Vibration and Acoustic data for defect cases of the cylindrical roller bearing (NBC: NU205E)

Citation Author(s):
Anil
Kumar
Rajesh
Kumar
Sant Longowal Institute of Engineering and Technology, Longowal
Submitted by:
Anil Kumar
Last updated:
Mon, 04/17/2023 - 00:17
DOI:
10.21227/n4zg-1a78
Data Format:
Excel Data
License:
Creative Commons Attribution
1324 Views
Categories:
Artificial Intelligence
Signal Processing
Machine Learning
Sensors
Standards Research Data
Keywords:
Vibration; Acoustic; Bearing; Defect; Intelligent defect diagnosis
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Abstract 

Vibration and Acoustic data for defect cases of the cylindrical roller bearing (NBC: NU205E) of Precision Metrology Laboratory, Mechanical Engineering Department, Sant Longowal Institute of Engineering and Technology, Longowal, India

This data can have the following applications:

-        Test the performance of various signal processing techniques.

-        Defect width measurement using vibration data

-        Development of Artificial intelligence models for defect identification

-        Performance of AI model can be tested on unseen data. For example, an AI model which is developed from conditions mentioned in Table 2 can be used to check the accuracy of the model using unseen data for condition mentioned in Table 3.

Instructions: 

 This data should be cited as:

Anil Kumar, Rajesh Kumar. (2022). Vibration and Acoustic data for defect cases of the cylindrical roller bearing (NBC: NU205E). Precision Metrology Laboratory, Mechanical Engineering Department, Sant Longowal Institute of Engineering and Technology, Longowal, India. IEEE Dataport. https://dx.doi.org/10.21227/n4zg-1a78

Very Important, researchers who intend to use this data must cite following relevant publications

1.     Anil Kumar, Yuqing Zhou, C.P. Gandhi, Rajesh Kumar, Jiawei Xiang. (2020) Bearing defect assessment using wavelet transform based deep convolutional neural network (DCNN). Alexandria Engineering Journal 59, 999–1012 (Elsevier). I.F.: 3.73 ISBN: 1110-0168. Available online at: https://doi.org/10.1016/j.aej.2020.03.034

2.     Anil Kumar and Rajesh Kumar (2017) Enhancing weak defect features using undecimated and adaptive wavelet transform for estimation of roller defect size in a bearing. Tribology Transactions. 60(5): 794-806 (Taylor and Francis publication, Impact Factor: 1.96). Available online at: http://doi.org/10.1080/10402004.2016.1213343. ISBN: 1547-397X

3.     Anil Kumar and Rajesh Kumar (2017) Least Square Fitting for Adaptive Wavelet Generation and Automatic Prediction of Defect Size in the Bearing Using Levenberg–Marquardt Backpropagation. Journal of Nondestructive Evaluation. 36 7: 1-16 (Springer publication, Impact Factor: 1.99). Available online at: http://doi.org/10.1007/s10921-016-0385-1. ISBN: 1573-4862

 

 For any query authors can reach at Email: anil_taneja86@yahoo.com         (Anil Kumar)

 

Comments

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Submitted by Anil Kumar on Mon, 04/17/2023 - 00:19

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Submitted by Anil Kumar on Thu, 05/05/2022 - 02:18

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