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Machine-Guard-AI

An AI-powered, sustainable industrial monitoring platform combining IoT, machine learning, and autonomous decision-making.

Status Python Kotlin Android

Project Overview

Machine-Guard-AI is an end-to-end industrial intelligence system designed to transform industries from reactive monitoring to autonomous, predictive, and sustainable operations.

The platform integrates:

  • IoT Hardware: ESP32-based real-time sensing (temperature, vibration, gas, power)
  • Backend Services: Scalable Flask API with MQTT ingestion and data storage
  • Machine Learning: Unsupervised anomaly detection and digital twin prediction
  • Mobile Application: Native Android app with real-time monitoring and AI insights
  • Security: Role-based access control and zone-based industrial monitoring

Why Machine-Guard-AI?

Traditional industrial systems:

  • React after failures occur
  • Rely on fixed thresholds
  • Waste energy
  • Require manual inspection
  • Expose workers to hazardous environments

Machine-Guard-AI enables:

  • Early failure prediction
  • Self-learning machine behavior modeling
  • Autonomous preventive actions
  • Energy-efficient operations
  • Safer and greener industries

Core Capabilities

IoT and Real-Time Data Ingestion

  • Receives real-time sensor data from ESP32 devices via MQTT
  • Supports temperature, vibration, gas, and power sensors
  • Low-latency, fault-tolerant ingestion

AI-Driven Anomaly Detection

  • Unsupervised ML models learn normal machine behavior
  • Detect unusual patterns without static thresholds
  • Machine-specific fine-tuning without full retraining
  • ML-ready numpy-compatible data export

Digital Twin-Based Prediction

  • Virtual behavioral model for each machine
  • Simulates future machine states
  • Predicts failures before physical breakdown

Autonomous Actions

  • Early alerts and risk scoring
  • Load control and safety shutdowns
  • Gas and thermal hazard response
  • Reduced human dependency

Sustainability Focus

  • Energy optimization and reduced waste
  • Extended machine lifespan
  • Lower carbon emissions
  • Improved worker safety

User Roles & Access Control

Owner

  • Full access to all zones
  • Can add and remove zones
  • Views system-wide analytics and insights
  • Controls expansion and configuration

Operator

  • Restricted to one assigned zone
  • Zone selected during signup
  • Initially limited to Zone 1
  • Cannot add or remove zones
  • Views data only for assigned zone

Zone-Based Architecture

  • System starts with a single default zone: Zone 1
  • Only Owners can create or delete zones
  • Operators are strictly restricted to their assigned zone
  • Designed for multi-zone industrial scaling

System Architecture

Data Flow:

ESP32 Sensors → MQTT Broker → Backend (Flask) → REST APIs → Android App
                     ↓
              Firebase/Database
                     ↓
           ML Models & Digital Twin

Component Overview:

  • ESP32 IoT Layer: Real-time sensor data collection
  • MQTT Broker: Low-latency message routing
  • Backend Services: Data validation, storage, ML pipeline, anomaly detection
  • Android Application: Real-time dashboard, alerts, AI-powered insights
  • ML Engine: Predictive maintenance and behavioral modeling

Repository Structure

Machine-Guard-AI/
│
├── backend/                           # Flask Backend Services
│   ├── app.py                         # Flask application entry point
│   ├── config.py                      # Environment configuration
│   ├── models.py                      # SQLAlchemy ORM models
│   ├── database.py                    # Database layer
│   ├── mqtt_client.py                 # MQTT client with validation
│   ├── firebase_config.py             # Firebase integration
│   ├── routes/
│   │   ├── sensors.py                 # Sensor data APIs
│   │   ├── system.py                  # Health & system endpoints
│   │   └── control.py                 # Control actions
│   ├── utils/
│   │   └── logger.py                  # Structured logging
│   ├── requirements.txt
│   └── README.md
│
├── Machine_GuardAI/                   # Android Application (Kotlin)
│   ├── app/src/main/java/com/example/machine_guard__ai/
│   │   ├── MainActivity.kt            # App entry point & navigation
│   │   ├── HomeScreen.kt              # Main dashboard with live data
│   │   ├── LoginScreen.kt             # User authentication
│   │   ├── SignupScreen.kt            # User registration
│   │   ├── OnboardingScreen.kt        # First-time user experience
│   │   ├── ZoneManagementScreen.kt    # Admin zone configuration
│   │   ├── InsightsScreen.kt          # Analytics & historical data
│   │   ├── AssetsListScreen.kt        # List of monitored machines
│   │   ├── AlertsListScreen.kt        # Alert history & logs
│   │   ├── MqttRepository.kt          # MQTT client for real-time data
│   │   ├── AuthRepository.kt          # Authentication & role management
│   │   ├── GeminiRepository.kt        # Google Gemini AI integration
│   │   ├── GrokRepository.kt          # xAI Grok integration
│   │   ├── SmsAlertManager.kt         # SMS notification handler
│   │   ├── EmailAlertManager.kt       # Email alert system (JavaMail)
│   │   └── ui/theme/                  # Material3 theme & styling
│   └── local.properties
│
├── ML Model and ML-Backend/           # Machine Learning Pipeline
│   ├── machine_guard_core.py          # Core ML engine
│   ├── model_training.py              # Training pipeline
│   ├── realtime_detection.ipynb       # Real-time anomaly detection
│   ├── evaluate_model.py              # Model evaluation
│   ├── firebase_monitor.py            # Firebase data monitoring
│   ├── calibrate.py                   # Sensor calibration
│   ├── models/
│   │   ├── esp32_calibration.json     # Calibration configs
│   │   └── esp32_training.csv         # Training datasets
│   └── testing/                       # Test data & validation
│
├── esp32/                             # IoT Firmware
│   ├── esp32_sensor_client.ino        # Arduino/ESP32 sensor code
│   └── README.md
│
├── docker-compose.yml                 # Multi-container orchestration
├── Dockerfile                         # Backend containerization
├── mosquitto.conf                     # MQTT broker configuration
├── quickstart.py                      # Quick setup script
└── README.md                          # This file

Android Application

Overview

Machine_GuardAI is a native Android application built with Kotlin and Jetpack Compose for real-time industrial machine monitoring and predictive maintenance. It provides an intuitive interface for operators and owners to monitor machine health, receive alerts, and take preventive actions.

Key Features

Real-Time Dashboard

  • Live visualization of machine health scores
  • Real-time sensor metrics (Temperature, Vibration, Current, Gas)
  • Risk level indicators with color-coded alerts
  • System health score card with predictive insights

Predictive Maintenance

Android App Setup

# Open project in Android Studio
# File -> Open -> Select Machine_GuardAI directory

# Configure local.properties with your SDK path
sdk.dir=/path/to/Android/sdk

# Sync Gradle dependencies
# Build -> Sync Project with Gradle Files

# Run on emulator or physical device
# Run -> Run 'app'

Requirements:

  • Android Studio Hedgehog or newer
  • Android SDK API 24+ (Android 7.0+)
  • Kotlin 1.9+
  • Gradle 8.0+

API Quick Referencestorical comparisons

Intelligent Alert System

  • Automatic SMS Alerts: Sent when machine health drops below 60%
  • Email Notifications: Critical alerts via SMTP (Gmail integration)
  • Real-time push notifications for immediate attention
  • Alert history and log management

AI Integration

  • Google Gemini: Advanced analysis and natural language insights
  • xAI Grok: Contextual recommendations and decision support
  • AI-powered anomaly explanation and root cause analysis

Role-Based Access

  • Owner Role: Full system access, zone management, analytics
  • Operator Role: Zone-specific monitoring and basic controls
  • Secure authentication with role-based UI adaptation

Zone Management

  • Multi-zone industrial facility support
  • Owner-controlled zone creation and configuration
  • Zone-specific asset and alert management

Tech Stack

Language: Kotlin
UI Framework: Jetpack Compose (Material3)
Architecture: MVVM with Repository pattern

Key Dependencies:

  • org.eclipse.paho:org.eclipse.paho.client.mqttv3 - MQTT client for IoT connectivity
  • com.squareup.okhttp3:okhttp - HTTP client for Grok API
  • com.sun.mail:android-mail - JavaMail for email alerts
  • com.google.ai.client.generativeai - Google Gemini SDK

Application Structure

com.example.machine_guard__ai/
├── MainActivity.kt              # Entry point & navigation host
├── HomeScreen.kt                # Main dashboard with sensors & health
├── LoginScreen.kt               # User authentication interface
├── SignupScreen.kt              # New user registration
├── OnboardingScreen.kt          # Introduction flow for new users
├── ZoneManagementScreen.kt      # Admin interface for zone management
├── InsightsScreen.kt            # Analytics & historical data view
├── AssetsListScreen.kt          # List of monitored machines/assets
├── AlertsListScreen.kt          # Alert history & logs
├── MqttRepository.kt            # MQTT client for real-time sensor data
├── AuthRepository.kt            # User authentication & role management
├── GeminiRepository.kt          # Google Gemini AI integration
├── GrokRepository.kt            # xAI Grok API integration
├── SmsAlertManager.kt           # SMS alert handling with permissions
├── EmailAlertManager.kt         # Email alert system (JavaMail)
└── ui/theme/
    ├── Color.kt                 # App color palette (MG_Blue, MG_Error, etc.)
    ├── Theme.kt                 # Material3 theme configuration
    └── Type.kt                  # Typography definitions

MQTT Configuration

  • Broker: tcp://test.mosquitto.org:1883 (configurable)
  • Topic: machine_guard/esp32_Machine_1/status
  • Protocol: JSON payload with real-time sensor readings

Alert Thresholds

  • Critical Health: < 60% triggers SMS and email
  • High Risk: Automatic notification to assigned operators
  • Gas Detection: Immediate alert for hazardous levels
  • Temperature/Vibration: Threshold-based warnings

Quick Start

Backend Setup (5 Minutes)

# Create virtual environment
python3 -m venv venv && source venv/bin/activate

# Install dependencies
pip install -r backend/requirements.txt

# Configure environment
cd backend && cp .env.example .env

python app.py

Visit: http://localhost:5000/api/health

API Quick Reference

# Health check
curl http://localhost:5000/api/health

# Latest sensor reading
curl http://localhost:5000/api/sensors/latest

# Historical data
curl "http://localhost:5000/api/sensors/history?limit=100"

# Statistics
curl http://localhost:5000/api/sensors/stats

# ML-ready data
curl http://localhost:5000/api/sensors/ml-data

Configuration

Create backend/.env:

MQTT_BROKER=localhost
MQTT_PORT=1883
DATABASE_URL=sqlite:///sensors.db
FLASK_HOST=0.0.0.0
FLASK_PORT=5000
FLASK_ENV=production
LOG_LEVEL=INFO

Docker Support

docker-compose up -d
# Or manually:
docker build -t machine-guard-ai .
docker run -p 5000:5000 -e MQTT_BROKER=localhost machine-guard-ai

Data Model

Field Type id Integer device_id String temperature Float vibration Float gas Float power Float timestamp DateTime created_at DateTime

Performance

  • 1000+ messages/second ingestion
  • <50ms end-to-end latency

Built With

Backend:

  • Flask (Python 3.9+)
  • SQLAlchemy ORM
  • paho-mqtt
  • PostgreSQL / SQLite

Documentation

  • backend/README.md - Backend API and architecture
  • esp32/README.md - ESP32 firmware and hardware setup
  • SETUP.md - Installation and deployment guide (if available)

Getting Started Guide

  1. Hardware Setup: Flash ESP32 with sensor client firmware
  2. Backend Deployment: Deploy Flask backend with MQTT broker
  3. Database Configuration: Set up PostgreSQL or SQLite
  4. Android App: Install APK or build from source
  5. ML Models: Train models using historical sensor data
  6. User Registration: Create Owner account and configure zones

Support & Troubleshooting

Backend Issues:

  • Enable LOG_LEVEL=DEBUG in .env for detailed logs
  • Verify MQTT broker connectivity: mosquitto_sub -h localhost -t '#'
  • Check database migrations: flask db upgrade

Android Issues:

  • Verify MQTT broker URL in MqttRepository.kt
  • Check permissions: SMS, Email, Network
  • Enable developer mode for detailed crash logs

IoT Issues:

  • Verify ESP32 WiFi credentials
  • Check MQTT topic subscription
  • Monitor serial output for debugging

Contributing

Contributions are welcome! Please follow these guidelines:

  • Fork the repository
  • Create a feature branch
  • Write clean, documented code
  • Submit pull request with detailed description

License

This project is licensed under the MIT License.

Acknowledgments

  • Eclipse Paho for MQTT libraries
  • Google for Gemini AI integration
  • xAI for Grok API access
  • Community contributors and testers

Version: 1.0.0
Status: Production Ready
Updated: February 2026
Maintained by: VIM4L-M

IoT:

  • ESP32 microcontroller
  • Arduino framework
  • MQTT protocol

ML/AI:

  • Python (NumPy, Pandas, Scikit-learn)
  • TensorFlow/PyTorch
  • Google Gemini
  • xAI Grokvalidation on all endpoints
  • SQL injection protection via ORM
  • Role-based and zone-based authorization
  • CORS enabled for frontend integration

Testing

python backend/test_generator.py --count 10

Sustainability Impact

  • 15-30% reduction in maintenance costs
  • 10-20% energy savings
  • 20-40% reduction in unplanned downtime
  • 30-50% improvement in worker safety
  • Lower environmental footprint

Built With

  • Flask
  • SQLAlchemy
  • paho-mqtt
  • PostgreSQL / SQLite
  • Docker

Documentation

  • backend/README.md - Backend API and architecture
  • SETUP.md - Installation and deployment guide

Support

  • Enable LOG_LEVEL=DEBUG for troubleshooting
  • Refer to SETUP.md for deployment issues

Version: 1.0.0 Status: Production Ready Updated: January 2026

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