Acoustic and vibration data for defect cases of the centrifugal pump

This data should be cited as:

Anil Kumar and Rajesh Kumar. 2022. Acoustic and vibration data for defect cases of the centrifugal pump. Precision Metrology Laboratory, Mechanical Engineering Department, Sant Longowal Institute of Engineering and Technology, Longowal, India. Available at: https://dx.doi.org/10.21227/8cqr-jr43.

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

1. Anil Kumar, C.P. Gandhi, Yuqing Zhou, Rajesh Kumar, Jiawei Xiang (2020) Improved deep convolutional neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images. Applied Acoustics. 167. 107399 (Elsevier publication, Impact Factor: 2.63). Available online at: https://doi.org/10.1016/j.apacoust.2020.107399: ISBN: 0003-682X

2. Anil Kumar and Rajesh Kumar (2018) Oscillatory behavior-based wavelet decomposition for the monitoring of bearing condition in centrifugal pumps. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology. 232(6): 757-772 (Sage publication, Impact Factor: 1.67). Available online at: http://doi.org/10.1177/1350650117727976. ISBN: 2041-305X

3. Anil Kumar and Rajesh Kumar (2017) Time-frequency analysis and support vector machine in automatic detection of defect from vibration signal of centrifugal pump. Measurement, 108: 119-133 (Elsevier publication, Impact Factor: 3.92) Available online at:  http://doi.org/10.1016/j.measurement.2017.04.041. ISBN: 0263-2241

4. Anil Kumar, Hesheng Tang, Govind Vashishtha, Jiawei Xiang, (2022) Noise subtraction and marginal enhanced square envelope spectrum (MESES) for the identification of bearing defects in centrifugal and axial pump. Mechanical Systems and Signal Processing. 165. 108366 (Elsevier publication. I.F.: 6.82) Available online at: https://doi.org/10.1016/j.ymssp.2021.108366.  ISBN: 0888-3270