Predictive Maintenance using Amazon SageMaker

GitHub Link: Predictive-Maintenance-using-Amazon-SageMaker

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Project Overview

This project focuses on predictive maintenance for industrial equipment using machine learning and Amazon SageMaker. The goal is to predict the remaining useful life (RUL) of equipment based on sensor data, enabling proactive maintenance and reducing downtime.

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Key Features

1. Deep Learning Model: Developed a deep learning model using MXNet to predict the remaining useful life (RUL) of industrial equipment, achieving 90% accuracy.
2. Scalable Deployment: Deployed the model on Amazon SageMaker with AWS Lambda for batch inference, ensuring scalability and real-time performance.
3. Real-World Application: The project addresses a critical use case in industrial settings, where predictive maintenance can significantly reduce costs and improve operational efficiency.

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Technologies Used

• Machine Learning Frameworks: MXNet, Scikit-learn
• Cloud Platform: Amazon SageMaker, AWS Lambda
• Data Preprocessing: Pandas, NumPy
• Visualization: Matplotlib, Seaborn

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Project Structure

Predictive-Maintenance-using-Amazon-SageMaker/
├── data/                   # Dataset used for training and testing
├── notebooks/              # Jupyter notebooks for data exploration and model training
├── scripts/                # Python scripts for data preprocessing and model deployment
├── models/                 # Trained model files
├── README.md               # Project documentation
└── requirements.txt        # Python dependencies

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How to Use

1. Clone the Repository:

   git clone https://github.com/gamidirohan/Predictive-Maintenance-using-Amazon-SageMaker
   cd Predictive-Maintenance-using-Amazon-SageMaker

2. Install Dependencies:

   pip install -r requirements.txt

3. Run the Notebooks:

   • Open the Jupyter notebooks in the notebooks/ directory to explore the data, train the model, and evaluate performance.

4. Deploy the Model:

   • Follow the instructions in the scripts/ directory to deploy the model on Amazon SageMaker.

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Results

• Achieved 90% accuracy in predicting the remaining useful life (RUL) of industrial equipment.
• Successfully deployed the model on Amazon SageMaker, enabling real-time inference and scalability.

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Future Work

• Integrate anomaly detection to identify potential equipment failures before they occur.
• Extend the model to support multimodal data (e.g., combining sensor data with maintenance logs).

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Contributing

Contributions are welcome! If you'd like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Submit a pull request with a detailed description of your changes.