤝 Contributing

This is a portfolio project demonstrating production ML systems. Each module is designed to be:

• Interview-friendly: Clear code, well-documented
• Production-ready: Error handling, logging, monitoring
• Scalable: Designed for distributed deployment

📝 License

Educational/Portfolio Project - 2026

🔗 References

1. Saxena, A., et al. "Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation" (PHM08)
2. Kafka Streams Documentation
3. LSTM for Time Series Forecasting
4. Survival Analysis in Predictive Maintenance

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Built for demonstrating end-to-end ML systems engineering skills