Variable Dropout One-Dimensional CNN for Vibration-Based Shaft Unbalance Detection in Industrial Machinery-Reference-Cited by-同舟云学术

Variable Dropout One-Dimensional CNN for Vibration-Based Shaft Unbalance Detection in Industrial Machinery

Published:2022-07-02 Issue:1 Volume:11 Page:301-318
ISSN:2523-3920
Container-title:Journal of Vibration Engineering & Technologies
language:en
Short-container-title:J. Vib. Eng. Technol.

Author:

Yadav Arihant,Rajagopalan Sudhar,Purohit Ashish,Singh Jaskaran

Publisher

Springer Science and Business Media LLC

Subject

Microbiology (medical),Immunology,Immunology and Allergy

Link

https://link.springer.com/content/pdf/10.1007/s42417-022-00578-w.pdf

Reference45 articles.

1. Altinors A, Yol F, Yaman O (2021) A sound based method for fault detection with statistical feature extraction in UAV motors. Appl Acoust 183:108325

2. Ranganatha Chakravarthy HS, Bharadwaj SC, Umashankar S, Padmanaban S, Dutta N, Holm-Nielsen JB (2019) Electrical fault detection using machine learning algorithm for centrifugal water pumps. In: Proceedings of the 2019 IEEE Int. Conf. Environ. Electr. Eng. 2019 IEEE Ind. Commer. Power Syst. Eur. EEEIC/I CPS Eur. 2019

3. Guo Y, Parsons T, King R, Dykes K, Veers P (2015) An analytical formulation for sizing and estimating the dimensions and weight of wind turbine hub and drivetrain components. Tech Rep

4. Quallen S, Xing T (2016) CFD simulation of a floating offshore wind turbine system using a variable-speed generator-torque controller. Renew Energy 97:230–242

5. Mobley RK (1998) Condition based maintenance. In: Handbook of condition monitoring. Springer, pp 35–53

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