DevMesh

Induction Motor Faults Detection

Gokulnath N

Gokulnath N

Chennai, Tamil Nadu

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  • 0 Collaborators

This project focuses on detecting faults in induction motors using machine learning and deep learning models. It preprocesses sensor data, trains models like SVM, kNN, and DNN, evaluates performance, and provides predictions with insights into motor health. ...learn more

Project status: Under Development

Networking, Internet of Things, Artificial Intelligence, Cloud, Performance Tuning

Intel Technologies
Intel® Core™ Processors, 10th Gen Intel® Core™ Processors, Intel powered laptop

Code Samples [1]

Overview / Usage

The project addresses the critical issue of early fault detection in induction motors, which is vital for minimizing downtime and maintenance costs in industrial settings. By leveraging data from sensors (e.g., vibration, temperature), it enables predictive maintenance and enhances operational efficiency.

Methodology / Approach

We preprocess sensor data through cleaning and normalization before splitting it into training and testing sets. Models such as SVM, kNN, and DNN are trained and evaluated using metrics like accuracy and ROC curves. The methodology ensures accurate fault prediction, aiding industrial automation and reliability.

Technologies Used

  • Libraries: TensorFlow, scikit-learn, pandas, NumPy, matplotlib.
  • Tools: Python, Jupyter Notebook.
  • Hardware: Systems with Intel CPUs or AI accelerators (optional).

Repository

https://www.kaggle.com/code/zengamer/induction-motor-faults-detection

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