deep-sensor

A PyTorch framework for sensory signals in machinery fault diagnostics.

requirements:

• numpy (>= 1.13.1)
• pytorch (>= 0.1.12.post2)

usage

1. data:
   • .txt file for data: # signal_samples x # signal_length
   • .txt file for label: # signal_samples x 1
   • An example of fake dataset with 100 samples and labels are in toydata.
2. $ python run.py

models

1D

• WDCNN from paper: A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
• Ince's from paper: Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks
• 1DCNN from paper: Convolutional Neural Networks for Fault Diagnosis Using Rotating Speed Normalized Vibration (cite as)

@InProceedings{wei_conv_2019,
author="Wei, Dongdong
and Wang, KeSheng
and Heyns, Stephan
and Zuo, Ming J.",
title="Convolutional Neural Networks for Fault Diagnosis Using Rotating Speed Normalized Vibration",
booktitle="Advances in Condition Monitoring of Machinery in Non-Stationary Operations",
year="2019",
publisher="Springer International Publishing",
pages="67--76",
isbn="978-3-030-11220-2"
doi="10.1007/978-3-030-11220-2_8"
}

2D

• LeNet5 for spectrogram of signals
• VGG forked from https://github.com/kuangliu/pytorch-cifar/blob/master/models/vgg.py