Fault Diagnostics on Steam Boilers and Forecasting System Based on Hybrid Fuzzy Clustering and Artificial Neural Networks in Early Detection of Chamber Slagging/Fouling

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ISSN Print: 2153-1285
ISSN Online: 2153-1293
www.scirp.org/journal/cs
E-mail: cs@scirp.org

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"Fault Diagnostics on Steam Boilers and Forecasting System Based on Hybrid Fuzzy Clustering and Artificial Neural Networks in Early Detection of Chamber Slagging/Fouling"
written by Mohan Sathya Priya, Radhakrishnan Kanthavel, Muthusamy Saravanan,
published by Circuits and Systems, Vol.7 No.12, 2016

has been cited by the following article(s):

[1]	Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis
	ChemBioEng Reviews, 2021

[2]	An ANN-based method for predicting Zhundong and other Chinese coal slagging potential
	2021

[3]	Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review
	Process Safety and Environmental Protection, 2019

[4]	Coal-fired boiler fault prediction using artificial neural networks
	International Journal of Electrical and Computer Engineering, 2018

[5]	An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips
	2018

[6]	Coal-Fired Boiler Fault Prediction using Artificial Neural Networks.
	2018

[1]	Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis ChemBioEng Reviews, 2021 DOI:10.1002/cben.202100008

[2]	An ANN-based method for predicting Zhundong and other Chinese coal slagging potential Fuel, 2021 DOI:10.1016/j.fuel.2021.120271

[3]	An ANN-based method for predicting Zhundong and other Chinese coal slagging potential Fuel, 2021 DOI:10.1016/j.fuel.2021.120271

[4]	An ANN-based method for predicting Zhundong and other Chinese coal slagging potential Fuel, 2021 DOI:10.1016/j.fuel.2021.120271

[5]	Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review Process Safety and Environmental Protection, 2019 DOI:10.1016/j.psep.2019.01.013

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