Predictive Maintenance IoT Platform (Azure)

🚀 Project Overview

The Predictive Maintenance IoT Platform is an end-to-end Azure-native solution designed to simulate IoT device telemetry, process real-time streaming data, detect anomalies, trigger alerts, and visualize device health.

It demonstrates Cloud, IoT, Machine Learning, and DevOps integration — providing a production-style workflow for predictive maintenance scenarios in industries like manufacturing, energy, or logistics.

Real-Life Example:
Imagine a factory with hundreds of machines. Each machine is equipped with sensors monitoring temperature, vibration, and operating hours. This platform can predict machine failure before it happens, allowing maintenance teams to act proactively, reducing downtime and saving costs.

---

Arcitecture Diagram

📈 How It Works

1. IoT Device Simulation

   • Python scripts simulate device telemetry (temperature, vibration, pressure, etc.)
   • Data is sent in JSON format to Azure IoT Hub

2. Real-Time Stream Processing

   • Azure Event Hub collects streaming telemetry
   • Stream Analytics detects anomalies (e.g., temperature > threshold)
   • Alerts are triggered automatically

3. Alerting System

   • Azure Functions process events and send notifications:
     • Teams messages
     • Email alerts
     • Optional API triggers for automated maintenance

4. Containerization & Deployment

   • Services (Functions, ML API) are dockerized
   • Images pushed to Azure Container Registry (ACR)
   • Deployment to Azure Kubernetes Service (AKS) for scalable infrastructure

5. CI/CD Automation

   • GitHub Actions workflows:
     • Build & push Docker images
     • Apply Terraform scripts for infrastructure provisioning
     • Deploy AKS manifests

6. Monitoring & Visualization

   • Prometheus collects metrics from deployed services
   • Grafana dashboards display:
     • Device health
     • Anomaly rates
     • Resource usage
   • Optionally connected with Azure Monitor for live telemetry

7. Machine Learning (Optional)

   • Train predictive models using Azure ML or local Python (scikit-learn/TensorFlow)
   • Deploy as REST endpoints
   • Integrate with Stream Analytics or Function app for real-time predictions

8. Security & Compliance

   • Secrets managed using Azure Key Vault
   • Role-Based Access Control (RBAC) implemented for resources
   • Network Security Groups (NSGs) ensure secure communication

---

📸 Project Screenshots

These screenshots showcase the step-by-step implementation of the Predictive Maintenance Platform — from IoT data ingestion to visualization dashboards.

🧰 Tools & Technologies

Azure Services:

• IoT Hub, Event Hub, Blob Storage, Functions, Stream Analytics, ACR, AKS, Azure ML, Monitor, Key Vault

DevOps Tools:

• Terraform (IaC), Docker, GitHub Actions (CI/CD), Prometheus, Grafana

Languages & Frameworks:

• Python, YAML, HCL (Terraform)

IDE / Editors:

• VS Code

---

🏗️ Project Structure

predictive-maintenance-azure/ ├── infra/ # Terraform scripts for Azure infrastructure ├── functions/ # Azure Functions & Python code ├── k8s/ # Kubernetes manifests / Helm charts ├── .github/workflows/ # GitHub Actions CI/CD pipelines ├── docs/ # Architecture diagrams, notes, screenshots └── README.md

yaml Copy code

---

⚡ Quick Start

1. Clone the repository

bash git clone https://github.com//predictive-maintenance-azure.git cd predictive-maintenance-azure 2. Install Dependencies bash Copy code

Python dependencies

pip install -r functions/requirements.txt

Terraform

terraform init terraform plan 3. Simulate IoT Data bash Copy code python functions/iot_simulator.py 4. Deploy Containers & AKS bash Copy code

Build Docker images

docker build -t functions/

Push to Azure Container Registry

docker push .azurecr.io/

Deploy to AKS

kubectl apply -f k8s/ 5. Monitor & Alert Access Grafana at http://localhost:3000

Connect Prometheus for metrics simulation

Optional: Integrate with Azure Monitor for live telemetry

🎯 Real-Life Use Cases

Manufacturing: Predict machine failures to schedule preventive maintenance

Energy: Monitor turbines or solar panels for anomalies

Logistics: Predict vehicle breakdowns to minimize delays

Healthcare: Monitor critical medical equipment in hospitals

🎥 Demo

IoT simulator sending telemetry data

Alerts triggering in Functions

Grafana dashboards updating in real time

Optional ML model predicting failure risks

Tools for demo: OBS Studio, screen recordings, or GIFs for GitHub docs

🔐 Security Considerations

Secrets managed via Azure Key Vault

RBAC ensures correct permissions for users/services

Network Security Groups (NSG) restrict access to resources

Optional vulnerability scanning using Trivy

📜 License

This project is licensed under the MIT License.

🌟 Outcome

A fully automated, Azure-native predictive maintenance platform demonstrating cloud, IoT, ML, and DevOps mastery — ready for recruiters and portfolio showcase.

Contributor

Shivali v Adharsh .U