bearing-python 1.0.4

README.md

Introduction

The bearing-python is package which include common bearing fault diagnosis dataset implement in numpy, CWRU[1], Paderborn[2] and Jnu[3], etc., and will include more in the future. This package can be used to Deep Learning, Transfer Learning and Semi-Supervised Learning (domain adaptation), etc.. Original data comes from the official bearing vibration. The data is based on the original vibration data without repeated segmentation (the sampling window length is equal to hop length) and is provided in numpy.Array format.

Although the original '*. mat' file of CWRU can be downloaded automatically at the first call , we still recommend you, for possible time consumption and transmission interruption, manually download the dataset file through the link below:

CWRU: Download Link：https://pan.xunlei.com/s/VNNBE0GiGHD5r5_l4BsYOTP-A1?pwd=3syk# Extraction Code：3syk

Paderborn: Download Link: https://www.aliyundrive.com/s/AanRmmBNZna
Extraction Code: 4qq9

Jnu: Download Link: https://pan.xunlei.com/s/VNNVrpOoaEAPPdIODQaJQQcQA1?pwd=jnri# Extraction Code: jnri

You may need to reorganize dir if you download CWRU from the official file by following format:

Cwru
	12DriveEndFault
		1797
			12DriveEndFault
				1797	
					0.007-Ball.mat
					..........
			NormalBaseline
				1797
					.........
	12FanEndFault
		......
	48DriveEndFault

install package

pip install bearing-py

Example

datasets

import bearing
dataset = bearing.datasets.Cwru(exp='12DriveEndFault', rpm='1797', length=1024, root=r'.')
print(dataset.x.shape, dataset.y.shape)
print(type(dataset.x))
cout >>:
 (2013, 1024) (2013,)
 <class 'numpy.ndarray'>

---

transform

import torch
import bearing.transform as T
transform = T.Compose(
    T.Normalization()
)
x = torch.randn(1, 1, 1024)
x_transform = transform(x)

Declaration

Our code refers to [Litchiware/cwru (github.com)] and modifies a bit.

for more details about each dataset please visit the following link: [1] CWRU: https://engineering.case.edu/bearingdatacenter/download-data-file. [2] Paderborn: https://mb.uni-paderborn.de/kat/forschung/datacenter/bearing-datacenter/ [3] Jnu: