/BearingFaultDetection

DC competition of bearing fault detection: rank 40/1388.

Primary LanguagePython

BearingFaultDetection

This code is for Bearing Faul Detection competition in DC platform. Rank 40/1388.

Please see offical website: https://challenge.datacastle.cn/v3/cmptDetail.html?id=248

Code Overview

  1. data/ directory stores the train data, test data and feature data containing time/frequency/time-frequency feature.
  2. analyze/ directory is used to analyze the correlatoins between features by a correlation matrix.
  3. preprocessing/ directory is main part to preprocess data: 1) Use wavelet filter to denoise data; 2) Use wavelet decoposing tool to extract time-frequency domain feature of dat. 3) And finally, apply PCA to reduce dimension of feature.
  4. RF.py uses random forest as our classifier to train model.

Visualization

Framework

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Comparison of signal processed by wavelet filter

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Heatmap of correlation matrix of features

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Report

Please refer to the report.pdf for details.