Realistic Condition-Based Anomaly Detection of Multi-Faults in Rotating Machines-Reference-Cited by-同舟云学术

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Realistic Condition-Based Anomaly Detection of Multi-Faults in Rotating Machines

Published:2023-08-09 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET)
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Author:

Mishra Rismaya Kumar¹,Choudhary Anurag²,Fatima Shahab¹,Mohanty Amiya Ranjan³,Panigrahi Bijiaya Ketan⁴

Affiliation:

1. Indian Institute of Technology Delhi,Centre for Automotive Research and Tribology,New Delhi,India

2. School of Interdisciplinary Research, Indian Institute of Technology Delhi,New Delhi,India

3. Indian Institute of Technology,Department of Mechanical Engineering,Kharagpur,West Bengal,India

4. Indian Institute of Technology Delhi,Department of Electrical Engineering,New Delhi,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10244770/10244772/10245372.pdf?arnumber=10245372

Reference43 articles.

1. On the use of the Gaussian mixture model and the Mahalanobis distance for fault diagnosis in dynamic components of electric motors

2. Mixed-fault diagnosis in induction motors considering varying load and broken bars location

3. Variable speed induction motors’ fault detection based on transient motor current signatures analysis: A review

4. Performance Evaluation of Support Vector Machine for System Level Multi-fault Diagnosis

5. Recent Advances in Thermal Imaging and its Applications Using Machine Learning: A Review

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