AI-Powered Predictive Maintenance System for IoT Devices

Overview

This project implements an AI-powered predictive maintenance system for IoT devices using machine learning to predict equipment failures before they occur. It uses sensor data from industrial equipment to forecast potential breakdowns, enabling proactive maintenance and reducing downtime.

Problem Statement

Industrial equipment failures can lead to costly downtime, safety risks, and production losses. Traditional maintenance approaches are either reactive (fix after failure) or preventive (schedule-based), both inefficient. Predictive maintenance uses AI to analyze sensor data in real-time to predict failures, allowing maintenance to be performed just before a breakdown.

Industry Relevance

This system is crucial for:

• Manufacturing Plants: Predict CNC machine failures
• Factories: Monitor conveyor belts and assembly lines
• Power Plants: Forecast turbine and generator issues
• Automotive Industry: Detect engine and transmission problems
• Aviation Industry: Predict aircraft component failures

Benefits include:

• Reduced unplanned downtime (up to 30-50%)
• Cost savings (maintenance costs reduced by 25-30%)
• Improved safety and efficiency
• Extended equipment lifespan

Tech Stack

• Language: Python 3.8+
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
• Model: Random Forest Classifier
• Dataset: NASA Turbofan Engine Degradation Simulation Dataset

Dataset

The project uses the NASA C-MAPSS dataset, which simulates turbofan engine degradation. It includes:

• Sensor readings (temperature, pressure, vibration, etc.)
• Engine operational cycles
• Failure indicators

Architecture

1. Data Collection: Sensor readings from IoT devices
2. Preprocessing: Cleaning and normalizing data
3. Feature Engineering: Creating relevant features
4. Model Training: Training ML model on historical data
5. Prediction: Real-time failure prediction
6. Alert System: Notifications for maintenance
7. Visualization: Dashboard for monitoring

Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/AI-Predictive-Maintenance-IoT.git
   cd AI-Predictive-Maintenance-IoT

2. Create virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Download dataset:

   • Download FD001 train and test data from NASA repository
   • Place in data/ folder as train_FD001.txt and test_FD001.txt

Usage

Running the Notebook

1. Open notebooks/predictive_maintenance.ipynb in Jupyter
2. Run all cells sequentially

Running the Script

python main.py

Expected Output

• Model accuracy metrics
• Confusion matrix
• Failure prediction plots
• Feature importance charts

Results

• Model Accuracy: ~95%
• Precision: 0.92
• Recall: 0.88
• F1-Score: 0.90

Screenshots

• Dataset Preview
• Model Performance
• Failure Prediction

Learning Outcomes

• Understanding predictive maintenance concepts
• Working with time-series sensor data
• Machine learning model development
• Data preprocessing and feature engineering
• Model evaluation and visualization
• GitHub project management

Contributing

Feel free to submit issues and enhancement requests!

License

This project is licensed under the MIT License.