Xiaohan-Chen/bear_fault_diagnosis

Bearing fault diagnosis model based on MCNN-LSTM

What it solves

This project provides a method for diagnosing faults in bearings, specifically targeting the identification of anomalies in rotating machinery using vibration data.

How it works

The system uses a hybrid deep learning architecture combining a multi-scale Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks. This approach allows the the model to extract both spatial and temporal features from the vibration signals of the bearing.

Who it’s for

This tool is designed for engineers and researchers in industrial maintenance and mechanical engineering who need to automate the same fault diagnosis process using the CWRU Bearing Data Center dataset.

Highlights

  • Official baseline implementation of a peer-reviewed paper.
  • Migrated from Keras/TensorFlow to PyTorch for modern compatibility.
  • Utilizes the CWRU Bearing Data Center dataset for training and validation.

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