Machinery Fault Diagnosis using Continuous Wavelet Transform and Artificial Intelligence based classification-Reference-Cited by-同舟云学术

Machinery Fault Diagnosis using Continuous Wavelet Transform and Artificial Intelligence based classification

Published:2022-07-22 Issue: Volume: Page:
ISSN:
Container-title:2022 The 3rd International Conference on Robotics Systems and Vehicle Technology (RSVT)
language:
Short-container-title:

Author:

Al Tobi Maamar Ali Saud¹,K p Ramachandran²,Al-Araimi Saleh²,Pacturan Rene¹,Rajakannu Amuthakkannan¹,Achuthan Geetha²

Affiliation:

1. College of Engineering/Mechanical and Industrial Engineering Department, National University of Science and Technology, Oman

2. National University of Science and Technology, Oman

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3560453.3560463

Reference28 articles.

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2. Yuan Z. Zhou T. Liu J. Zhang C. and Liu Y. 2021. Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection. Shock and Vibration 2021 pp.1-17. Yuan Z. Zhou T. Liu J. Zhang C. and Liu Y. 2021. Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection. Shock and Vibration 2021 pp.1-17.

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4. On the Usefulness of Pre-processing Methods in Rotating Machines Faults Classification using Artificial Neural Network;Alzghoul A.;Journal of Applied and Computational Mechanics,2021

5. Jalayer M. Orsenigo C. and Vercellis C. 2021. Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM Fast Fourier and continuous wavelet transforms. Computers in Industry 125 p.103378. Jalayer M. Orsenigo C. and Vercellis C. 2021. Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM Fast Fourier and continuous wavelet transforms. Computers in Industry 125 p.103378.