An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

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ISSN Print: 1947-3931
ISSN Online: 1947-394X
www.scirp.org/journal/eng
E-mail: eng@scirp.org

"An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP"
written by Faisal Al Thobiani, Van Tung Tran, Tiedo Tinga,
published by Engineering, Vol.9 No.6, 2017

has been cited by the following article(s):

[1]	Unbalance detection in rotating machinery based on support vector machine using time and frequency domain vibration features
	2021

[2]	REMOTE MONITORING AND INTELLIGENT FAILURE DIAGNOSIS OF ROTATING MACHINERY USING THE B/S MODE
	International Journal of Mechatronics and Applied …, 2021

[3]	Modern Diagnostics of Aircraft Gas Turbine Engines
	2020

[4]	Optimal region growing and multi-kernel SVM for fault detection in electrical equipments using infrared thermography images
	2020

[5]	A Review on Fault Diagnosis of Misaligned Rotor Systems.
	2020

[6]	Physical Model-Based Prognostics and Health Monitoring to Enable Predictive Maintenance
	2019

[7]	A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network
	Sensors, 2018

[8]	88. Feather Extraction of Equipment Fault Diagnosis
	2017

[1]	RFR-GA-BLS: A Feature Selection and Parameter Optimization Method for Fault Diagnosis of Rolling Bearing Using Infrared Images Applied Sciences, 2023 DOI:10.3390/app13137350

[2]	Unbalance detection in rotating machinery based on support vector machine using time and frequency domain vibration features Noise & Vibration Worldwide, 2021 DOI:10.1177/0957456521999836

[3]	Modern Diagnostics of Aircraft Gas Turbine Engines IOP Conference Series: Materials Science and Engineering, 2020 DOI:10.1088/1757-899X/714/1/012010

[4]	Predictive Maintenance in Dynamic Systems 2019 DOI:10.1007/978-3-030-05645-2_11

[5]	A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network Sensors, 2018 DOI:10.3390/s18092955

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