Skip to content

Latest commit

 

History

History
68 lines (48 loc) · 1.79 KB

README.md

File metadata and controls

68 lines (48 loc) · 1.79 KB
Raw

Fault Diagnosis with Time-Domain Vibration Data

This repository aims to diagnose types of faults based on time-domain vibration data using neural networks. It covers experiments conducted with Temporal Convolution Network-LSTM models.

Experimental Setup

  • 4 DC Motors
    • Normal | Misalignment | Unbalance | Damaged Bearing
  • 26.5kHz Sampling Rate
  • Data Collection: 102400 samples every 5 seconds

Dependencies

Version of libraries may not make a problem except tensorflow.

  • tensorflow == 2.9.0
  • scikit-learn == 1.0.2
  • numpy == 1.21.6
  • scipy == 1.9.1
  • matplotlib == 3.5.2

Temporal Convolution Network-LSTM

The TCN implementation is based on @philipperemy's work (https://github.com/philipperemy/keras-tcn).

The TCN-LSTM architecture is created by myself.

TCN Framework

  • input shape: (, 800)
  • Train data: 13639
  • Validation data: 3410
  • Test data: 1895

Train Data Structure

  • data(data_path)
    • label 1
      • data1.csv
      • data2.csv
      • ...
    • label 2
    • label 3
    • label 4

Experimental results

Even if the accuracy for sampling points is not 100%, with a total of over 100,000 data points available for real-time diagnosis, the final fault diagnosis is determined based on the majority of predictions obtained during real-time diagnosis. In the experimental environment, as mentioned earlier, the test results confirmed successful fault type diagnosis.

+) 240808

  • Apply Normalization with MinMaxScaler()
  • It needs one normal status vibration data as csv

TCN-LSTM

  • Test datasets = 1895
  • ROC AUC = 0.995
  • F1 Score = 0.96328125
  • Accuracy = 96.328 %


TCN Confusion matrix