Condition monitoring of industrial exhaust motor-fan assembly using statistical feature optimization with nature-inspired optimizers-Reference-Cited by-同舟云学术

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Condition monitoring of industrial exhaust motor-fan assembly using statistical feature optimization with nature-inspired optimizers

Published:2024-01-25 Issue: Volume: Page:97-102
ISSN:
Container-title:2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC)
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Author:

Das Abhrodeep¹,Agrawal Ayushman²,Kundoliya Nishkarsh³,Dutta Ananta²,Pal Surjya K²,Kumar Rishi⁴

Affiliation:

1. Jalpaiguri Government Engineering College,Dept. of Computer Science and Engineering,WB,India

2. Indian Institute of Technology,Dept. of Mechanical Engineering,Kharagpur,WB,India

3. Indian Institute of Technology,Dept of Ocean Engg & Naval Architecture,Kharagpur,WB,India

4. Steel Authority of India Limited (SAIL),Research & Development Centre for Iron & Steel,Ranchi,Jharkhand,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10467194/10468072/10468197.pdf?arnumber=10468197

Reference18 articles.

1. Deep Learning Based Approach for Bearing Fault Diagnosis

2. A Cross-Domain Stacked Denoising Autoencoders for Rotating Machinery Fault Diagnosis Under Different Working Conditions

3. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network

4. Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks

5. Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

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